Book Image

Practical Computer Vision

By : Abhinav Dadhich
Book Image

Practical Computer Vision

By: Abhinav Dadhich

Overview of this book

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.

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Table of Contents (12 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
A Fast Introduction to Computer Vision
A Fast Introduction to Computer Vision
What constitutes computer vision?
Computer vision is everywhere
Getting started
Computer vision research conferences
Summary
2
Libraries, Development Platform, and Datasets
Libraries, Development Platform, and Datasets
Libraries and installation
Datasets
Summary
References
3
Image Filtering and Transformations in OpenCV
Image Filtering and Transformations in OpenCV
Datasets and libraries required
Image manipulation
Introduction to filters
Transformation of an image
Image pyramids
Summary
4
What is a Feature?
What is a Feature?
Features use cases
Harris Corner Detection
Summary
References
5
Convolutional Neural Networks
Convolutional Neural Networks
Datasets and libraries used
Introduction to neural networks
Revisiting the convolution operation
Convolutional Neural Networks
CNN in practice
Summary
6
Feature-Based Object Detection
Feature-Based Object Detection
Introduction to object detection
Challenges in object detection
Dataset and libraries used
Methods for object detection
Summary
References
7
Segmentation and Tracking
Segmentation and Tracking
Datasets and libraries
Segmentation
Tracking
Summary
References
8
3D Computer Vision
3D Computer Vision
Dataset and libraries
Applications
Image formation
Aligning images
Visual odometry
Visual SLAM
Summary
References
9
Mathematics for Computer Vision
Mathematics for Computer Vision
Datasets and libraries
Linear algebra
Introduction to probability theory
Summary
10
Machine Learning for Computer Vision
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
11
Other Books You May Enjoy
Other Books You May Enjoy
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Computer vision research conferences

Some of the conferences to look for latest research and applications are as follows:

  • CVPR: Conference on Computer Vision and Pattern Recognition is held every year and is one of the popular conferences with research papers ranging from both theory and application across a wide domain
  • ICCV: International Conference on Computer Vision is another major conference held every other year attracting one of the best research papers
  • SIGGRAPH: Special Interest Group on Computer Graphics and interactive techniques though more on computer graphics domain has several applications papers that utilizes computer vision techniques.

Other notable conferences include Neural Information Processing Systems (NIPS), International Conference on Machine Learning (ICML), Asian Conference on Computer Vision (ACCV), European Conference on Computer Vision (ECCV), and so on.

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