Practical Computer Vision

A practical guide designed to get you from basics to current state of art in computer vision systems.
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Practical Computer Vision

Abhinav Dadhich

A practical guide designed to get you from basics to current state of art in computer vision systems.
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Book Details

ISBN 139781788297684
Paperback234 pages

Book Description

In this book, you will find several recently proposed methods in various domains of computer vision. You will start by setting up the proper Python environment to work on practical applications. This includes setting up libraries such as OpenCV, TensorFlow, and Keras using Anaconda. Using these libraries, you'll start to understand the concepts of image transformation and filtering. You will find a detailed explanation of feature detectors such as FAST and ORB; you'll use them to find similar-looking objects.

With an introduction to convolutional neural nets, you will learn how to build a deep neural net using Keras and how to use it to classify the Fashion-MNIST dataset. With regard to object detection, you will learn the implementation of a simple face detector as well as the workings of complex deep-learning-based object detectors such as Faster R-CNN and SSD using TensorFlow. You'll get started with semantic segmentation using FCN models and track objects with Deep SORT. Not only this, you will also use Visual SLAM techniques such as ORB-SLAM on a standard dataset. 

By the end of this book, you will have a firm understanding of the different computer vision techniques and how to apply them in your applications.

Table of Contents

Chapter 1: A Fast Introduction to Computer Vision
What constitutes computer vision?
Computer vision is everywhere
Getting started
Computer vision research conferences
Summary
Chapter 2: Libraries, Development Platform, and Datasets
Libraries and installation
Datasets
Summary
References
Chapter 3: Image Filtering and Transformations in OpenCV
Datasets and libraries required
Image manipulation
Introduction to filters
Transformation of an image
Image pyramids
Summary
Chapter 4: What is a Feature?
Features use cases 
Harris Corner Detection
Summary
References
Chapter 5: Convolutional Neural Networks
Datasets and libraries used
Introduction to neural networks
Revisiting the convolution operation
Convolutional Neural Networks
CNN in practice 
Summary
Chapter 6: Feature-Based Object Detection
Introduction to object detection
Challenges in object detection
Dataset and libraries used
Methods for object detection
Summary
References
Chapter 7: Segmentation and Tracking
Datasets and libraries
Segmentation
Tracking
Summary
References
Chapter 8: 3D Computer Vision
Dataset and libraries
Applications
Image formation
Aligning images 
Visual odometry
Visual SLAM
Summary
References
Chapter 9: Mathematics for Computer Vision
Datasets and libraries
Linear algebra
Introduction to probability theory
Summary
Chapter 10: Machine Learning for Computer Vision
What is machine learning?
Kinds of machine learning techniques
Dimensionality's curse
A rolling-ball view of learning
Useful tools
Evaluation
Summary

What You Will Learn

  • Learn the basics of image manipulation with OpenCV
  • Implement and visualize image filters such as smoothing, dilation, histogram equalization, and more 
  • Set up various libraries and platforms, such as OpenCV, Keras, and Tensorflow, in order to start using computer vision, along with appropriate datasets for each chapter, such as 
  • MSCOCO, MOT, and Fashion-MNIST
  • Understand image transformation and downsampling with practical implementations. 
  • Explore neural networks for computer vision and convolutional neural networks using Keras 
  • Understand working on deep-learning-based object detection such as Faster-R-CNN, SSD, and more
  • Explore deep-learning-based object tracking in action
  • Understand Visual SLAM techniques such as ORB-SLAM

Authors

Table of Contents

Chapter 1: A Fast Introduction to Computer Vision
What constitutes computer vision?
Computer vision is everywhere
Getting started
Computer vision research conferences
Summary
Chapter 2: Libraries, Development Platform, and Datasets
Libraries and installation
Datasets
Summary
References
Chapter 3: Image Filtering and Transformations in OpenCV
Datasets and libraries required
Image manipulation
Introduction to filters
Transformation of an image
Image pyramids
Summary
Chapter 4: What is a Feature?
Features use cases 
Harris Corner Detection
Summary
References
Chapter 5: Convolutional Neural Networks
Datasets and libraries used
Introduction to neural networks
Revisiting the convolution operation
Convolutional Neural Networks
CNN in practice 
Summary
Chapter 6: Feature-Based Object Detection
Introduction to object detection
Challenges in object detection
Dataset and libraries used
Methods for object detection
Summary
References
Chapter 7: Segmentation and Tracking
Datasets and libraries
Segmentation
Tracking
Summary
References
Chapter 8: 3D Computer Vision
Dataset and libraries
Applications
Image formation
Aligning images 
Visual odometry
Visual SLAM
Summary
References
Chapter 9: Mathematics for Computer Vision
Datasets and libraries
Linear algebra
Introduction to probability theory
Summary
Chapter 10: Machine Learning for Computer Vision
What is machine learning?
Kinds of machine learning techniques
Dimensionality's curse
A rolling-ball view of learning
Useful tools
Evaluation
Summary

Book Details

ISBN 139781788297684
Paperback234 pages
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