Feature Extraction & Image Processing - 2nd Edition - ISBN: 9780123725387, 9780080556727

Feature Extraction & Image Processing

2nd Edition

Authors: Mark Nixon
eBook ISBN: 9780080556727
Imprint: Academic Press
Published Date: 10th December 2007
Page Count: 424
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Description

Whilst other books cover a broad range of topics, Feature Extraction and Image Processing takes one of the prime targets of applied computer vision, feature extraction, and uses it to provide an essential guide to the implementation of image processing and computer vision techniques.

Acting as both a source of reference and a student text, the book explains techniques and fundamentals in a clear and concise manner and helps readers to develop working techniques, with usable code provided throughout. The new edition is updated throughout in line with developments in the field, and is revised to focus on mathematical programming in Matlab.

Key Features

  • Essential reading for engineers and students working in this cutting edge field
  • Ideal module text and background reference for courses in image processing and computer vision

Readership

  • Undergraduates studying Electrical/Electronic Engineering, Computer Engineering, Computer Science, Software Engineering (to accompany specific modules or as supplementary / further reading)
  • Postgraduate students and researchers in Computer Vision and Image Processing (adoption potential for MSc / MEng courses)
  • Industry researchers and professionals using computer vision and image processing technology and wanting to get up to speed with social and industrial applications of the technology (eg. medical, industrial inspection, geographic studies)

Table of Contents

1 Introduction
1.1 Human and Computer Vision
1.2 The Human Vision System
1.2.1 The Eye
1.2.2 The Neural System
1.2.3 Processing
1.3 Computer Vision Systems
1.3.1 Cameras - review and update
1.3.2 Computer Interfaces - compress
1.3.3 Processing an Image
1.4 Mathematical Systems
1.4.1 Mathematical Tools
1.4.2 Hello Mathcad, Hello Images! - compress
1.4.3 Hello Matlab! - update
1.5 Associated Literature
1.5.1 Journals and Magazines - review and update
1.5.2 Textbooks - review and update
1.5.3 The Web - review and update
1.6 Chapter 1 References - review and update

2 Images, Sampling and Frequency Domain Processing
2.1 Image Formation (n.b. new Appendix material)
2.2 The Fourier Transform
2.3 The Sampling Criterion
2.4 The Discrete Fourier Transform (DFT)
2.4.1 One Dimensional Transform
2.4.2 Two Dimensional Transform
2.5 Other Properties of the Fourier Transform
2.5.1 Shift Invariance
2.5.2 Rotation
2.5.3 Frequency Scaling
2.5.4 Superposition (Linearity)
2.6 Transforms other than Fourier
2.6.1 Discrete Cosine Transform
2.6.2 Discrete Hartley Transform
2.6.3 Introductory Wavelets; The Gabor Wavelet
2.6.4 Other Transforms - review and update
2.7 Applications using Frequency Domain Properties
2.8 Further Reading
2.9 Chapter 2 References - review and update

3 Basic Image Processing Operations
3.1 Histograms
3.2 Point Operators
3.2.1 Basic Point Operations
3.2.2 Histogram Normalisation
3.2.3 Histogram Equalisation
3.2.4 Thresholding
3.3 Group Operations
3.3.1 Template Convolution
3.3.2 Averaging Operator
3.3.3 On Different Template Size
3.3.4 Gaussian Averaging Operator - extend link between frequency domain and performance
3.4 Other Statistical Operators
3.4.1 More on Averaging - review and inc Anisotropic Diffusion
3.4.2 Median Filter
3.4.3 Mode Filter
3.4.4 Comparison of Statistical Operators
3.5 Other Group Operators
3.5.1 Basic Morphology - review and update
3.5.2 Distance Transforms - review and update
3.6 Further Reading - review and update
3.7 Chapter 3 References - review and update


