Index

A note on the digital index

A link in an index entry is displayed as the section title in which that entry appears. Because some sections have multiple index markers, it is not unusual for an entry to have several links to the same section. Clicking on any link will take you directly to the place in the text in which the marker appears.

A

absolute value, Matrix and Image Operators, Matrix and Image Operators, cvAbs, cvAbsDiff, and cvAbsDiffS, cvConvertScaleAbs

accumulation functions, Accumulating means, variances, and covariances, Accumulating means, variances, and covariances

accumulator plane, Hough Line Transform, Hough Line Transform

AdaBoost, Boosting, AdaBoost, Boosting Code, Using Random Trees, Supervised Learning and Boosting Theory, Boosting in the Haar cascade

affine transforms, Stretch, Shrink, Warp, and Rotate, Stretch, Shrink, Warp, and Rotate, Dense affine transformations, cvWarpAffine performance, Computing the affine map matrix, Computing the affine map matrix, Sparse affine transformations, CartToPolar and PolarToCart, Affine and Perspective Transformations

allocation of memory, Memory Storage, Common Routines in the ML Library

alpha blending, cvAdd, cvAddS, cvAddWeighted, and alpha blending, cvAnd and cvAndS, cvAdd, cvAddS, cvAddWeighted, and alpha blending, cvAdd, cvAddS, cvAddWeighted, and alpha blending, cvAnd and cvAndS

anchor points, Dilation and Erosion, Convolution, Convolution

aperture problem, How Lucas-Kanade works

arrays, Accessing Data in Your Matrix, The hard way, Arrays of Points, Arrays of Points, cvCalcCovarMatrix, cvDet, cvNorm, cvSetIdentity, cvSVBkSb, cvXor and cvXorS

accessing members of, Accessing Data in Your Matrix, The hard way

norm, cvNorm

of points, Arrays of Points, Arrays of Points

row/column index, cvSVBkSb

setting elements of, cvSetIdentity, cvXor and cvXorS

square, cvDet

artistic community, Specific Items

averaging background method, Averaging Background Method, Advanced Background Method, Averaging Background Method, Averaging Background Method, Averaging Background Method, Averaging Background Method, Accumulating means, variances, and covariances, Advanced Background Method, Advanced Background Method

B

back projection, Back Projection, Back Projection, Patch-based back projection, Patch-based back projection, Template Matching

back-propagation (MLP), Boosting Code, Multilayer Perceptron

background, Background Subtraction, Background Subtraction, Background Subtraction, Frame Differencing, Averaging Background Method, Averaging Background Method, Advanced Background Method, Learning the background

defined, Background Subtraction

learning, Averaging Background Method, Learning the background

statistical model, Averaging Background Method

subtraction (differencing), Background Subtraction, Background Subtraction, Frame Differencing, Advanced Background Method

background-foreground segmentation, OpenCV Structure and Content

barrel (fish-eye) effect, Lens Distortions

Bayer pattern, cvCvtColor

Bayes classifier, OpenCV ML Algorithms, Training, Naïve/Normal Bayes Classifier, Naïve/Normal Bayes Classifier, Naïve/Normal Bayes Code, Naïve/Normal Bayes Code

Bayesian network, Generative and Discriminative Models, Naïve/Normal Bayes Classifier, Naïve/Normal Bayes Classifier

Bhattacharyya matching, Bhattacharyya distance (method = CV_COMP_BHATTACHARYYA), Histogram Usage Examples

bias (underfitting), Diagnosing Machine Learning Problems, Diagnosing Machine Learning Problems, Diagnosing Machine Learning Problems, Diagnosing Machine Learning Problems, Diagnosing Machine Learning Problems

overview of, Diagnosing Machine Learning Problems, Diagnosing Machine Learning Problems, Diagnosing Machine Learning Problems, Diagnosing Machine Learning Problems, Diagnosing Machine Learning Problems

bilateral filter, Smoothing, Dilation and Erosion, Smoothing, Smoothing, Smoothing, Smoothing, Dilation and Erosion

bird's-eye view transform, Bird's-Eye View Transform Example, Bird's-Eye View Transform Example, Bird's-Eye View Transform Example, Bird's-Eye View Transform Example, Bird's-Eye View Transform Example, Bird's-Eye View Transform Example

bitwise AND operation, cvAnd and cvAndS

bitwise OR operation, cvOr and cvOrS

bitwise XOR operation, cvXor and cvXorS

Black Hat operation, More General Morphology, Top Hat and Black Hat, Top Hat and Black Hat, Flood Fill

block matching method, Block matching method, Stereo Correspondence, Stereo Correspondence

Boolean images, More General Morphology, Morphological gradient, Canny

Boolean mask, Image Pyramids

boosted rejection cascade, Face Detection or Haar Classifier

boosting classifiers, OpenCV ML Algorithms, Boosting, Boosting, Boosting, Boosting Code, Boosting Code, Boosting Code, Boosting Code, Using Random Trees, Boosting in the Haar cascade

bootstrapping, Cross-validation, bootstrapping, ROC curves, and confusion matrices

Borgefors (Gunilla) method, Distance Transform

Bouguet, Calibration

Bouguet algorithm, Calibrated stereo rectification: Bouguet's algorithm, Calibrated stereo rectification: Bouguet's algorithm, Stereo Calibration, Rectification, and Correspondence Code

boundaries, Convolution, Contour Finding, Contour Finding, Advanced Background Method

box, Advanced Background Method

convolution, Convolution

exterior, Contour Finding

interior, Contour Finding

Breiman binary decision trees, Binary Decision Trees

Breiman random forests theory, Random Trees

Breiman variable importance algorithm, Variable Importance, Decision Tree Results

Bresenham algorithm, Lines

brightness constancy, How Lucas-Kanade works, How Lucas-Kanade works, Horn-Schunck method

Brown method, Lens Distortions, What's under the hood?

buttons (simulating), No Buttons

C

calibration, OpenCV Structure and Content, Subpixel Corners, Camera Models and Calibration, Calibration, Putting Calibration All Together, Putting Calibration All Together, Putting Calibration All Together, Putting Calibration All Together

callback, Mouse Events, Mouse Events

cameras, Documentation Available in HTML, Getting Started, Second Program—AVI Video, Input from a Camera, Working with Video, Working with Video, Reading Video, ConvertImage, Smoothing, Histogram Equalization, Subpixel Corners, Camera Models and Calibration, Camera Model, Camera Model, Camera Model, Camera Model, Lens Distortions, Lens Distortions, Homography, Stereo Imaging, Structure from Motion

artifact reduction, Smoothing

CVCAM interface, Documentation Available in HTML

focal length, Camera Model, Camera Model

format reversed, ConvertImage

input from, Getting Started, Second Program—AVI Video, Input from a Camera, Working with Video, Working with Video

intrinsics matrix, Structure from Motion

manufacturing defects, Lens Distortions, Lens Distortions

path, Subpixel Corners

pinhole model, Camera Models and Calibration, Camera Model, Camera Model

projection matrix, Homography

properties, Reading Video

stereo imaging, Stereo Imaging

whiteouts, Histogram Equalization

camshift tracking, Mean-Shift and Camshift Tracking, Camshift

Canny, Canny

Canny edge detector, A Not-So-Simple Transformation, Canny, Canny, Canny, Hough Line Transform, Hough Line Transform, Hough Circle Transform, Hough Circle Transform, Histogram Equalization, Contour Finding

Cartesian to polar coordinates, CartToPolar and PolarToCart, LogPolar, CartToPolar and PolarToCart, LogPolar, LogPolar

center of projection, Camera Model, Affine and Perspective Transformations

chain code histogram (CCH), Pairwise Geometrical Histograms

channel, Arrays of Points

channel of interest (COI), IplImage Data Structure, More on ROI and widthStep

chessboards (calibration object), Rotation Matrix and Translation Vector, Chessboards, Subpixel corners, Subpixel corners, Drawing chessboard corners, How many chess corners for how many parameters?, Calibration function, Stereo Calibration, Stereo Correspondence

corners, Chessboards, Subpixel corners, Subpixel corners, Drawing chessboard corners, How many chess corners for how many parameters?, Calibration function

overview of, Rotation Matrix and Translation Vector, Stereo Calibration

stereo rectification, Stereo Correspondence

chi-square method, Chi-square (method = CV_COMP_CHISQR)

Chinese wiki site, Documentation via the Wiki

circle transform (Hough), Hough Circle Transform, Hough Circle Transform, Hough Circle Transform, Hough Circle Transform, Hough Circle Transform, Hough Circle Transform

circles, Circles and Ellipses, Enclosing circles and ellipses

circum-circle property, Delaunay Triangulation, Voronoi Tesselation

classification, Training and Test Set, Supervised and Unsupervised Data

classification and regression tree (CART) algorithms, Binary Decision Trees, Training the tree, Boosting

classifiers, OpenCV ML Algorithms, Naïve/Normal Bayes Classifier, Regression Impurity, Naïve/Normal Bayes Classifier, Naïve/Normal Bayes Classifier, Regression Impurity, Boosting, Boosting, AdaBoost, Boosting Code, Boosting Code, Boosting Code, Face Detection or Haar Classifier, Viola-Jones Classifier Theory, Face Detection or Haar Classifier, Viola-Jones Classifier Theory, Viola-Jones Classifier Theory, Viola-Jones Classifier Theory, Viola-Jones Classifier Theory

Bayes, Naïve/Normal Bayes Classifier, Regression Impurity, Naïve/Normal Bayes Classifier, Naïve/Normal Bayes Classifier, Regression Impurity

Haar, Face Detection or Haar Classifier, Viola-Jones Classifier Theory, Face Detection or Haar Classifier, Viola-Jones Classifier Theory, Viola-Jones Classifier Theory, Viola-Jones Classifier Theory

strong, Boosting

Viola-Jones, Viola-Jones Classifier Theory

weak, OpenCV ML Algorithms, Boosting, AdaBoost, Boosting Code, Boosting Code, Boosting Code

clone functions, CvMat Matrix Structure

clustering algorithms, Training and Test Set, Training and Test Set, Supervised and Unsupervised Data, K-Means

codebook method, Background Subtraction, Advanced Background Method, Advanced Background Method, Advanced Background Method, Structures, Learning the background, Learning the background, Learning the background, Learning with moving foreground objects, Background differencing: Finding foreground objects, A few more thoughts on codebook models

codecs, Writing to an AVI File, Working with Video, Reading Video, Writing Video

COI (channel of interest), IplImage Data Structure, More on ROI and widthStep

color conversions, cvAbs, cvAbsDiff, and cvAbsDiffS, cvCvtColor, cvCvtColor, cvCvtColor, Writing Video

color histograms, Histogram Usage Examples, Some More Complicated Stuff

color similarity, Mean-Shift Segmentation

color space, cvCvtColor, cvCvtColor, Advanced Background Method

compilers, Portability

compression codecs, Working with Video, Reading Video, Writing Video

Concurrent Versions System (CVS), Getting the Latest OpenCV via CVS

condensation algorithm, Estimators, Estimators, The Condensation Algorithm, The Condensation Algorithm, The Condensation Algorithm, Exercises

conditional random field (CRF), Specific Items

configuration and log files, Data Persistence

confusion matrices, Cross-validation, bootstrapping, ROC curves, and confusion matrices, Cross-validation, bootstrapping, ROC curves, and confusion matrices, Decision Tree Results, Boosting

connected components, Opening and closing, Image Pyramids, Connected Components for Foreground Cleanup, Connected Components for Foreground Cleanup, A quick test, A quick test, A quick test

closing and, Opening and closing

defined, Image Pyramids

foreground cleanup and, Connected Components for Foreground Cleanup, Connected Components for Foreground Cleanup, A quick test, A quick test, A quick test

constructor methods, OpenCV Primitive Data Types

containment, Contour Finding

contour, Canny, Contour Finding, Contour Finding, Contour Finding, Contour Finding, Drawing Contours, Another Contour Example, More to Do with Contours, Summary Characteristics, Summary Characteristics, Length, Bounding boxes, Enclosing circles and ellipses, Matching Contours, Contour Convexity and Convexity Defects, Moments, More About Moments, More About Moments, Matching with Hu Moments, Hierarchical Matching, Hierarchical Matching, Hierarchical Matching, Hierarchical Matching, Contour Convexity and Convexity Defects, Contour Convexity and Convexity Defects, Contour Convexity and Convexity Defects, Contour Convexity and Convexity Defects, Contour Convexity and Convexity Defects, Contour Convexity and Convexity Defects

