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Index
Title Page Copyright and Credits
Practical Computer Vision
Dedication Packt Upsell
Why subscribe? PacktPub.com
Contributors
About the author About the reviewer Packt is searching for authors like you
Preface
Who this book is for What this book covers To get the most out of this book
Download the example code files Download the color images Conventions used
Get in touch
Reviews
A Fast Introduction to Computer Vision
What constitutes computer vision? Computer vision is everywhere Getting started
Reading an image  Image color conversions
Computer vision research conferences Summary
Libraries, Development Platform, and Datasets
Libraries and installation
Installing Anaconda
NumPy Matplotlib SciPy Jupyter notebook
Installing OpenCV
OpenCV Anaconda installation  OpenCV build from source Opencv FAQs
TensorFlow for deep learning Keras for deep learning 
Datasets
ImageNet MNIST CIFAR-10 Pascal VOC MSCOCO TUM RGB-D dataset
Summary References
Image Filtering and Transformations in OpenCV
Datasets and libraries required Image manipulation Introduction to filters
Linear filters
2D linear filters Box filters Properties of linear filters
Non-linear filters 
Smoothing a photo  Histogram equalization Median filter 
Image gradients
Transformation of an image
Translation Rotation  Affine transform 
Image pyramids Summary
What is a Feature?
Features use cases 
Datasets and libraries Why are features important?
Harris Corner Detection
FAST features ORB features
FAST feature limitations BRIEF Descriptors and their limitations ORB features using OpenCV
The black box feature Application – find your object in an image  Applications – is it similar?
Summary References
Convolutional Neural Networks
Datasets and libraries used Introduction to neural networks
A simple neural network
Revisiting the convolution operation Convolutional Neural Networks
The convolution layer The activation layer The pooling layer The fully connected layer Batch Normalization Dropout
CNN in practice 
Fashion-MNIST classifier training code Analysis of CNNs 
Popular CNN architectures
VGGNet Inception models ResNet model
Transfer learning
Summary
Feature-Based Object Detection
Introduction to object detection Challenges in object detection Dataset and libraries used Methods for object detection
Deep learning-based object detection
Two-stage detectors Demo – Faster R-CNN with ResNet-101 One-stage detectors Demo
Summary References
Segmentation and Tracking
Datasets and libraries Segmentation
Challenges in segmentation  CNNs for segmentation Implementation of FCN
Tracking
Challenges in tracking Methods for object tracking
MOSSE tracker Deep SORT
Summary References
3D Computer Vision
Dataset and libraries Applications Image formation Aligning images  Visual odometry Visual SLAM Summary References
Mathematics for Computer Vision
Datasets and libraries Linear algebra
Vectors
Addition Subtraction Vector multiplication Vector norm Orthogonality
Matrices
Operations on matrices
Addition Subtraction Matrix multiplication
Matrix properties
Transpose Identity matrix Diagonal matrix Symmetric matrix Trace of a matrix Determinant Norm of a matrix Getting the inverse of a matrix  Orthogonality Computing eigen values and eigen vectors
Hessian matrix Singular Value Decomposition
Introduction to probability theory
What are random variables? Expectation Variance Probability distributions
Bernoulli distribution Binomial distribution Poisson distribution Uniform distribution Gaussian distribution
Joint distribution Marginal distribution Conditional distribution Bayes theorem
Summary
Machine Learning for Computer Vision
What is machine learning? Kinds of machine learning techniques
Supervised learning
Classification Regression
Unsupervised learning
Dimensionality's curse A rolling-ball view of learning Useful tools
Preprocessing
Normalization Noise
Postprocessing
Evaluation
Precision Recall F-measure
Summary
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