Feature Extraction and Image Processing for Computer Vision

by MSc Nixon BSc, Alberto Aguado PhD

Overview

Feature Extraction for Image Processing and Computer Vision is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in MATLAB and Python. Algorithms are presented and fully explained to enable complete understanding of the methods and techniques demonstrated. As one reviewer noted, "The main strength of the proposed book is the link between theory and exemplar code of the algorithms." Essential background theory is carefully explained. This text gives students and researchers in image processing and computer vision a complete introduction to classic and state-of-the art methods in feature extraction together with practical guidance on their implementation. - The only text to concentrate on feature extraction with working implementation and worked through mathematical derivations and algorithmic methods - A thorough overview of available feature extraction methods including essential background theory, shape methods, texture and deep learning - Up to date coverage of interest point detection, feature extraction and description and image representation (including frequency domain and colour) - Good balance between providing a mathematical background and practical implementation - Detailed and explanatory of algorithms in MATLAB and Python

Product Details

ISBN-13:	9780128149775
Publisher:	Elsevier Science & Technology Books
Publication date:	11/17/2019
Sold by:	Barnes & Noble
Format:	eBook
Pages:	650
File size:	182 MB
Note:	This product may take a few minutes to download.

About the Author

Mark Nixon is the Professor in Computer Vision at the University of Southampton UK. His research interests are in image processing and computer vision. His team develops new techniques for static and moving shape extraction which have found application in biometrics and in medical image analysis. His team were early workers in automatic face recognition, later came to pioneer gait recognition and more recently joined the pioneers of ear biometrics. With Tieniu Tan and Rama Chellappa, their book Human ID based on Gait is part of the Springer Series on Biometrics and was published in 2005. He has chaired/ program chaired many conferences (BMVC 98, AVBPA 03, IEEE Face and Gesture FG06, ICPR 04, ICB 09, IEEE BTAS 2010) and given many invited talks. Dr. Nixon is a Fellow IET and a Fellow IAPR.Alberto Aguado is a principal algorithm researcher and developer at Foundry London were he works developing Image Processing, Computer Vision and rendering technologies for video production. Previously, he was head of research on animation technologies at Natural Motion. He developed image processing technologies for sport tracking at Sportradar. He worked as developer for Electronic Arts and for Black Rock Disney Game Studios. He gained academic experience as a Lecturer in the Centre for Vision, Speech and Signal Processing in the University of Surrey. He pursued a postdoctoral fellowship in Computer Vision at INRIA Rhône-Alpes (Marie Curie fellowship) and he received his PhD in Computer Vision /Image Processing from the University of Southampton.