area, Length

bounding, Bounding boxes, Enclosing circles and ellipses

Canny and, Canny

convexity, Contour Convexity and Convexity Defects, Contour Convexity and Convexity Defects, Contour Convexity and Convexity Defects, Contour Convexity and Convexity Defects, Contour Convexity and Convexity Defects

drawing, Drawing Contours, More to Do with Contours

finding, Contour Finding, Another Contour Example

length, Summary Characteristics

matching, Matching Contours, Contour Convexity and Convexity Defects, Matching with Hu Moments, Hierarchical Matching, Hierarchical Matching, Contour Convexity and Convexity Defects

moments, Summary Characteristics, Moments, More About Moments, More About Moments, Hierarchical Matching

tree, Contour Finding, Contour Finding, Contour Finding, Hierarchical Matching

control motion, Systems with dynamics

convex hull, Contour Convexity and Convexity Defects

convexity defects, Contour Convexity and Convexity Defects, Contour Convexity and Convexity Defects, Contour Convexity and Convexity Defects, Contour Convexity and Convexity Defects, Contour Convexity and Convexity Defects

convolution theorem, Convolution and DFT, Discrete Cosine Transform (DCT), Convolution and DFT, Convolution and DFT, Discrete Cosine Transform (DCT)

convolutions, Convolution, Convolution Boundaries

correlation methods, Correlation (method = CV_COMP_CORREL), Correlation (method = CV_COMP_CORREL), Correlation matching methods (method = CV_TM_CCORR), Correlation coefficient matching methods (method = CV_TM_CCOEFF), Normalized methods, Normalized methods, Exercises

correspondence, Triangulation, Rectification map, Stereo Calibration, Rectification, and Correspondence Code, Stereo Correspondence, Stereo Correspondence, Stereo Correspondence, Stereo Correspondence, Stereo Correspondence, Stereo Correspondence, Stereo Calibration, Rectification, and Correspondence Code, Depth Maps from 3D Reprojection, Stereo Calibration, Rectification, and Correspondence Code, Stereo Calibration, Rectification, and Correspondence Code, Stereo Calibration, Rectification, and Correspondence Code, Stereo Calibration, Rectification, and Correspondence Code, Stereo Calibration, Rectification, and Correspondence Code, Depth Maps from 3D Reprojection, Depth Maps from 3D Reprojection, Depth Maps from 3D Reprojection

calibration and, Stereo Calibration, Rectification, and Correspondence Code, Depth Maps from 3D Reprojection, Stereo Calibration, Rectification, and Correspondence Code, Stereo Calibration, Rectification, and Correspondence Code, Stereo Calibration, Rectification, and Correspondence Code, Stereo Calibration, Rectification, and Correspondence Code, Depth Maps from 3D Reprojection, Depth Maps from 3D Reprojection, Depth Maps from 3D Reprojection

defined, Triangulation

stereo, Rectification map, Stereo Calibration, Rectification, and Correspondence Code, Stereo Correspondence, Stereo Correspondence, Stereo Correspondence, Stereo Correspondence, Stereo Correspondence, Stereo Correspondence, Stereo Calibration, Rectification, and Correspondence Code

covariance matrix, cvCalcCovarMatrix

CRF (conditional random field), Specific Items

cross-validation, Cross-validation, bootstrapping, ROC curves, and confusion matrices

cumulative distribution function, Histogram Equalization, Histogram Equalization

CV, Documentation via the Wiki

CVaux, OpenCV Structure and Content

Cvcore, Exercises

CVS (Concurrent Versions System), Getting the Latest OpenCV via CVS, Getting the Latest OpenCV via CVS

CvXX OpenCV classes, Common Routines in the ML Library, Controlling Training Iterations, Common Routines in the ML Library, Controlling Training Iterations, Decision Tree Usage, Decision Tree Results, Boosting Code, Random Trees, Boosting Code, Boosting Code, Boosting Code, Boosting Code, Random Trees, K-Nearest Neighbors, Multilayer Perceptron, Support Vector Machine

CvANN_MLP, Multilayer Perceptron

CvBoost, Boosting Code, Random Trees, Boosting Code, Boosting Code, Boosting Code, Random Trees

CvDTree, Decision Tree Usage, Decision Tree Results, Boosting Code

CvKNearest, K-Nearest Neighbors

CvStatModel, Common Routines in the ML Library, Controlling Training Iterations, Common Routines in the ML Library, Controlling Training Iterations

CvSVM, Support Vector Machine

CvXX OpenCV data structures, A Simple Transformation, OpenCV Primitive Data Types, OpenCV Primitive Data Types, OpenCV Primitive Data Types, OpenCV Primitive Data Types, OpenCV Primitive Data Types, OpenCV Primitive Data Types, OpenCV Primitive Data Types, Matrix and Image Types, Matrix and Image Types, CvMat Matrix Structure, The easy way, The hard way, The hard way, The right way, Lines, Data Persistence, Data Persistence, Sliders, Trackbars, and Switches, Writing Video, Writing Video, Image Pyramids, Basic Histogram Data Structure, Creating a Sequence, Bounding boxes, Moments, Contour Convexity and Convexity Defects, OpenCV and the Kalman filter, Stereo Correspondence, Stereo Correspondence, Controlling Training Iterations

CvArr, Matrix and Image Types

CvBox2D, Bounding boxes

CvConnectedComponent, Image Pyramids

CvConvexityDefect, Contour Convexity and Convexity Defects

CvFileStorage, Data Persistence

CvHistogram, Basic Histogram Data Structure

CvKalman, OpenCV and the Kalman filter

CvMat, Matrix and Image Types, CvMat Matrix Structure, The easy way, The hard way, The hard way, The right way, Data Persistence

CvMoments, Moments

CvPointXXX, OpenCV Primitive Data Types

CvRect, OpenCV Primitive Data Types, OpenCV Primitive Data Types

CvScalar, OpenCV Primitive Data Types, OpenCV Primitive Data Types, Lines

CvSeq, Creating a Sequence

CvSize, A Simple Transformation, OpenCV Primitive Data Types, OpenCV Primitive Data Types

CvStereoBMState, Stereo Correspondence, Stereo Correspondence

CvTermCriteria, Controlling Training Iterations

CvTrackbarCallback, Sliders, Trackbars, and Switches

CvVideoWriter, Writing Video, Writing Video

cvXX OpenCV functions, First Program—Display a Picture, First Program—Display a Picture, First Program—Display a Picture, First Program—Display a Picture, First Program—Display a Picture, First Program—Display a Picture, Second Program—AVI Video, Second Program—AVI Video, Second Program—AVI Video, Moving Around, Moving Around, Moving Around, A Simple Transformation, A Not-So-Simple Transformation, Input from a Camera, Writing to an AVI File, Writing to an AVI File, Writing to an AVI File, OpenCV Primitive Data Types, OpenCV Primitive Data Types, OpenCV Primitive Data Types, CvMat Matrix Structure, CvMat Matrix Structure, CvMat Matrix Structure, CvMat Matrix Structure, CvMat Matrix Structure, CvMat Matrix Structure, The hard way, The hard way, The hard way, The hard way, The hard way, The hard way, More on ROI and widthStep, More on ROI and widthStep, More on ROI and widthStep, cvAbs, cvAbsDiff, and cvAbsDiffS, cvAdd, cvAddS, cvAddWeighted, and alpha blending, cvAnd and cvAndS, cvAvg, cvAvg, cvAvgSdv, cvCalcCovarMatrix, cvCmp and cvCmpS, cvConvertScale, cvConvertScale, cvConvertScaleAbs, cvCopy, cvCvtColor, cvDet, cvDiv, cvDotProduct, cvEigenVV, cvGEMM, cvGEMM, cvGetCol and cvGetCols, cvGetDiag, cvGetDims and cvGetDimSize, cvGetRow and cvGetRows, cvGetSize, cvGetSubRect, cvInRange and cvInRangeS, cvInvert, cvMahalanobis, cvMax and cvMaxS, cvMerge, cvMin and cvMinS, cvMinMaxLoc, cvMinMaxLoc, cvMul, cvMul, cvNot, cvNorm, cvNorm, cvNormalize, cvOr and cvOrS, cvReduce, cvReduce, cvSet and cvSetZero, cvSetIdentity, cvSolve, cvSplit, cvSub, cvSubS, and cvSubRS, cvSub, cvSubS, and cvSubRS, cvSum, cvSVD, cvSVBkSb, cvTranspose and cvT, cvXor and cvXorS, Lines, Lines, Circles and Ellipses, Polygons, Polygons, Fonts and Text, Fonts and Text, Fonts and Text, Data Persistence, Data Persistence, Data Persistence, Data Persistence, Data Persistence, Data Persistence, Data Persistence, Data Persistence, Data Persistence, Data Persistence, Data Persistence, Data Persistence, Data Persistence, Verifying Installation, Creating a Window, Creating a Window, Creating a Window, Loading an Image, Loading an Image, Displaying Images, Displaying Images, Displaying Images, Displaying Images, Displaying Images, WaitKey, Mouse Events, Working with Video, Reading Video, Reading Video, Reading Video, Reading Video, Reading Video, Writing Video, Writing Video, Writing Video, ConvertImage, Dilation and Erosion, Dilation and Erosion, Making Your Own Kernel, More General Morphology, Flood Fill, Resize, Resize, Image Pyramids, Image Pyramids, Image Pyramids, Threshold, Threshold, Threshold, Threshold, Threshold, Threshold, Adaptive Threshold, Convolution, Convolution Boundaries, Scharr Filter, Laplace, Canny, Canny, Hough Circle Transform, Hough Circle Transform, Remap, Remap, Dense affine transformations, cvWarpAffine performance, Computing the affine map matrix, Computing the affine map matrix, Sparse affine transformations, Dense perspective transform, Sparse perspective transformations, CartToPolar and PolarToCart, LogPolar, CartToPolar and PolarToCart, CartToPolar and PolarToCart, CartToPolar and PolarToCart, LogPolar, Discrete Fourier Transform (DFT), Discrete Fourier Transform (DFT), Spectrum Multiplication, Convolution and DFT, Discrete Cosine Transform (DCT), Integral Images, Distance Transform, Histogram Equalization, Basic Histogram Data Structure, Basic Histogram Data Structure, Basic Histogram Data Structure, Basic Histogram Data Structure, Basic Manipulations with Histograms, Basic Manipulations with Histograms, Basic Manipulations with Histograms, Basic Manipulations with Histograms, Basic Manipulations with Histograms, Basic Manipulations with Histograms, Basic Manipulations with Histograms, Comparing Two Histograms, Earth Mover's Distance, Earth Mover's Distance, Earth Mover's Distance, Earth Mover's Distance, Earth Mover's Distance, Back Projection, Patch-based back projection, Template Matching, Normalized methods, Contours, Memory Storage, Memory Storage, Creating a Sequence, Deleting a Sequence, Direct Access to Sequence Elements, Direct Access to Sequence Elements, Slices, Copying, and Moving Data, Slices, Copying, and Moving Data, Slices, Copying, and Moving Data, Slices, Copying, and Moving Data, Slices, Copying, and Moving Data, Slices, Copying, and Moving Data, Slices, Copying, and Moving Data, Slices, Copying, and Moving Data, Inserting and Removing Elements, Inserting and Removing Elements, Sequence Block Size, Sequence Readers and Sequence Writers, Sequence Readers and Sequence Writers, Sequence Readers and Sequence Writers, Sequence Readers and Sequence Writers, Sequence Readers and Sequence Writers, Sequence Readers and Sequence Writers, Sequence Readers and Sequence Writers, Sequence Readers and Sequence Writers, Sequences and Arrays, Sequences and Arrays, Contour Finding, Contour Finding, Contours Are Sequences, Contours Are Sequences, Contours Are Sequences, Contours Are Sequences, Contours Are Sequences, Drawing Contours, Polygon Approximations, Polygon Approximations, Polygon Approximations, Length, Length, Bounding boxes, Bounding boxes, Enclosing circles and ellipses, Enclosing circles and ellipses, Geometry, Geometry, Geometry, Moments, More About Moments, Matching with Hu Moments, Matching with Hu Moments, Hierarchical Matching, Contour Convexity and Convexity Defects, Contour Convexity and Convexity Defects, A Slice of Pixels, A Slice of Pixels, Frame Differencing, Frame Differencing, Averaging Background Method, Averaging Background Method, Averaging Background Method, Averaging Background Method, Averaging Background Method, Accumulating means, variances, and covariances, Accumulating means, variances, and covariances, Image Repair by Inpainting, Mean-Shift Segmentation, Mean-Shift Segmentation, Mean-Shift Segmentation, Mean-Shift Segmentation, Mean-Shift Segmentation, Mean-Shift Segmentation, Delaunay Triangulation, Voronoi Tesselation, Navigating Delaunay Subdivisions, Walking on edges, Method 1: Use an external point to locate an edge or vertex, Identifying the bounding triangle or edges on the convex hull and walking the hull, Usage Examples, Usage Examples, Corner Finding, Subpixel Corners, Subpixel Corners, How Lucas-Kanade works, How Lucas-Kanade works, Mean-Shift, Motion Templates, Motion Templates, Motion Templates, Motion Templates, Motion Templates, Motion Templates, Motion Templates, OpenCV and the Kalman filter, OpenCV and the Kalman filter, The Condensation Algorithm, Camera Models and Calibration, Basic Projective Geometry, Basic Projective Geometry, Chessboards, Subpixel corners, Drawing chessboard corners, Homography, Calibration function, Undistortion, Undistortion, Undistortion, Undistortion, Undistortion, Rodrigues Transform, Projections, Projections, Projections, Bird's-Eye View Transform Example, Bird's-Eye View Transform Example, Bird's-Eye View Transform Example, Bird's-Eye View Transform Example, Bird's-Eye View Transform Example, Bird's-Eye View Transform Example, Bird's-Eye View Transform Example, Bird's-Eye View Transform Example, POSIT: 3D Pose Estimation, POSIT: 3D Pose Estimation, How OpenCV handles all of this, How OpenCV handles all of this, Computing Epipolar Lines, Uncalibrated stereo rectification: Hartley's algorithm, Calibrated stereo rectification: Bouguet's algorithm, Rectification map, Rectification map, Stereo Correspondence, Stereo Correspondence, Stereo Calibration, Rectification, and Correspondence Code, Stereo Calibration, Rectification, and Correspondence Code, Depth Maps from 3D Reprojection, Structure from Motion, Structure from Motion, Structure from Motion, Fitting Lines in Two and Three Dimensions, Fitting Lines in Two and Three Dimensions, Fitting Lines in Two and Three Dimensions, Mahalanobis Distance, Mahalanobis Distance, K-Means, K-Means Code, Training the tree, Training the tree, Face Detection or Haar Classifier, Code for Detecting Faces, Code for Detecting Faces, Code for Detecting Faces, Code for Detecting Faces, Code for Detecting Faces

