Name: Practical Computer Vision
Author: Abhinav Dadhich
ISBN: 9781788294768

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Practical Computer Vision

Abhinav Dadhich

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Title Page
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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Practical Computer Vision