Table of Contents

Preface 1. Introduction 1.1 Overview 1.2 Human and computer vision 1.3 The human vision system 1.3.1 The eye 1.3.2 The neural system 1.3.3 Processing 1.4 Computer vision systems 1.4.1 Cameras 1.4.2 Computer interfaces 1.5 Processing images 1.5.1 Processing 1.5.2 Hello Python, hello images! 1.5.3 Mathematical tools 1.5.4 Hello Matlab 1.6 Associated literature 1.6.1 Journals, magazines and conferences 1.6.2 Textbooks 1.6.3 The web 1.7 Conclusions References 2. Images, sampling and frequency domain processing 2.1 Overview 2.2 Image formation 2.3 The Fourier Transform 2.4 The sampling criterion 2.5 The discrete Fourier Transform 2.5.1 One-dimensional transform 2.5.2 Two-dimensional transform 2.6 Properties of the Fourier Transform 2.6.1 Shift invariance 2.6.2 Rotation 2.6.3 Frequency scaling 2.6.4 Superposition (linearity) 2.6.5 The importance of phase 2.7 Transforms other than Fourier 2.7.1 Discrete cosine transform 2.7.2 Discrete Hartley Transform 2.7.3 Introductory wavelets 2.7.3.1 Gabor Wavelet 2.7.3.2 Haar Wavelet 2.7.4 Other transforms 2.8 Applications using frequency domain properties 2.9 Further reading References 3. Image processing 3.1 Overview 3.2 Histograms 3.3 Point operators 3.3.1 Basic point operations 3.3.2 Histogram normalisation 3.3.3 Histogram equalisation 3.3.4 Thresholding 3.4 Group operations 3.4.1 Template convolution 3.4.2 Averaging operator 3.4.3 On different template size 3.4.4 Template convolution via the Fourier transform 3.4.5 Gaussian averaging operator 3.4.6 More on averaging 3.5 Other image processing operators 3.5.1 Median filter 3.5.2 Mode filter 3.5.3 Nonlocal means 3.5.4 Bilateral filtering 3.5.5 Anisotropic diffusion 3.5.6 Comparison of smoothing operators 3.5.7 Force field transform 3.5.8 Image ray transform 3.6 Mathematical morphology 3.6.1 Morphological operators 3.6.2 Grey level morphology 3.6.3 Grey level erosion and dilation 3.6.4 Minkowski operators 3.7 Further reading References 4. Low-level feature extraction (including edge detection) 4.1 Overview 4.2 Edge detection 4.2.1 First-order edge detection operators 4.2.1.1 Basic operators 4.2.1.2 Analysis of the basic operators 4.2.1.3 Prewitt edge detection operator 4.2.1.4 Sobel edge detection operator 4.2.1.5 The Canny edge detector 4.2.2 Second-order edge detection operators 4.2.2.1 Motivation 4.2.2.2 Basic operators: The Laplacian 4.2.2.3 The Marr–Hildreth operator 4.2.3 Other edge detection operators 4.2.4 Comparison of edge detection operators 4.2.5 Further reading on edge detection 4.3 Phase congruency 4.4 Localised feature extraction 4.4.1 Detecting image curvature (corner extraction) 4.4.1.1 Definition of curvature 4.4.1.2 Computing differences in edge direction 4.4.1.3 Measuring curvature by changes in intensity (differentiation) 4.4.1.4 Moravec and Harris detectors 4.4.1.5 Further reading on curvature 4.4.2 Feature point detection; region/patch analysis 4.4.2.1 Scale invariant feature transform 4.4.2.2 Speeded up robust features 4.4.2.3 FAST, ORB, FREAK, LOCKY and other keypoint detectors 4.4.2.4 Other techniques and performance issues 4.4.3 Saliency 4.4.3.1 Basic saliency 4.4.3.2 Context aware saliency 4.4.3.3 Other saliency operators 4.5 Describing image motion 4.5.1 Area-based approach 4.5.2 Differential approach 4.5.3 Recent developments: deep flow, epic flow and extensions 4.5.4 Analysis of optical flow 4.6 Further reading References 5. High-level feature extraction: fixed shape matching 5.1 Overview 5.2 Thresholding and subtraction 5.3 Template matching 5.3.1 Definition 5.3.2 Fourier transform implementation 5.3.3 Discussion of template matching 5.4 Feature extraction by low-level features 5.4.1 Appearance-based approaches 5.4.1.1 Object detection by templates 5.4.1.2 Object detection by combinations of parts 5.4.2 Distribution-based descriptors 5.4.2.1 Description by interest points (SIFT, SURF, BRIEF) 5.4.2.2 Characterising object appearance and shape 5.5 Hough transform 5.5.1 Overview 5.5.2 Lines 5.5.

What People are Saying About This

From the Publisher

Full coverage of the theory and implementation of feature extraction algorithms and techniques – revised and updated with the latest developments and new tutorials

Editorial Reviews

"…the book is well written and is easy to follow. In fact, the presentation order is the logical order of any actual computer vision system processing pipeline. The authors have done a great job grouping related topics together and touching upon recent techniques." —IAPR Newsletter, October 2013

"The mathematical element is presented in a non-mathematical way thus making the content more accessible…this edition is a very welcome addition to vision extraction." —IMA.org, August 2013

"All in all, I highly recommend this 600 pager as an introduction for students, and as a reference for practitioners. The latter audience will find an abundance of use references in each chapter…" —ComputingReviews.com, April 18, 2013

"After reviewing the human vision system, Nixon…and Aguardo…introduce signal processing theory for computer vision and current digital techniques for edge detection within an image, fixed shape matching, and deformable shape analysis. The undergraduate engineering textbook also explains the characterization of objects by boundary, region, and texture descriptions." —Reference and Research Book News, February 2013

From the Publisher