cv2DRotationMatrix(), Computing the affine map matrix, Bird's-Eye View Transform Example

cvAbs(), cvAbs, cvAbsDiff, and cvAbsDiffS

cvAcc(), Threshold, Averaging Background Method

cvAdaptiveThreshold(), Adaptive Threshold, Contour Finding

cvAdd(), cvAdd, cvAddS, cvAddWeighted, and alpha blending

cvADD(), Threshold

cvAddWeighted(), Threshold

cvAnd(), cvAnd and cvAndS

cvApproxChains(), Contours Are Sequences

cvApproxPoly(), Polygon Approximations, Polygon Approximations

cvArcLength(), Length

cvAvg(), cvAvg

cvBoundingRect(), Bounding boxes

cvCalcBackProject(), Back Projection

cvCalcCovarMatrix(), cvCalcCovarMatrix, Mahalanobis Distance

cvCalcEMD2(), Earth Mover's Distance

cvCalcGlobalOrientation(), Motion Templates

cvCalcHist(), Basic Manipulations with Histograms

cvCalcMotionGradient(), Motion Templates

cvCalcOpticalFlowLK(), How Lucas-Kanade works

cvCalcOpticalFlowPyrLK(), How Lucas-Kanade works, Structure from Motion

cvCalcSubdivVoronoi2D(), Navigating Delaunay Subdivisions

cvCalibrateCamera2(), Camera Models and Calibration, Projections

cvCanny(), Canny

cvCaptureFromCamera(), Second Program—AVI Video

cvCartToPolar(), CartToPolar and PolarToCart, LogPolar, LogPolar

cvCircle(), Circles and Ellipses

cvClearSeq(), Deleting a Sequence

cvCloneImage(), Basic Manipulations with Histograms

cvCloneMat(), CvMat Matrix Structure

cvCloneSeq(), Slices, Copying, and Moving Data

cvCmp(), cvCmp and cvCmpS

cvCompareHist(), Comparing Two Histograms

cvComputeCorrespondEpilines(), Computing Epipolar Lines

cvConDensInitSampleSet(), The Condensation Algorithm

cvContourMoments(), Moments

cvContourPerimeter(), Length

cvConvert(), cvConvertScale

cvConvertImage(), ConvertImage

cvConvertPointsHomogenious(), Basic Projective Geometry

cvConvertScale(), cvConvertScale, Averaging Background Method

cvConvertScaleAbs(), cvConvertScaleAbs

cvConvexHull2(), Contour Convexity and Convexity Defects

cvConvexityDefects(), Contour Convexity and Convexity Defects

cvCopy(), cvCopy

cvCopyHist(), Basic Manipulations with Histograms

cvCopyMakeBorder(), Convolution Boundaries

cvCreateBMState(), Stereo Calibration, Rectification, and Correspondence Code

cvCreateCameraCapture(), Input from a Camera

cvCreateData(), CvMat Matrix Structure

cvCreateFileCapture(), Second Program—AVI Video, Working with Video

cvCreateImage(), A Simple Transformation, Fonts and Text

cvCreateMat(), CvMat Matrix Structure

cvCreateMatHeader(), CvMat Matrix Structure

cvCreatePOSITObject(), POSIT: 3D Pose Estimation

cvCreateSeq(), Creating a Sequence

cvCreateStereoBMState(), Stereo Correspondence

cvCreateStructuringElementEx(), Making Your Own Kernel

cvCreateTrackbar(), Moving Around

cvCreateVideoWriter(), Writing to an AVI File, Writing Video

cvCvtColor(), cvCvtColor, Code for Detecting Faces

cvCvtScale(), Averaging Background Method

cvCvtSeqToArray(), Sequences and Arrays

cvDCT(), Discrete Cosine Transform (DCT)

cvDestroyAllWindows(), Displaying Images

cvDestroyWindow(), First Program—Display a Picture, Creating a Window

cvDet(), cvDet

cvDFT(), CartToPolar and PolarToCart, Convolution and DFT

cvDilate(), Dilation and Erosion

cvDistTransform(), Distance Transform

cvDiv(), cvDiv

cvDotProduct(), cvDotProduct

cvDrawChessboardCorners(), Drawing chessboard corners

cvDrawContours(), Drawing Contours

cvDTreeParams(), Training the tree

cvEigenVV(), cvEigenVV

cvEndFindContour(), Contours Are Sequences

cvEndWriteSeq(), Sequence Readers and Sequence Writers

cvEndWriteStruct(), Data Persistence

cvEqualizeHist(), Histogram Equalization, Code for Detecting Faces

cvErode(), Dilation and Erosion, Frame Differencing

cvFillPoly(), Polygons

cvFilter2D(), Convolution

cvFindChessboardCorners(), Chessboards

cvFindContours(), Canny, Contours

cvFindCornerSubPix(), Subpixel Corners, Subpixel corners

cvFindDominantPoints(), Polygon Approximations

cvFindExtrinsicCameraParameters2(), Rodrigues Transform

cvFindFundamentalMat(), How OpenCV handles all of this, How OpenCV handles all of this

cvFindHomography(), Homography

cvFindNearestPoint2D(), Usage Examples

cvFindNextContour(), Contours Are Sequences

cvFindStereoCorrespondenceBM(), Stereo Correspondence, Structure from Motion

cvFitEllipse2(), Enclosing circles and ellipses

cvFitLine(), Fitting Lines in Two and Three Dimensions, Fitting Lines in Two and Three Dimensions, Fitting Lines in Two and Three Dimensions

cvFloodFill(), Flood Fill

cvFlushSeqWriter(), Sequence Readers and Sequence Writers

cvGEMM(), cvGEMM

cvGet*D() family, The hard way, The hard way

cvGetAffineTransform(), Computing the affine map matrix, Bird's-Eye View Transform Example

cvGetCaptureProperty(), Moving Around, Reading Video

cvGetCol(), cvGetCol and cvGetCols

cvGetDiag(), cvGetDiag

cvGetDims(), cvGetDims and cvGetDimSize

cvGetFileNodeByName(), Data Persistence

cvGetMinMaxHistValue(), Basic Manipulations with Histograms

cvGetModuleInfo(), Verifying Installation

cvGetPerspectiveTransform(), Bird's-Eye View Transform Example

cvGetQuadrangleSubPix(), cvWarpAffine performance, Bird's-Eye View Transform Example

cvGetRow(), cvGetRow and cvGetRows

cvGetSeqElem(), Image Pyramids, Direct Access to Sequence Elements

cvGetSeqReaderPos(), Sequence Readers and Sequence Writers

cvGetSize(), cvGetSize

cvGetSubRect(), cvGetSubRect

cvGoodFeaturesToTrack(), Corner Finding

cvGrabFrame(), Reading Video

cvHaarDetectObjects(), Face Detection or Haar Classifier

cvHistogram, Basic Manipulations with Histograms

cvHoughCircles(), Hough Circle Transform

cvHoughLines2(), Hough Circle Transform

cvInitFont( ), Fonts and Text

cvInitLineIterator(), A Slice of Pixels

cvInitMatHeader(), CvMat Matrix Structure

cvInitUndistortMap(), Undistortion

cvInitUndistortRectifyMap(), Rectification map

cvInpaint(), Image Repair by Inpainting

cvInRange(), cvInRange and cvInRangeS, Averaging Background Method

cvIntegral(), Integral Images

cvInvert(), cvInvert, Mahalanobis Distance

cvKalmanCorrect(), OpenCV and the Kalman filter

cvKalmanPredict(), OpenCV and the Kalman filter

cvKMeans2(), K-Means Code

cvLaplace(), Laplace

cvLine(), Lines

cvLoad(), Data Persistence, Code for Detecting Faces

cvLoadImage(), First Program—Display a Picture, Loading an Image

cvLogPolar(), CartToPolar and PolarToCart

cvMahalanobis(), cvMahalanobis

cvMakeHistHeaderForArray(), Basic Histogram Data Structure

cvMakeSeqHeaderForArray(), Sequences and Arrays

cvMat(), CvMat Matrix Structure

cvMatchShapes(), Matching with Hu Moments, Matching with Hu Moments

cvMatchTemplate(), Template Matching

cvMatMul(), cvGEMM

cvMax(), cvMax and cvMaxS

cvMaxRect(), Geometry

cvMean(), cvAvg

cvMeanShift(), Mean-Shift Segmentation, Mean-Shift, K-Means

cvMean_StdDev(), cvAvgSdv

cvMemStorageAlloc(), Memory Storage

cvMerge(), cvMerge, Discrete Fourier Transform (DFT)

cvmGet(), The hard way

cvMin(), cvMin and cvMinS

cvMinAreaRect2(), Bounding boxes

cvMinEnclosingCircle(), Enclosing circles and ellipses

cvMinMaxLoc(), cvMinMaxLoc, Patch-based back projection

cvMoments(), More About Moments

cvMorphologyEx(), More General Morphology

cvMoveWindow(), Displaying Images

cvmSet(), The hard way, Earth Mover's Distance

cvMul(), cvMul

cvMulSpectrums(), Spectrum Multiplication

cvMultiplyAcc(), Accumulating means, variances, and covariances

cvNamedWindow(), First Program—Display a Picture, Creating a Window

cvNorm(), cvNorm, cvNorm

cvNormalize(), cvNormalize, Normalized methods

cvNot(), cvNot

cvOpenFileStorage(), Data Persistence, Training the tree

cvOr(), cvOr and cvOrS, Frame Differencing

cvPerspectiveTransform(), Sparse perspective transformations, Bird's-Eye View Transform Example

