Computer Vision and Image Processing
Fundamentals and Applications
Preview
Book Description
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.
Table of Contents
I Image Formation and Image Processing
1 Introduction to Computer Vision and Basic Concepts of Image
Formation
1.1 Introduction and Goals of Computer Vision
1.2 Image Formation and Radiometry
1.3 Geometric Transformation
1.4 Geometric Camera Models
1.5 Image Reconstruction from a Series of Projections
1.6 Summary
2 Image Processing Concepts
2.1 Fundamentals of Image Processing
2.2 Image Transforms
2.3 Image Filtering
2.4 Colour Image Processing
2.5 Mathematical Morphology
2.6 Image Segmentation
2.7 Summary
II Image Features
3 Image Descriptors and Features
3.1 Texture Descriptors
3.2 Colour Features
3.3 Edge Detection
3.4 Object Boundary and Shape Representations
3.5 Interest or Corner Point Detectors
3.6 Histogram of Oriented Gradients (HOG)
3.7 Scale Invariant Feature Transform (SIFT)
3.8 Speeded up Robust Features (SURF)
3.9 Saliency
3.10 Summary
III Recognition
4 Fundamental Pattern Recognition Concepts
4.1 Introduction to Pattern Recognition
4.2 Linear Regression
4.3 Basic Concepts of Decision Functions
4.4 Elementary Statistical Decision Theory
4.5 Gaussian Classifier
4.6 Parameter Estimation
4.7 Clustering for Knowledge Representation
4.8 Dimension Reduction
4.9 Template Matching
4.10 Artificial Neural Network (ANN) for Pattern Classification
4.11 Convolutional Neural Networks (CNNs)
4.12 Autoencoder
4.13 Summary
IV Applications
5 Applications of Computer Vision
5.1 Machine Learning Algorithms and their Applications in Medical Image Segmentation
5.2 Motion Estimation and Object Tracking
5.3 Face and Facial Expression Recognition
5.4 Gesture Recognition
5.5 Image Fusion
5.6 Programming Examples
Author(s)
Biography
Prof. Manas Kamal Bhuyan received a Ph.D. degree in electronics and communication engineering from the India Institute of Technology (IIT) Guwahati, India. He was with the School of Information Technology and Electrical Engineering, University of Queensland, St. Lucia, QLD, Australia, where he was involved in postdoctoral research. He was also a Researcher with the SAFE Sensor Research Group, NICTA, Brisbane, QLD, Australia. He was an Assistant Professor with the Department of Electrical Engineering, IIT Roorkee, India and Jorhat Engineering College, Assam, India. In 2014, he was a Visiting Professor with Indiana University and Purdue University, Indiana, USA. He is currently a Professor with the Department of Electronics and Electrical Engineering, IIT Guwahati, and Associate Dean of Infrastructure, Planning and Management, IIT Guwahati. His current research interests include image/video processing, computer vision, machine Learning and human computer interactions (HCI), virtual reality and augmented reality. Dr. Bhuyan was a recipient of the National Award for Best Applied Research/Technological Innovation, which was presented by the Honorable President of India, the Prestigious Fullbright-Nehru Academic and Professional Excellence Fellowship, and the BOYSCAST Fellowship. He is an IEEE senior member. He has almost 25 years of industry, teaching, and research experience.