To order this title, and for more information, click here Second Edition
By Mark Nixon, University of Southampton, UK Alberto S Aguado, University of Surrey, UK
Description * 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
* Companion website includes worksheets, links to free software, Matlab files and new demonstrations
Image processing and computer vision are currently hot topics with undergraduates and professionals alike. Feature Extraction and Image
Processing provides an essential guide to the implementation of image processing and computer vision techniques, explaining techniques
and fundamentals in a clear and concise manner. Readers can develop working techniques, with usable code provided throughout and working
Matlab and Mathcad files on the web.
Focusing on feature extraction while also covering issues and techniques such as image acquisition,
sampling theory, point operations and low-level feature extraction, the authors have a clear and coherent approach that will appeal to
a wide range of students and professionals.
The new edition includes:
* New coverage of curvature in low-level feature extraction (SIFT
and saliency) and features (phase congruency); geometric active contours; morphology; camera models
* Updated coverage of image smoothing
(anistropic diffusion); skeletonization; edge detection; curvature; shape descriptions (moments)
Audience
- 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)
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
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