cvPointPolygonTest(), Geometry

cvPointSeqFromMat(), Geometry

cvPolarToCart(), CartToPolar and PolarToCart

cvPolyLine(), Polygons

cvProjectPoints2(), Projections

cvPtr*D() family, The hard way, The hard way

cvPutText(), Fonts and Text

cvPyrDown(), A Not-So-Simple Transformation, Mean-Shift Segmentation

cvPyrMeanShiftFiltering(), Mean-Shift Segmentation, Delaunay Triangulation, Voronoi Tesselation

cvPyrSegmentation(), Image Pyramids, Image Pyramids, Mean-Shift Segmentation

cvPyrUp(), Mean-Shift Segmentation

cvQueryFrame(), Second Program—AVI Video, Reading Video

cvRead(), Data Persistence

cvReadByName(), Data Persistence

cvReadInt(), Data Persistence

cvReadIntByName(), Data Persistence

cvRealScalar(), OpenCV Primitive Data Types

cvRect(), More on ROI and widthStep

cvRectangle(), Lines, Code for Detecting Faces

cvReduce(), cvReduce, cvReduce

cvReleaseCapture(), Reading Video

cvReleaseFileStorage(), Data Persistence

cvReleaseHist(), Basic Histogram Data Structure

cvReleaseImage(), First Program—Display a Picture

cvReleasePOSITObject(), POSIT: 3D Pose Estimation

cvReleaseVideoWriter(), Writing to an AVI File, Writing Video

cvRemap(), Remap, Undistortion, Rectification map

cvReprojectImageTo3D(), Depth Maps from 3D Reprojection, Structure from Motion

cvResetImageROI(), More on ROI and widthStep

cvReshape(), Basic Projective Geometry

cvResize(), Resize, Resize, Code for Detecting Faces

cvResizeWindow(), Creating a Window

cvRestoreMemStoragePos(), Memory Storage

cvRetrieveFrame(), Reading Video

cvRodrigues2(), Calibration function

cvRunningAverage(), Accumulating means, variances, and covariances

cvSampleLine(), A Slice of Pixels

cvSave(), Data Persistence

cvSaveImage(), Loading an Image

cvScalar(), OpenCV Primitive Data Types, Earth Mover's Distance

cvScalarAll(), OpenCV Primitive Data Types

cvScale(), cvMul

cvSegmentMotion(), Motion Templates

cvSeqElemIdx(), Direct Access to Sequence Elements

cvSeqInsert(), Inserting and Removing Elements

cvSeqInsertSlice(), Slices, Copying, and Moving Data

cvSeqInvert(), Slices, Copying, and Moving Data

cvSeqPartition(), Slices, Copying, and Moving Data

cvSeqPush(), Sequence Readers and Sequence Writers

cvSeqRemove(), Inserting and Removing Elements

cvSeqRemoveSlice(), Slices, Copying, and Moving Data

cvSeqSearch(), Slices, Copying, and Moving Data

cvSeqSlice(), Slices, Copying, and Moving Data

cvSeqSort(), Slices, Copying, and Moving Data

cvSet(), cvSet and cvSetZero

cvSet2D(), Earth Mover's Distance

cvSetCaptureProperty(), Moving Around

cvSetCOI(), cvMinMaxLoc

cvSetHistBinRanges(), Basic Histogram Data Structure

cvSetHistRanges(), Basic Histogram Data Structure

cvSetIdentity(), cvSetIdentity

cvSetImageROI(), More on ROI and widthStep

cvSetMouseCallback(), Mouse Events

cvSetReal2D(), Earth Mover's Distance

cvSetSeqBlockSize(), Sequence Block Size

cvSetSeqReaderPos(), Sequence Readers and Sequence Writers

cvShowImage(), First Program—Display a Picture, Displaying Images, Displaying Images

cvSobel(), Scharr Filter

cvSolve(), cvSolve

cvSplit(), cvSplit, Basic Manipulations with Histograms, Averaging Background Method

cvStartAppendToSeq(), Sequence Readers and Sequence Writers

cvStartFindContours(), Contours Are Sequences

cvStartReadSeq(), Sequence Readers and Sequence Writers

cvStartWindowThread(), Displaying Images

cvStartWriteSeq(), Sequence Readers and Sequence Writers

cvStartWriteStruct(), Data Persistence

cvStereoCalibrate(), Projections

cvStereoRectify(), Undistortion, Calibrated stereo rectification: Bouguet's algorithm

cvStereoRectifyUncalibrated(), Uncalibrated stereo rectification: Hartley's algorithm

cvSub(), cvSub, cvSubS, and cvSubRS

cvSubdiv2DGetEdge(), Walking on edges

cvSubdiv2DLocate(), Method 1: Use an external point to locate an edge or vertex, Usage Examples

cvSubdiv2DNextEdge(), Identifying the bounding triangle or edges on the convex hull and walking the hull

cvSubS(), cvSub, cvSubS, and cvSubRS

cvSubstituteContour(), Contours Are Sequences

cvSum(), cvSum

cvSVBkSb(), cvSVBkSb

cvSVD(), cvSVD

cvTermCriteria(), Hierarchical Matching, Mean-Shift Segmentation, Subpixel Corners

cvThreshold(), Threshold, Threshold, Threshold, Basic Manipulations with Histograms, Contour Finding

cvTransform(), Sparse affine transformations, Bird's-Eye View Transform Example

cvTranspose(), cvTranspose and cvT

cvUndistort2(), Undistortion

cvUndistortPoints(), Undistortion, Stereo Calibration, Rectification, and Correspondence Code

cvUpdateMotionHistory(), Motion Templates, Motion Templates, Motion Templates, Motion Templates

cvWaitKey(), First Program—Display a Picture, WaitKey

cvWarpAffine(), Remap, Dense affine transformations, Bird's-Eye View Transform Example

cvWarpPerspective(), Dense perspective transform, Bird's-Eye View Transform Example

cvWrite(), Data Persistence

cvWriteFrame(), Writing to an AVI File, Writing Video

cvWriteInt(), Data Persistence

cvXor(), cvXor and cvXorS

cvZero(), Discrete Fourier Transform (DFT)

CxCore, Documentation Available in HTML, OpenCV Structure and Content, Data Persistence, Data Persistence

D

DAISY (dense rapidly computed Gaussian scale variant gradients), Specific Items

DARPA Grand Challenge race, Who Uses OpenCV?, Exercises

data persistence, Data Persistence, Integrated Performance Primitives, Data Persistence, Data Persistence, Data Persistence, Integrated Performance Primitives, Integrated Performance Primitives

data structures, A Simple Transformation, OpenCV Primitive Data Types, OpenCV Primitive Data Types, Matrix and Image Types, Arrays of Points, CvMat Matrix Structure, CvMat Matrix Structure, CvMat Matrix Structure, The easy way, The hard way, The hard way, The right way, Arrays of Points, IplImage Data Structure, Accessing Image Data, IplImage Data Structure, IplImage Data Structure, Accessing Image Data, Data Persistence

handling of, A Simple Transformation

image, OpenCV Primitive Data Types, IplImage Data Structure, Accessing Image Data, IplImage Data Structure, IplImage Data Structure, Accessing Image Data

matrix, Matrix and Image Types, Arrays of Points, CvMat Matrix Structure, CvMat Matrix Structure, CvMat Matrix Structure, The easy way, The hard way, The hard way, The right way, Arrays of Points

primitive, OpenCV Primitive Data Types

serializing, Data Persistence

DCT (discrete cosine transform), Discrete Cosine Transform (DCT)

de-allocation of memory, Memory Storage

debug builds, Windows, Getting Started

debugging, A Slice of Pixels, Drawing chessboard corners

decision stumps, AdaBoost, Boosting in the Haar cascade, Viola-Jones Classifier Theory, Learning New Objects

decision trees, OpenCV ML Algorithms, Using Machine Learning in Vision, Binary Decision Trees, Boosting, Misclassification impurity, Training the tree, Decision Tree Usage, Training the tree, Training the tree, Training the tree, Training the tree, Training the tree, Training the tree, Decision Tree Results, Boosting, Boosting, Boosting, Random Trees, Random Trees, Random Tree Code, Random Tree Code, Random Tree Code, Using Random Trees

advanced analysis, Training the tree

binary, Binary Decision Trees, Boosting, Boosting

creating and training, Misclassification impurity, Training the tree, Decision Tree Usage, Training the tree, Training the tree, Training the tree, Training the tree

predicting, Training the tree

pruning, Decision Tree Results, Boosting, Boosting

random, OpenCV ML Algorithms, Using Machine Learning in Vision, Random Trees, Random Trees, Random Tree Code, Random Tree Code, Random Tree Code, Using Random Trees

deep copy, Slices, Copying, and Moving Data

degenerate configurations, Averaging Background Method, How OpenCV handles all of this

Delaunay triangulation, OpenCV Structure and Content, Delaunay Triangulation, Voronoi Tesselation, Delaunay Triangulation, Voronoi Tesselation, Creating a Delaunay or Voronoi Subdivision, Creating a Delaunay or Voronoi Subdivision, Identifying the bounding triangle or edges on the convex hull and walking the hull, Usage Examples, Usage Examples

dense rapidly computed Gaussian scale variant gradients (DAISY), Specific Items

depth maps, Stereo Imaging, Depth Maps from 3D Reprojection, Depth Maps from 3D Reprojection

deque, Sequences

detect_and_draw() code, Code for Detecting Faces

dilation, Image Morphology, Morphological gradient, Dilation and Erosion, Dilation and Erosion, Dilation and Erosion, Opening and closing, Morphological gradient

directories, Getting Started

discrete cosine transform (DCT), Discrete Cosine Transform (DCT)

discriminative models, Generative and Discriminative Models, Naïve/Normal Bayes Classifier

disparity effects, Projection and 3D Vision

disparity maps, Stereo Imaging

distance transforms, Distance Transform, Histogram Equalization

distortion, Basic Projective Geometry, Lens Distortions, Calibration function

coefficients, Calibration function

lens, Basic Projective Geometry, Lens Distortions

documentation, More OpenCV Documentation, Documentation via the Wiki, Common Routines in the ML Library, Specific Items

dominant point, Polygon Approximations

Douglas-Peucker approximation, Polygon Approximations, Polygon Approximations, Connected Components for Foreground Cleanup

download and installation, Downloading and Installing OpenCV, Linux, Linux, Getting the Latest OpenCV via CVS

dynamical motion, Systems with dynamics

E

earth mover's distance (EMD), Bhattacharyya distance (method = CV_COMP_BHATTACHARYYA), Earth Mover's Distance

edges, What Is Computer Vision?, Canny, Hough Line Transform, Creating a Delaunay or Voronoi Subdivision, Usage Examples, Creating a Delaunay or Voronoi Subdivision, Usage Examples, Navigating Delaunay Subdivisions, Walking on edges, Walking on edges, Walking on edges, Method 1: Use an external point to locate an edge or vertex, Method 2: Step through a sequence of points or edges, Identifying the bounding triangle or edges on the convex hull and walking the hull, Usage Examples, Usage Examples, Usage Examples, Usage Examples

Delaunay, Creating a Delaunay or Voronoi Subdivision, Usage Examples, Navigating Delaunay Subdivisions, Walking on edges, Walking on edges, Method 2: Step through a sequence of points or edges, Identifying the bounding triangle or edges on the convex hull and walking the hull, Usage Examples, Usage Examples

detection, What Is Computer Vision?, Canny, Hough Line Transform

Voronoi, Creating a Delaunay or Voronoi Subdivision, Usage Examples, Walking on edges, Method 1: Use an external point to locate an edge or vertex, Usage Examples, Usage Examples

edible mushrooms example, Cross-validation, bootstrapping, ROC curves, and confusion matrices, Decision Tree Usage, Training the tree, Training the tree, Training the tree, Decision Tree Results, Decision Tree Results, Boosting, Boosting, Boosting Code, Using Random Trees

Eigen objects, OpenCV Structure and Content

ellipses, Circles and Ellipses, Enclosing circles and ellipses

EM (expectation maximization), Generative and Discriminative Models, OpenCV ML Algorithms, K-Means, Learning New Objects