4 Low-Level Feature Extraction and Edge Detection
4.1 Low Level Features
4.2 First Order Edge Detection Operators
4.2.1 Basic Operators
4.2.2 Analysis of the Basic Operators
4.2.3 Prewitt Edge Detection Operator
4.2.4 Sobel Edge Detection Operator - extend link between frequency domain and performance
4.2.5 The Canny Edge Detector
4.3 Second Order Edge Detection Operators
4.3.1 Motivation
4.3.2 Basic Operators: The Laplacian
4.3.3 The Marr-Hildreth Operator - inc. DoG
4.4 Other Edge Detection Operators
4.4.1 Susan Operator - review and update
4.4.2 Spacek Operator - excise?
4.4.3 Petrou Operator
4.5 Comparison of Edge Detection Operators
4.6 Phase Comgruency - review and update
4.7 Detecting Image Curvature
4.7.1 Computing Differences in Edge Direction
4.7.2 Approximation to a Continuous Curve - excise
4.7.3 Measuring Curvature by Changes in Intensity
4.7.4 Autocorrelation as a Measure of Curvature
4.7.5 Current corner detectors, inc SIFT - review and update
4.8 Describing Image Motion: Optical Flow
4.9 Further Reading
4.10 Chapter 4 References - review and update

5 Feature Extraction by Shape Matching
5.1 Overview - make consistent with Chaps 1-4
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 Hough Transform (HT)
5.4.1 Overview
5.4.2 Lines - show more on noise
5.4.3 HT for Circles - show more on occlusion
5.4.4 HT for Ellipses
5.4.5 Parameter Space Decomposition
5.5 Generalised Hough Transform (GHT)
5.5.1 Formal Definition of the GHT
5.5.2 Polar definition
5.5.3 The GHT Technique
5.5.4 Invariant GHT
5.6 Other Extensions to the HT
5.7 Further Reading
5.8 Chapter 5 References - review and update

6 Flexible Shape Extraction (Snakes and Other Techniques)
6.1 Overview - make consistent with Chaps 1-4
6.2 Deformable Templates
6.3 Active Contours (Snakes)
6.3.1 Basics
6.3.2 The Greedy Algorithm for Snakes
6.3.3 Complete (Kass) Snake Implementation
6.3.4 Other Snake Approaches
6.3.5 Further Snake Developments - review and update, e.g. GVF etc.
6.4 Discrete Symmetry Operator
6.5 Curvature Scale Space - review and update
6.6 Flexible Shape Models - extend (n.b. new appendix on PCA)
6.7 Further Reading
6.8 Chapter 6 References - review and update

7 Object Description
7.1 Overview - make consistent with Chaps 1-4
7.2 Boundary Descriptions
7.2.1 Boundary and Region
7.2.2 Chain Codes
7.2.3 Fourier Descriptors
7.3 Region Descriptors
7.3.1 Basic Region Descriptors
7.3.2 Moments - extend orthogonal moments, include Fourier moments and reconstruction
7.4 Further Reading
7.5 Chapter 7 References - review and update

8 Introduction to Texture Description, Segmentation and Classification
8.1 Overview - make consistent with Chaps 1-4
8.2 What is Texture?
8.3 Texture Description
8.3.1 Performance Requirements
8.3.2 Structural Approaches
8.3.3 Statistical Approaches
8.3.4 Combination Approaches
8.4 Classification
8.4.1 The k-Nearest Neighbour Rule
8.4.2 Other Classification Approaches - review and update
8.5 Segmentation
8.6 Further Reading
8.7 Chapter 8 References - review and update

9 Appendices
9.1 Appendix 1 Image Formation
9.1.1 Camera Models - review and update
9.1.2 Homogeneous Co-ordinate System - review and update
9.1.3 Appendix 1 References - review and update
9.2 Appendix 2 Least Squares Analysis
9.2.1 Appendix 2.1 The Least Squares Criterion
9.2.2 Appendix 2.2 Curve Fitting by Least Squares
9.3 Appendix 3 Example Mathcad Worksheet - excise
9.3 Appendix 3 Principal Components Analysis
9.4 Appendix 4 Example Matlab Worksheet - excise

Index - review and update

Details

No. of pages:
424
Language:
English
Copyright:
© Academic Press 2007
Published:
Imprint:
Academic Press
eBook ISBN:
9780080556727

About the Author

Mark Nixon

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.

Affiliations and Expertise

Professor of Electronics and Computer Science at the University of Southampton, UK.