Computer Vision and Image Processing

Fundamentals and Applications

Manas Kamal Bhuyan

• Publisher: CRC Press
• Year: 2019
• ISBN:
  9781351248372
• Edition: 1
• Topic: Computer Science
• Subtopic: Computer Graphics
• Index: Computer Science

The book familiarizes readers with fundamental concepts and issues related to computer vision and major approaches that address them. The focus of the book is on image acquisition and image formation models, radiometric models of image formation, image formation in the camera, image processing concepts, concept of feature extraction and feature selection for pattern classification/recognition, and advanced concepts like object classification, object tracking, image-based rendering, and image registration. Intended to be a companion to a typical teaching course on computer vision, the book takes a problem-solving approach.

Part I

Image Formation and Image Processing

1

Introduction to Computer Vision and Basic Concepts of Image Formation

CONTENTS

1.1Introduction and Goals of Computer Vision

1.2Image Formation and Radiometry

1.2.1Image formation

1.2.2Radiometric quantities

1.2.3Shape from shading

1.2.4Photometric stereo

1.3Geometric Transformation

1.3.12D transformations

1.3.23D transformations

1.4Geometric Camera Models

1.4.1Single camera setup of image formation

1.4.2Image formation in a stereo vision setup

1.4.3Basics of stereo correspondence

1.4.4Issues related to accurate disparity map estimation

1.5Image Reconstruction from a Series of Projections

1.5.1Inverse Radon transform - back-projection method

1.5.2Inverse Radon transform - Fourier transform method

1.6Summary

1.1 Introduction and Goals of Computer Vision

Computer vision is a field of computer science, and it aims at enabling computers to process and identify images and videos in the same way that human vision does. Computer vision aims to mimic the human visual system. The objective is to build artificial systems which can extract information from images, i.e., objective is to make computers understand images and videos. The image data may be a video sequence, depth images, views from multiple cameras, or multi-dimensional data from image sensors. The main objective of computer vision is to describe a real world scene in one or more images and to identify and reconstruct its properties, such as colour characteristics, shape information, texture characteristics, scene illumination, etc.

Let us see the similarity between a human visual system and a computer vision system. As illustrated in Figure 1.1, the basic principle of these two systems is almost same, i.e., conversion of light into useful signals/information from which accurate models of the physical world are constructed. Similarly, when considered at a high level, the structures of human and computer vision are somewhat similar, i.e., both have light sensors which convert photons into a signal (image), a processing step, and finally a mechanism to interpret the signal (object recognition).

FIGURE 1.1 Human visual system vs. computer vision.

The difference between computer vision, image processing and computer graphics...