EMD (earth mover's distance), Bhattacharyya distance (method = CV_COMP_BHATTACHARYYA), Earth Mover's Distance, Earth Mover's Distance, Earth Mover's Distance

entropy impurity, Misclassification impurity

epipolar geometry, Epipolar Geometry, Epipolar Geometry, The Essential and Fundamental Matrices

epipolar lines, Computing Epipolar Lines, Computing Epipolar Lines

erosion, Image Morphology, Morphological gradient, Dilation and Erosion, Dilation and Erosion, Dilation and Erosion, Opening and closing, Opening and closing, Morphological gradient

Eruhimov, The Origin of OpenCV

essential matrices, The Essential and Fundamental Matrices, The Essential and Fundamental Matrices, Essential matrix math, Stereo Calibration, Rectification, and Correspondence Code, Structure from Motion

Euclidean distance, Earth Mover's Distance, OpenCV ML Algorithms

expectation maximization (EM), OpenCV ML Algorithms, OpenCV ML Algorithms, K-Means, Expectation Maximization

F

face recognition, cvCalcCovarMatrix, Template Matching, Creating a Delaunay or Voronoi Subdivision, OpenCV ML Algorithms, Face Detection or Haar Classifier, Face Detection or Haar Classifier, Supervised Learning and Boosting Theory, Works well on …

Delaunay points, Creating a Delaunay or Voronoi Subdivision

detector classifier, OpenCV ML Algorithms, Face Detection or Haar Classifier, Works well on …

eigenfaces, cvCalcCovarMatrix

Haar classifier, Face Detection or Haar Classifier, Supervised Learning and Boosting Theory

template matching, Template Matching

face recognition tasks, OpenCV Structure and Content, Training and Test Set, Diagnosing Machine Learning Problems, Diagnosing Machine Learning Problems, Naïve/Normal Bayes Classifier, Works well on …, Works well on …, Code for Detecting Faces, Learning New Objects, Learning New Objects, Learning New Objects, Learning New Objects

age, Training and Test Set, Diagnosing Machine Learning Problems

eyes, Works well on …, Learning New Objects, Learning New Objects

features, Naïve/Normal Bayes Classifier

mouth, OpenCV Structure and Content, Diagnosing Machine Learning Problems, Works well on …, Learning New Objects

samples, Learning New Objects

sizes, Code for Detecting Faces

fast PCA, cvCalcCovarMatrix, cvCalcCovarMatrix

file, Getting Started, First Program—Display a Picture, Second Program—AVI Video, Second Program—AVI Video, Second Program—AVI Video, Moving Around, Writing to an AVI File, Writing to an AVI File, OpenCV Primitive Data Types, CvMat Matrix Structure, Data Persistence, Loading an Image, Reading Video, Reading Video, Reading Video, Reading Video, Writing Video

configuration (logging), Data Persistence

disk, Writing Video

header, Getting Started, OpenCV Primitive Data Types

information about file, Second Program—AVI Video

moving within, Moving Around

playing video, Second Program—AVI Video, Writing to an AVI File, Reading Video

properties, Reading Video

querying, CvMat Matrix Structure

reading images from, First Program—Display a Picture, Second Program—AVI Video, Writing to an AVI File, Reading Video, Reading Video

signature, Loading an Image

Filip, Directions

filter pipeline, A Not-So-Simple Transformation

fish-eye (barrel) effect, Lens Distortions

fish-eye camera lenses, Stereo Calibration

flood fill, Flood Fill, Flood Fill, Flood Fill, Flood Fill, Flood Fill, Flood Fill, Flood Fill, Flood Fill

fonts, Fonts and Text, Data Persistence, Fonts and Text, Data Persistence

foreground, Background Subtraction, Averaging Background Method, Background differencing: Finding foreground objects

finding objects, Background differencing: Finding foreground objects

overview of, Background Subtraction

segmentation into, Averaging Background Method

foreground versus background, Scene Modeling

forward projection, Remap

forward transform, Discrete Fourier Transform (DFT)

FOURCC (four-character code), Writing to an AVI File, Reading Video

Fourier, Discrete Fourier Transform (DFT)

Fourier transforms, Overview, Discrete Fourier Transform (DFT), Convolution and DFT, Convolution and DFT, Convolution and DFT

frame differencing, Frame Differencing, A quick test, A quick test, A quick test

Freeman chains, Contours Are Sequences, Pairwise Geometrical Histograms

Freund, Boosting

frontal parallel configuration, Triangulation, Triangulation, Triangulation, Stereo Correspondence, Depth Maps from 3D Reprojection

fundamental matrix, Projection and 3D Vision, The Essential and Fundamental Matrices, Fundamental matrix math, Fundamental matrix math, How OpenCV handles all of this, How OpenCV handles all of this, Structure from Motion

G

Galton, Normalized methods

Gauss, Discrete Fourier Transform (DFT)

Gaussian elimination, cvDet, cvInvert, cvInvert

Gaussian filter, Smoothing, Smoothing, Smoothing, Smoothing, Smoothing, Smoothing, Smoothing

Gaussian smooth, A Simple Transformation, A Not-So-Simple Transformation

GEMM (generalized matrix multiplication), cvAbs, cvAbsDiff, and cvAbsDiffS, cvGEMM, cvMul

generalized matrix multiplication (GEMM), Matrix and Image Operators, cvGEMM

generative algorithms, Generative and Discriminative Models, Naïve/Normal Bayes Classifier

Geometric Blur, Directions, Specific Items

geometric manipulations, Stretch, Shrink, Warp, and Rotate, Sparse perspective transformations, Sparse perspective transformations

geometrical checking, Geometry

gesture recognition, OpenCV Structure and Content, Histograms and Matching, Histograms and Matching, Motion Templates

Gini index (impurity), Classification Impurity

GLOH (gradient location and orientation histogram), Specific Items

Google, Who Uses OpenCV?, Directions

gradient location and orientation histogram (GLOH), Specific Items

gradients, More General Morphology, Morphological gradient, Morphological gradient, Flood Fill, Hough Circle Transform

Hough, Hough Circle Transform

morphological, More General Morphology, Morphological gradient, Morphological gradient, Flood Fill

grayscale, Writing to an AVI File, cvCvtColor, cvCvtColor, cvCvtColor, Loading an Image, ConvertImage

grayscale morphology, Top Hat and Black Hat

H

Haar classifier, Integral Images, OpenCV ML Algorithms, Common Routines in the ML Library, Face Detection or Haar Classifier, Face Detection or Haar Classifier, Boosting in the Haar cascade, Viola-Jones Classifier Theory, Works well on …

haartraining, Documentation Available in HTML, Learning New Objects, Learning New Objects, Learning New Objects

Harris corners, Corner Finding, Corner Finding, Corner Finding, Subpixel Corners, How Lucas-Kanade works, Subpixel corners, Specific Items

Hartley's algorithm, Stereo Rectification, Uncalibrated stereo rectification: Hartley's algorithm, Uncalibrated stereo rectification: Hartley's algorithm, Stereo Correspondence

Hessian image, Corner Finding

HighGUI, Documentation Available in HTML, OpenCV Structure and Content, Getting Started, First Program—Display a Picture, Second Program—AVI Video, Moving Around, A Portable Graphics Toolkit

hill climbing algorithm, Mean-Shift

histogram of oriented gradients (HoG), Directions, Specific Items

histograms, Scharr Filter, Histogram Equalization, Histogram Equalization, Histogram Equalization, Histograms and Matching, Histograms and Matching, Patch-based back projection, Histograms and Matching, Histograms and Matching, Histograms and Matching, Histograms and Matching, Basic Histogram Data Structure, Basic Histogram Data Structure, Basic Histogram Data Structure, Basic Histogram Data Structure, Accessing Histograms, Accessing Histograms, Accessing Histograms, Basic Manipulations with Histograms, Comparing Two Histograms, Comparing Two Histograms, Intersection (method = CV_COMP_INTERSECT), Intersection (method = CV_COMP_INTERSECT), Bhattacharyya distance (method = CV_COMP_BHATTACHARYYA), Bhattacharyya distance (method = CV_COMP_BHATTACHARYYA), Bhattacharyya distance (method = CV_COMP_BHATTACHARYYA), Bhattacharyya distance (method = CV_COMP_BHATTACHARYYA), Histogram Usage Examples, Histogram Usage Examples, Some More Complicated Stuff, Earth Mover's Distance, Earth Mover's Distance, Earth Mover's Distance, Patch-based back projection, Patch-based back projection

assembling, Scharr Filter

color, Histogram Usage Examples

comparing, Comparing Two Histograms, Bhattacharyya distance (method = CV_COMP_BHATTACHARYYA), Bhattacharyya distance (method = CV_COMP_BHATTACHARYYA)

converting to signatures, Earth Mover's Distance

data structure, Histograms and Matching, Basic Histogram Data Structure

defined, Histograms and Matching

dense, Accessing Histograms

equalization, Histogram Equalization, Histogram Equalization, Histogram Equalization

grid size problems, Histograms and Matching

intersection, Intersection (method = CV_COMP_INTERSECT)

matching methods, Bhattacharyya distance (method = CV_COMP_BHATTACHARYYA)

overview of, Histograms and Matching

homogeneous coordinates, Sparse perspective transformations, Basic Projective Geometry, Homography, Homography, Homography

homographies, Stretch, Shrink, Warp, and Rotate, Perspective Transform, Perspective Transform, Dense perspective transform, Sparse perspective transformations, Drawing chessboard corners, Camera Calibration, Homography, Homography, Camera Calibration, Projections

dense, Perspective Transform

flexibility of, Stretch, Shrink, Warp, and Rotate, Perspective Transform

map matrix, Dense perspective transform

overview of, Projections

planar, Drawing chessboard corners, Camera Calibration, Homography, Homography, Camera Calibration

sparse, Sparse perspective transformations

Horn-Schunk dense tracking method, The Basics of Tracking, Optical Flow

horopter, Stereo Correspondence, Stereo Correspondence, Stereo Correspondence, Stereo Correspondence

Hough transforms, Hough Transforms, Hough Circle Transform, Hough Line Transform, Hough Line Transform, Hough Line Transform, Hough Circle Transform, Hough Circle Transform, Hough Circle Transform

Hu moments, More About Moments, More About Moments, More About Moments, Matching with Hu Moments, Motion Templates

hue saturation histogram, Histogram Usage Examples, Histogram Usage Examples, Histogram Usage Examples, Histogram Usage Examples, Histogram Usage Examples

I

illuminated grid histogram, Histogram Usage Examples, Histogram Usage Examples, Histogram Usage Examples

image (projective) planes, Camera Model, Projections

Image Processing Library (IPL), IplImage Data Structure

image pyramids, A Not-So-Simple Transformation, Image Pyramids, Image Pyramids, Image Pyramids, Image Pyramids, Image Pyramids, Image Pyramids

images, First Program—Display a Picture, IplImage Data Structure, cvConvertScale, cvFlip, Loading an Image, Displaying Images, ConvertImage

data types, IplImage Data Structure, cvConvertScale

displaying, Displaying Images

formats, First Program—Display a Picture, cvFlip, ConvertImage

loading, Loading an Image

impurity metrics, Binary Decision Trees, Classification Impurity

inpainting, Image Repair by Inpainting

installation of OpenCV, Install, Windows, Linux, Getting Started, OpenCV Primitive Data Types, Verifying Installation

integral images, Integral Images, Integral Images, Integral Images, Distance Transform, Viola-Jones Classifier Theory

Integrated Performance Primitives (IPP), What Is OpenCV?, Speeding Up OpenCV with IPP, Windows, Linux, Integrated Performance Primitives

Intel Compiler, Learning New Objects

Intel Corporation, Past and Future

Intel Research, The Origin of OpenCV

Intel website for IPP, Windows

intensity bumps/holes, Image Morphology

intentional bias, Decision Tree Results, Decision Tree Results, Boosting

interpolation, Resize, Remap, Remap, LogPolar

intersection method, Intersection (method = CV_COMP_INTERSECT)

intrinsic parameters, Camera Models and Calibration

intrinsics matrix, Basic Projective Geometry

inverse transforms, Discrete Fourier Transform (DFT)

IPAN algorithm, Polygon Approximations, Polygon Approximations

IPL (Image Processing Library), IplImage Data Structure

IplImage data structure, The hard way, The hard way, IplImage Data Structure, IplImage Data Structure

element functions, The hard way, The hard way

overview of, IplImage Data Structure

variables, IplImage Data Structure

IPP (Integrated Performance Primitives), What Is OpenCV?, Speeding Up OpenCV with IPP, Windows, Linux, Integrated Performance Primitives, Discrete Fourier Transform (DFT)

J

Jacobi's method, cvEigenVV

Jaehne, Image Pyramids

Jones, Face Detection or Haar Classifier, Supervised Learning and Boosting Theory, Boosting in the Haar cascade, Viola-Jones Classifier Theory, Works well on …, Learning New Objects

K

K-means algorithm, OpenCV ML Algorithms, Common Routines in the ML Library, K-Means, Problems and Solutions, K-Means Code, K-Means Code

K-nearest neighbor (KNN), OpenCV ML Algorithms, Cross-validation, bootstrapping, ROC curves, and confusion matrices, K-Nearest Neighbors

Kalman filter, Estimators, A Brief Note on the Extended Kalman Filter, Estimators, The Kalman Filter, Some Kalman math, Some Kalman math, Some Kalman math, Some Kalman math, Systems with dynamics, Kalman equations, Kalman equations, Kalman equations, Kalman equations, Kalman equations, Kalman equations, Kalman equations, OpenCV and the Kalman filter, A Brief Note on the Extended Kalman Filter, OpenCV and the Kalman filter, OpenCV and the Kalman filter, Kalman filter example code, Kalman filter example code, Kalman filter example code, Kalman filter example code, Kalman filter example code, Kalman filter example code, Kalman filter example code, A Brief Note on the Extended Kalman Filter, A Brief Note on the Extended Kalman Filter, A Brief Note on the Extended Kalman Filter, A Brief Note on the Extended Kalman Filter, A Brief Note on the Extended Kalman Filter

blending factor (Kalman gain), Kalman equations

extended, A Brief Note on the Extended Kalman Filter

limitations of, A Brief Note on the Extended Kalman Filter

mathematics of, The Kalman Filter, Some Kalman math, Some Kalman math, Kalman equations, Kalman equations, Kalman equations, Kalman equations

OpenCV and, OpenCV and the Kalman filter, A Brief Note on the Extended Kalman Filter, Kalman filter example code, Kalman filter example code, Kalman filter example code, A Brief Note on the Extended Kalman Filter, A Brief Note on the Extended Kalman Filter

overview of, Estimators

kernel density estimation, Mean-Shift

kernels, Dilation and Erosion, Making Your Own Kernel, More General Morphology, Making Your Own Kernel, Making Your Own Kernel, More General Morphology, More General Morphology, Convolution, Convolution

convolution, Convolution

custom, Making Your Own Kernel, More General Morphology, Making Your Own Kernel, Making Your Own Kernel, More General Morphology

defined, Dilation and Erosion

shape values, More General Morphology

support of, Convolution

Kerns, Boosting

key-frame, Moving Around

Konolige, Stereo Correspondence

Kuriakin, The Origin of OpenCV

L

Lagrange multiplier, Horn-Schunck method

Laplacian operator, Laplace, Canny, Laplace, Canny

Laplacian pyramid, Image Pyramids, Image Pyramids

learning, What Is Machine Learning

Lee, The Origin of OpenCV

lens distortion model, Camera Models and Calibration, Lens Distortions, Lens Distortions, Calibration, Calibration, What's under the hood?, Triangulation

lenses, Camera Models and Calibration

Levenberg-Marquardt algorithm, Stereo Calibration

licensing terms, Who Uses OpenCV?

Lienhart, Face Detection or Haar Classifier

linear transformation, cvConvertScale

lines, Drawing Things, Circles and Ellipses, Hough Transforms

drawing, Drawing Things, Circles and Ellipses

finding, Hough Transforms

link strength, Mean-Shift Segmentation

Linux systems, What Is OpenCV?, Downloading and Installing OpenCV, Linux, Displaying Images, Directions, Specific Items

Lloyd algorithm, K-Means

LMedS algorithm, How OpenCV handles all of this

log-polar transforms, LogPolar, Discrete Fourier Transform (DFT), LogPolar, LogPolar, LogPolar, Discrete Fourier Transform (DFT)

Lowe, Specific Items

Lowe SIFT demo, Using Machine Learning in Vision

Lucas-Kanade (sparse) method, The Basics of Tracking, Corner Finding, Optical Flow, Lucas-Kanade Method, How Lucas-Kanade works, How Lucas-Kanade works, Dense Tracking Techniques, Dense Tracking Techniques, Horn-Schunck method

M

machine learning, What Is Machine Learning, Training and Test Set, Supervised and Unsupervised Data, OpenCV ML Algorithms, Using Machine Learning in Vision, Using Machine Learning in Vision, Using Machine Learning in Vision, Diagnosing Machine Learning Problems

Machine Learning Library (MLL), What Is OpenCV?, Documentation Available in HTML, OpenCV Structure and Content, Cross-validation, bootstrapping, ROC curves, and confusion matrices, Training, Training, Training, Controlling Training Iterations

MacOS systems, MacOS X, Creating a Window, Displaying Images

Mahalanobis distance, cvMahalanobis

Mahalonobis distance, Matrix and Image Operators, OpenCV ML Algorithms, OpenCV ML Algorithms, Diagnosing Machine Learning Problems, Cross-validation, bootstrapping, ROC curves, and confusion matrices, Mahalanobis Distance, Mahalanobis Distance, Mahalanobis Distance

malloc() function, Memory Storage

Manhattan distance, Earth Mover's Distance

Manta open source ray-tracing, Specific Items

Markov random fields (MRFs), Specific Items

masks, More on ROI and widthStep, More General Morphology, Flood Fill

matching methods, Basic Manipulations with Histograms, Histogram Usage Examples, Correlation (method = CV_COMP_CORREL), Bhattacharyya distance (method = CV_COMP_BHATTACHARYYA), Bhattacharyya distance (method = CV_COMP_BHATTACHARYYA), Histogram Usage Examples, Histogram Usage Examples, Histogram Usage Examples, Template Matching, Exercises, Correlation matching methods (method = CV_TM_CCORR), Correlation coefficient matching methods (method = CV_TM_CCOEFF), Normalized methods, Normalized methods, Exercises, Matching Contours, Contour Convexity and Convexity Defects, Moments, More About Moments, More About Moments, Matching with Hu Moments, Matching with Hu Moments, Matching with Hu Moments, Hierarchical Matching, Contour Convexity and Convexity Defects, Hierarchical Matching, Hierarchical Matching, Contour Convexity and Convexity Defects, Contour Convexity and Convexity Defects, Block matching method, Estimators, Stereo Correspondence, Stereo Correspondence

Bhattacharyya, Bhattacharyya distance (method = CV_COMP_BHATTACHARYYA)

block, Block matching method, Stereo Correspondence, Stereo Correspondence

contours, Matching Contours, Contour Convexity and Convexity Defects, Moments, More About Moments, Matching with Hu Moments, Matching with Hu Moments, Hierarchical Matching, Contour Convexity and Convexity Defects

hierarchical, Hierarchical Matching, Contour Convexity and Convexity Defects, Hierarchical Matching, Contour Convexity and Convexity Defects

histogram, Basic Manipulations with Histograms, Histogram Usage Examples, Correlation (method = CV_COMP_CORREL), Bhattacharyya distance (method = CV_COMP_BHATTACHARYYA), Histogram Usage Examples, Histogram Usage Examples, Histogram Usage Examples

Hu moments, More About Moments, Matching with Hu Moments, Estimators

template, Template Matching, Exercises, Correlation matching methods (method = CV_TM_CCORR), Correlation coefficient matching methods (method = CV_TM_CCOEFF), Normalized methods, Normalized methods, Exercises

Matlab interface, What Is OpenCV?, Smoothing, Stereo Rectification

matrix, OpenCV Primitive Data Types, Arrays of Points, Matrix and Image Types, CvMat Matrix Structure, CvMat Matrix Structure, CvMat Matrix Structure, CvMat Matrix Structure, CvMat Matrix Structure, CvMat Matrix Structure, CvMat Matrix Structure, The easy way, The easy way, The hard way, The hard way, The hard way, The right way, The right way, The right way, The right way, Arrays of Points, Arrays of Points, Arrays of Points, cvGEMM, cvMul, The Essential and Fundamental Matrices, The Essential and Fundamental Matrices, Essential matrix math, Essential matrix math, Fundamental matrix math, How OpenCV handles all of this, How OpenCV handles all of this, Stereo Calibration, Rectification, and Correspondence Code, Structure from Motion

accessing data in, CvMat Matrix Structure, The easy way, The hard way, The hard way, The right way, The right way, Arrays of Points

array, The right way

creating, CvMat Matrix Structure

data types, OpenCV Primitive Data Types, Arrays of Points, Matrix and Image Types, CvMat Matrix Structure, CvMat Matrix Structure, CvMat Matrix Structure, The easy way, The hard way, The right way, Arrays of Points, Arrays of Points

elements of, CvMat Matrix Structure

essential, The Essential and Fundamental Matrices, Essential matrix math, Essential matrix math, Stereo Calibration, Rectification, and Correspondence Code

fundamental, The Essential and Fundamental Matrices, Fundamental matrix math, How OpenCV handles all of this, How OpenCV handles all of this, Structure from Motion

header, CvMat Matrix Structure

multiplication, cvGEMM, cvMul

maximally stable external region (MSER), Specific Items

Maydt, Face Detection or Haar Classifier

mean-shift segmentation/tracking, Advanced Background Method, Mean-Shift Segmentation, Mean-Shift Segmentation, Mean-Shift Segmentation, Mean-Shift and Camshift Tracking, Mean-Shift, Mean-Shift, Mean-Shift, Mean-Shift, K-Means

mechanical turk, Using Machine Learning in Vision

median filter, Smoothing, Smoothing, Smoothing, Smoothing

memory, Arrays of Points, Arrays of Points, Contours, Contour Finding, Memory Storage, Sequences, Slices, Copying, and Moving Data, Slices, Copying, and Moving Data, Sequence Readers and Sequence Writers, Contour Finding

layout, Arrays of Points, Arrays of Points

storage, Contours, Contour Finding, Memory Storage, Sequences, Slices, Copying, and Moving Data, Slices, Copying, and Moving Data, Sequence Readers and Sequence Writers, Contour Finding

misclassification, Cross-validation, bootstrapping, ROC curves, and confusion matrices, Misclassification impurity

missing values, Training, Boosting Code

MIT Media Lab, The Origin of OpenCV, Motion Templates

MJPG (motion jpeg), Writing to an AVI File

MLL (Machine Learning Library), What Is OpenCV?

MLP (multilayer perceptron), OpenCV ML Algorithms, Boosting Code, Multilayer Perceptron

moments, Moments, More About Moments, More About Moments, More About Moments, More About Moments, More About Moments, Hierarchical Matching

central, More About Moments

defined, Moments

Hu, More About Moments, More About Moments, More About Moments, Hierarchical Matching

normalized, More About Moments

morphological transformations, Image Morphology, Flood Fill, Dilation and Erosion, Morphological gradient, Dilation and Erosion, Morphological gradient, Dilation and Erosion, Dilation and Erosion, Dilation and Erosion, Dilation and Erosion, Dilation and Erosion, Making Your Own Kernel, Making Your Own Kernel, Making Your Own Kernel, Making Your Own Kernel, More General Morphology, More General Morphology, More General Morphology, More General Morphology, Opening and closing, Opening and closing, Opening and closing, Opening and closing, Morphological gradient, Morphological gradient, Morphological gradient, Morphological gradient, Morphological gradient, Morphological gradient, Top Hat and Black Hat, Top Hat and Black Hat, Top Hat and Black Hat, Flood Fill, Flood Fill, Flood Fill, Flood Fill, Flood Fill, Flood Fill

closing operation, More General Morphology, Opening and closing, Top Hat and Black Hat

custom kernels, Making Your Own Kernel, More General Morphology

dilation, Dilation and Erosion, Morphological gradient, Dilation and Erosion, Dilation and Erosion, Making Your Own Kernel, Morphological gradient

erosion, Dilation and Erosion, Morphological gradient, Dilation and Erosion, Morphological gradient

gradient operation, Opening and closing, Morphological gradient, Morphological gradient, Morphological gradient, Flood Fill, Flood Fill

intensity images, Dilation and Erosion

opening operation, More General Morphology, Opening and closing

motion, Systems with dynamics, Systems with dynamics, Systems with dynamics

control, Systems with dynamics

dynamical, Systems with dynamics

random, Systems with dynamics

motion jpeg (MJPG), Writing to an AVI File

motion templates, Motion Templates, Motion Templates, Motion Templates, Motion Templates, Motion Templates, Motion Templates, Motion Templates, Motion Templates, Motion Templates

mouse events, Mouse Events, Mouse Events, Mouse Events, Mouse Events, Mouse Events, Mouse Events, Mouse Events

MRFs (Markov random fields), Specific Items

MSER (maximally stable external region), Directions, Specific Items

multilayer perception (MLP), OpenCV ML Algorithms, Boosting Code, Multilayer Perceptron

mushrooms example, Random Tree Code, Random Tree Code

N

Newton's method, How Lucas-Kanade works

Ng, Variable Importance

nonpyramidal Lucas-Kanade dense optical flow, Lucas-Kanade code

normalized template matching, Normalized methods

Numpy, Specific Items

O

object silhouettes, Motion Templates, Motion Templates, Motion Templates, Motion Templates, Motion Templates, Motion Templates, Motion Templates

offset image patches, Specific Items

onTrackbarSlide() function, Moving Around

OOB (out of bag) measure, Random Trees

OpenCV, What Is OpenCV?, What Is OpenCV?, Who Uses OpenCV?, What Is Computer Vision?, What Is Computer Vision?, The Origin of OpenCV, Who Owns OpenCV?, More OpenCV Documentation, Documentation via the Wiki, OpenCV Structure and Content, OpenCV Structure and Content, Portability, Getting Started, Getting Started, Getting Started, Getting Started, OpenCV Primitive Data Types, Integrated Performance Primitives, Verifying Installation, Past and Future, Directions

definition and purpose, What Is OpenCV?, What Is Computer Vision?

directories, Getting Started

documentation, More OpenCV Documentation, Documentation via the Wiki

download and installation, Getting Started, OpenCV Primitive Data Types, Verifying Installation

future developments, The Origin of OpenCV, OpenCV Structure and Content, Past and Future, Directions

header files, Getting Started

how to use, What Is Computer Vision?

libraries, Getting Started

license, Who Uses OpenCV?

optimization with IPP, Who Owns OpenCV?, Integrated Performance Primitives

portability, Portability

programming languages, What Is OpenCV?

structure and content, OpenCV Structure and Content

OpenMP, Learning New Objects

operator functions, Matrix and Image Operators, cvAbs, cvAbsDiff, and cvAbsDiffS

optical flow, Optical Flow, Optical Flow, Lucas-Kanade Method, How Lucas-Kanade works, How Lucas-Kanade works, How Lucas-Kanade works, How Lucas-Kanade works, Pyramid Lucas-Kanade code, Pyramid Lucas-Kanade code, Pyramid Lucas-Kanade code, Dense Tracking Techniques, Dense Tracking Techniques, Horn-Schunck method, Structure from Motion, Directions

order constraint, Stereo Correspondence

out of bag (OOB) measure, Random Trees

overfitting (variance), Diagnosing Machine Learning Problems, Diagnosing Machine Learning Problems

P

pairwise geometrical histogram (PGH), Pairwise Geometrical Histograms, Pairwise Geometrical Histograms

Pearson, Chi-square (method = CV_COMP_CHISQR)

Peleg, Earth Mover's Distance

PGH (pairwise geometrical histogram), Pairwise Geometrical Histograms, Pairwise Geometrical Histograms

PHOG, Directions

PHOW (pyramid histogram embedding of other features), Directions, Specific Items

pinhole camera model, Camera Models and Calibration, Camera Model, Camera Model, Camera Model, What's under the hood?

pipeline, A Not-So-Simple Transformation

Pisarevsky, The Origin of OpenCV

pixel types, IplImage Data Structure

pixels, Smoothing, Convolution

planar homography, Homography

plumb bob model, Lens Distortions

point, Polygon Approximations

pointer arithmetic, The hard way, The right way, The right way, Accessing Image Data

polar to Cartesian coordinates, CartToPolar and PolarToCart, LogPolar, CartToPolar and PolarToCart, LogPolar

polygons, Polygons, Polygons

portability guide, Exercises

pose, Rotation Matrix and Translation Vector, Projection and 3D Vision, POSIT: 3D Pose Estimation

POSIT (Pose from Orthography and Scaling with Iteration), POSIT: 3D Pose Estimation, POSIT: 3D Pose Estimation, POSIT: 3D Pose Estimation

PPHT (progressive probabilistic Hough transform), Hough Line Transform

prediction, Estimators

primitive data types, OpenCV Primitive Data Types

principal points, Camera Model, Triangulation

principal rays, Triangulation

probabilistic graphical models, Naïve/Normal Bayes Classifier

progressive probabilistic Hough transform (PPHT), Hough Line Transform

projections, Projection and 3D Vision

projective planes, Camera Model, Affine and Perspective Transformations

projective transforms, Sparse perspective transformations, Basic Projective Geometry, Affine and Perspective Transformations

pyramid histogram embedding of other features (PHOW), Specific Items

pyramidal Lucas-Kanade optical flow, Horn-Schunck method

pyramids, Image Pyramids, Image Pyramids, Image Pyramids

Python, What Is OpenCV?, Linux, Directions, Specific Items

R

radial distortions, Lens Distortions, Lens Distortions, Lens Distortions, Calibration function, Stereo Calibration

random forests, Random Trees

random motion, Systems with dynamics

RANSAC algorithm, How OpenCV handles all of this

receiver operating characteristic (ROC), Cross-validation, bootstrapping, ROC curves, and confusion matrices

recognition, Supervised and Unsupervised Data

recognition by context, Specific Items

recognition tasks, cvCmp and cvCmpS, Opening and closing, LogPolar, Histograms and Matching, Some More Complicated Stuff, Back Projection, Back Projection, Patch-based back projection, Patch-based back projection, Patch-based back projection, Template Matching, Template Matching, Normalized methods, Pairwise Geometrical Histograms, Background Subtraction, A Slice of Pixels, Averaging Background Method, Estimators, Estimators, Kalman equations, POSIT: 3D Pose Estimation, Using Machine Learning in Vision, Diagnosing Machine Learning Problems, Cross-validation, bootstrapping, ROC curves, and confusion matrices, Cross-validation, bootstrapping, ROC curves, and confusion matrices, Decision Tree Results, Works well on …, Past and Future, Past and Future, Past and Future

blocky features, Works well on …

car in motion, Kalman equations

depth perception, Past and Future

edible mushrooms, Decision Tree Results

flesh color, Some More Complicated Stuff, Back Projection, Back Projection, Patch-based back projection, Patch-based back projection, Template Matching

flight simulator, POSIT: 3D Pose Estimation

flowers, Cross-validation, bootstrapping, ROC curves, and confusion matrices

gestures, Histograms and Matching

hand, Averaging Background Method

local navigation on Mars, Past and Future

microscope slides, Opening and closing, Cross-validation, bootstrapping, ROC curves, and confusion matrices

novel information from video stream, cvCmp and cvCmpS

object, LogPolar, Patch-based back projection, Template Matching

person, Estimators, Estimators, Using Machine Learning in Vision, Diagnosing Machine Learning Problems

product inspection, Normalized methods, Past and Future

shape, Pairwise Geometrical Histograms

tree, Background Subtraction, A Slice of Pixels

rectangles, Circles and Ellipses, Exercises, Stretch, Shrink, Warp, and Rotate, Stretch, Shrink, Warp, and Rotate, Bounding boxes

bounding, Bounding boxes

drawing, Circles and Ellipses, Exercises

parallelogram, Stretch, Shrink, Warp, and Rotate

trapezoid, Stretch, Shrink, Warp, and Rotate

rectification, Stereo Calibration, Rectification map, Stereo Rectification, Uncalibrated stereo rectification: Hartley's algorithm, Calibrated stereo rectification: Bouguet's algorithm, Calibrated stereo rectification: Bouguet's algorithm, Rectification map, Rectification map, Rectification map

region of interest (ROI), IplImage Data Structure, More on ROI and widthStep, More on ROI and widthStep, cvAdd, cvAddS, cvAddWeighted, and alpha blending

regression, Supervised and Unsupervised Data

regularization constant, Horn-Schunck method

reinforcement (deferred) learning, Supervised and Unsupervised Data

remapping, Remap

reprojection, Stereo Calibration, Calibrated stereo rectification: Bouguet's algorithm, Calibrated stereo rectification: Bouguet's algorithm, Calibrated stereo rectification: Bouguet's algorithm, Depth Maps from 3D Reprojection

resizing, Stretch, Shrink, Warp, and Rotate

RGB images, Accessing Image Data, A Slice of Pixels

robot tasks, What Is Computer Vision?, Bird's-Eye View Transform Example, Bird's-Eye View Transform Example, Stereo Rectification, Depth Maps from 3D Reprojection, Prediction, Past and Future, Past and Future

camera on arm, Stereo Rectification

car on road, Bird's-Eye View Transform Example

cart, Bird's-Eye View Transform Example

objects, Depth Maps from 3D Reprojection, Past and Future

office security, What Is Computer Vision?

planning, Past and Future

scanning a scene, Prediction

robotics, The Origin of OpenCV, Projection and 3D Vision, Structure from Motion, Past and Future, Specific Items, Afterword, Afterword

ROC (receiver operating characteristic), Cross-validation, bootstrapping, ROC curves, and confusion matrices, Cross-validation, bootstrapping, ROC curves, and confusion matrices

Rodrigues, Rodrigues Transform

Rodrigues transform, Rodrigues Transform, Rodrigues Transform, Projections

ROI (region of interest), IplImage Data Structure, IplImage Data Structure, More on ROI and widthStep, More on ROI and widthStep, cvAdd, cvAddS, cvAddWeighted, and alpha blending

Rom, Earth Mover's Distance

Rosenfeld-Johnson algorithm, Polygon Approximations

rotation matrix, Rotation Matrix and Translation Vector, Rotation Matrix and Translation Vector, Rotation Matrix and Translation Vector

rotation vector, Calibration function

Ruby interface, What Is OpenCV?

running average, Accumulating means, variances, and covariances

S

SAD (sum of absolute difference), Stereo Correspondence, Stereo Correspondence

scalable recognition techniques, Specific Items

scalar tuples, OpenCV Primitive Data Types

scale-invariant feature transform (SIFT), Invariant Features, Using Machine Learning in Vision, Specific Items

scene modeling, Scene Modeling

scene transitions, Histograms and Matching

Schapire, Boosting

Scharr filter, Scharr Filter, Motion Templates

SciPy, Specific Items

scrambled covariance matrix, cvCalcCovarMatrix, cvCalcCovarMatrix

seed point, Flood Fill

segmentation, Parts and Segments

self-cleaning procedure, A Not-So-Simple Transformation

sequences, Image Pyramids, Sequences, Contour Finding, Creating a Sequence, Creating a Sequence, Direct Access to Sequence Elements, Creating a Sequence, Deleting a Sequence, Direct Access to Sequence Elements, Slices, Copying, and Moving Data, Slices, Copying, and Moving Data, Using a Sequence As a Stack, Slices, Copying, and Moving Data, Using a Sequence As a Stack, Slices, Copying, and Moving Data, Slices, Copying, and Moving Data, Using a Sequence As a Stack, Using a Sequence As a Stack, Using a Sequence As a Stack, Inserting and Removing Elements, Sequence Block Size, Sequence Readers and Sequence Writers, Sequence Readers and Sequence Writers, Sequences and Arrays, Sequence Readers and Sequence Writers, Sequences and Arrays, Sequence Readers and Sequence Writers, Sequences and Arrays, Sequences and Arrays, Sequences and Arrays, Sequences and Arrays, Contour Finding

accessing, Image Pyramids

block size, Sequence Block Size

converting to array, Sequence Readers and Sequence Writers

copying, Slices, Copying, and Moving Data, Using a Sequence As a Stack, Using a Sequence As a Stack

creating, Creating a Sequence, Direct Access to Sequence Elements, Direct Access to Sequence Elements

deleting, Deleting a Sequence

inserting and removing elements from, Inserting and Removing Elements

moving, Slices, Copying, and Moving Data, Using a Sequence As a Stack, Using a Sequence As a Stack

partitioning, Slices, Copying, and Moving Data

readers, Sequence Readers and Sequence Writers, Sequences and Arrays, Sequences and Arrays

sorting, Slices, Copying, and Moving Data

stack, Using a Sequence As a Stack

writers, Sequence Readers and Sequence Writers, Sequences and Arrays, Sequences and Arrays

setup, Getting Started

Shape Context, Directions, Specific Items

Shi and Tomasi corners, Corner Finding, Invariant Features

SHT (standard Hough transform), Hough Line Transform

SIFT (scale-invariant feature transform), Invariant Features, Using Machine Learning in Vision

silhouettes, Motion Templates, Motion Templates, Motion Templates, Motion Templates

simultaneous localization and mapping (SLAM), Specific Items

singular value decomposition, cvDet, cvEigenVV, cvSVD, What's under the hood?

singularity threshold, cvSVBkSb

SLAM (simultaneous localization and mapping), Specific Items

slider trackbar, Moving Around, Moving Around, Moving Around, Sliders, Trackbars, and Switches, Sliders, Trackbars, and Switches, No Buttons, No Buttons, Reading Video, A Contour Example

smoothing, A Simple Transformation, A Simple Transformation, Smoothing, Smoothing, Smoothing, Smoothing, Smoothing, Smoothing

Sobel derivatives, Convolution, Gradients and Sobel Derivatives, Gradients and Sobel Derivatives, Scharr Filter, Laplace, Hough Circle Transform, Corner Finding, Motion Templates

Software Performance Libraries group, The Origin of OpenCV

SourceForge site, Downloading and Installing OpenCV

spatial coherence, How Lucas-Kanade works

speckle noise, Dilation and Erosion, Stereo Correspondence

spectrum multiplication, Spectrum Multiplication

square differences matching method, Square difference matching method (method = CV_TM_SQDIFF)

stack, Using a Sequence As a Stack

standard Hough transform (SHT), Hough Line Transform

Stanford's "Stanley" robot, Who Uses OpenCV?

statistical machine learning, Diagnosing Machine Learning Problems

stereo imaging, Stereo Imaging, Stereo Calibration, Stereo Calibration, Stereo Calibration, Stereo Calibration, Stereo Calibration, Uncalibrated stereo rectification: Hartley's algorithm, Rectification map, Stereo Correspondence, Stereo Correspondence, Stereo Correspondence, Stereo Correspondence, Stereo Correspondence, Stereo Correspondence, Stereo Calibration, Rectification, and Correspondence Code, Stereo Calibration, Rectification, and Correspondence Code, Stereo Calibration, Rectification, and Correspondence Code, Stereo Calibration, Rectification, and Correspondence Code, Depth Maps from 3D Reprojection

calibration, Stereo Calibration, Stereo Calibration, Stereo Calibration, Stereo Calibration, Stereo Calibration, Rectification, and Correspondence Code, Stereo Calibration, Rectification, and Correspondence Code, Stereo Calibration, Rectification, and Correspondence Code

correspondence, Stereo Correspondence, Stereo Correspondence, Stereo Correspondence, Stereo Correspondence, Stereo Correspondence, Stereo Correspondence, Stereo Calibration, Rectification, and Correspondence Code

overview of, Stereo Imaging

rectification, Stereo Calibration, Uncalibrated stereo rectification: Hartley's algorithm, Rectification map, Depth Maps from 3D Reprojection

strong classifiers, Boosting, Boosting Code

structured light, Directions

subpixel corners, Subpixel Corners, Subpixel Corners, Subpixel Corners, Subpixel corners, Directions

sum of absolute difference (SAD), Stereo Correspondence

summary characteristics, Summary Characteristics

supervised/unsupervised data, Training and Test Set

support vector machine (SVM), OpenCV ML Algorithms, Support Vector Machine

SURF gradient histogram grids, Directions, Specific Items

SVD (singular value decomposition), cvDet, cvEigenVV, cvSVD, What's under the hood?

SVM (support vector machine), Cross-validation, bootstrapping, ROC curves, and confusion matrices, Support Vector Machine

switches, No Buttons

T

tangential distortions, Lens Distortions, Lens Distortions, Lens Distortions

Taylor series, Lens Distortions

Teh-Chin algorithm, Polygon Approximations

temporal persistence, How Lucas-Kanade works

test sets, Training and Test Set, Using Machine Learning in Vision, OpenCV ML Algorithms, Using Machine Learning in Vision, Using Machine Learning in Vision

text, Fonts and Text, Fonts and Text

texture descriptors, OpenCV Structure and Content

textured scene, Stereo Correspondence

thresholds, cvSVBkSb, Image Pyramids, Adaptive Threshold, Exercises, Adaptive Threshold, Adaptive Threshold, Adaptive Threshold, Exercises, Exercises, Canny

adaptive, Adaptive Threshold, Exercises, Adaptive Threshold, Adaptive Threshold, Adaptive Threshold, Exercises

binary, Exercises

hysteresis, Canny

singularity, cvSVBkSb

types, Image Pyramids

timer function (wait for keystroke), First Program—Display a Picture, Second Program—AVI Video

Top Hat operation, Top Hat and Black Hat, Top Hat and Black Hat, Flood Fill

trackbar slider, Moving Around, Moving Around, Moving Around, Sliders, Trackbars, and Switches, Sliders, Trackbars, and Switches, No Buttons, A Contour Example

tracking, The Basics of Tracking, The Basics of Tracking, Corner Finding, Subpixel Corners, Corner Finding, Corner Finding, Corner Finding, Subpixel Corners, Subpixel Corners, Subpixel Corners

corner finding, Corner Finding, Subpixel Corners, Corner Finding, Corner Finding, Corner Finding, Subpixel Corners, Subpixel Corners, Subpixel Corners

identification, The Basics of Tracking

modeling, The Basics of Tracking

training sets, Training and Test Set, Using Machine Learning in Vision, Training and Test Set, OpenCV ML Algorithms, Using Machine Learning in Vision, Using Machine Learning in Vision

transforms, Overview, Stretch, Shrink, Warp, and Rotate, Discrete Fourier Transform (DFT), Discrete Fourier Transform (DFT), Distance Transform, Histogram Equalization, Distance Transform, Histogram Equalization, Histogram Equalization

distance, Distance Transform, Histogram Equalization, Distance Transform, Histogram Equalization, Histogram Equalization

forward, Discrete Fourier Transform (DFT)

inverse, Discrete Fourier Transform (DFT)

overview of, Overview

perspective, Stretch, Shrink, Warp, and Rotate

translation vectors, Rotation Matrix and Translation Vector, Rotation Matrix and Translation Vector, Rotation Matrix and Translation Vector, Calibration function

trees, Contour Finding, Contour Finding, Hierarchical Matching, Hierarchical Matching

triangulation, Delaunay Triangulation, Voronoi Tesselation, Delaunay Triangulation, Voronoi Tesselation, Usage Examples, Stereo Imaging

U

undistortion, Undistortion

Unix systems, WaitKey

updates, Getting the Latest OpenCV via CVS

user community, Who Uses OpenCV?, The Origin of OpenCV

user input, First Program—Display a Picture, First Program—Display a Picture, Second Program—AVI Video, Moving Around, Moving Around, A Portable Graphics Toolkit, WaitKey, Mouse Events, Mouse Events, Mouse Events, Mouse Events, Mouse Events, Mouse Events, Sliders, Trackbars, and Switches, Sliders, Trackbars, and Switches, No Buttons, Watershed Algorithm, K-Means Code

marked objects, Watershed Algorithm

mouse, Mouse Events, Mouse Events, Mouse Events, Mouse Events, Mouse Events, Mouse Events

trackbar, Sliders, Trackbars, and Switches, Sliders, Trackbars, and Switches, No Buttons

wait for keyboard, First Program—Display a Picture, First Program—Display a Picture, Second Program—AVI Video, Moving Around, Moving Around, WaitKey, K-Means Code

window functions, A Portable Graphics Toolkit

V

validation sets, Training and Test Set

variable importance, Variable Importance, Training the tree, Decision Tree Results, Decision Tree Results, Boosting, Random Tree Code, Random Tree Code, Random Tree Code, Using Random Trees

variables, First Program—Display a Picture, Moving Around, IplImage Data Structure, More on ROI and widthStep

global, Moving Around

IplImage, First Program—Display a Picture, IplImage Data Structure, More on ROI and widthStep

variance, Accumulating means, variances, and covariances

variance (overfitting), Diagnosing Machine Learning Problems, Diagnosing Machine Learning Problems, Diagnosing Machine Learning Problems, Cross-validation, bootstrapping, ROC curves, and confusion matrices, Decision Tree Results

Viola, Face Detection or Haar Classifier, Supervised Learning and Boosting Theory, Boosting in the Haar cascade, Viola-Jones Classifier Theory, Works well on …, Learning New Objects

Viola-Jones rejection cascade (detector), Face Detection or Haar Classifier, Boosting in the Haar cascade, Boosting in the Haar cascade, Viola-Jones Classifier Theory, Works well on …

virtual pixels, Smoothing, Convolution

vision, What Is OpenCV?, Who Uses OpenCV?, Who Uses OpenCV?, Who Uses OpenCV?, What Is Computer Vision?, What Is Computer Vision?, What Is Computer Vision?, What Is Computer Vision?, What Is Computer Vision?, What Is Computer Vision?, What Is Computer Vision?, OpenCV Structure and Content, Opening and closing, Background Subtraction, Weaknesses of Background Subtraction, Camera Models and Calibration, Camera Models and Calibration, Using Machine Learning in Vision, Support Vector Machine

applications of, What Is OpenCV?, Who Uses OpenCV?, What Is Computer Vision?, What Is Computer Vision?, What Is Computer Vision?, Opening and closing, Background Subtraction, Weaknesses of Background Subtraction

challenges of, Who Uses OpenCV?, What Is Computer Vision?, What Is Computer Vision?, What Is Computer Vision?, Camera Models and Calibration, Using Machine Learning in Vision

Visual Studio, Getting Started

Voronoi iteration, K-Means

Voronoi tessellation, Delaunay Triangulation, Voronoi Tesselation, Creating a Delaunay or Voronoi Subdivision, Navigating Delaunay Subdivisions, Walking on edges, Method 2: Step through a sequence of points or edges, Identifying the bounding triangle or edges on the convex hull and walking the hull, Usage Examples, Usage Examples

W

walking on edges, Walking on edges

warping, Stretch, Shrink, Warp, and Rotate, Computing the affine map matrix, Dense affine transformations, Dense affine transformations, Computing the affine map matrix

watercolor effect, Smoothing

watershed algorithm, Watershed Algorithm, Image Repair by Inpainting, Watershed Algorithm, Watershed Algorithm, Image Repair by Inpainting

weak classifiers, OpenCV ML Algorithms, Boosting, AdaBoost, Boosting Code, Boosting Code, Boosting Code, Boosting Code, Boosting in the Haar cascade, Learning New Objects

weak-perspective approximation, POSIT: 3D Pose Estimation

Werman, Earth Mover's Distance

whitening, Cross-validation, bootstrapping, ROC curves, and confusion matrices

widthStep image parameter, IplImage Data Structure, Matrix and Image Operators, Accessing Image Data, More on ROI and widthStep, Matrix and Image Operators

Wiki sites, Downloading and Installing OpenCV, Documentation via the Wiki, Common Routines in the ML Library

Willow Garage, The Origin of OpenCV, Past and Future

Win32 systems, cvFlip, Creating a Window, WaitKey

Windows, Who Owns OpenCV?, Documentation Available in HTML, Install, Linux, Getting the Latest OpenCV via CVS, Documentation Available in HTML

OpenCV installation, Who Owns OpenCV?, Documentation Available in HTML, Install, Linux, Getting the Latest OpenCV via CVS, Documentation Available in HTML

windows, First Program—Display a Picture, First Program—Display a Picture, A Simple Transformation, Creating a Window, Creating a Window, Displaying Images

clean up, First Program—Display a Picture, Displaying Images

creating, A Simple Transformation, Creating a Window

names versus handles, Creating a Window

properties of, First Program—Display a Picture

wrapper function, A Not-So-Simple Transformation

Y

Yahoo groups forum, Who Uses OpenCV?

Z

Zhang's method, What's under the hood?

Zisserman's approximate nearest neighbor suggestion, Specific Items