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Image Processing, Analysis, and Machine Vision

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ISBN-10: 049508252X

ISBN-13: 9780495082521

Edition: 3rd 2008

Authors: Milan Sonka, Vaclav Hlavac, Roger Boyle

List price: $304.95
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This robust text provides deep and wide coverage of the full range of topics encountered in the field of image processing and machine vision. As a result, it can serve undergraduates, graduates, researchers, and professionals looking for a readable reference. The book's encyclopedic coverage of topics is wide, and it can be used in more than one course (both image processing and machine vision classes). In addition, while advanced mathematics is not needed to understand basic concepts (making this a good choice for undergraduates), rigorous mathematical coverage is included for more advanced readers. It is also distinguished by its easy-to-understand algorithm descriptions of difficult…    
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Book details

List price: $304.95
Edition: 3rd
Copyright year: 2008
Publisher: Course Technology
Publication date: 3/19/2007
Binding: Hardcover
Pages: 829
Size: 8.25" wide x 9.50" long x 1.50" tall
Weight: 3.278
Language: English

Milan Sonka is Professor of Electrical and Computer Engineering at the University of Iowa. His research interests include medical image analysis, computer-aided diagnosis, and machine vision.

Vaclav Hlavac is Professor of Cybernetics at the Czech Technical University, Prague. his research interests are knowledge based image analysis, 3D model-based vision and relations between statistical and structural pattern recognition.

Introduction Motivation
Why is Computer Vision Difficult?
Image Representation and Image Analysis Tasks
The Image, its Representations and Properties Image Representations, a Few Concepts
Image Digitization
Digital Image Properties
Metric and Topological Properties of Digital Images
Visual Perception of the Image
Image Quality
Noise in Images
Color Images
Physics of Color
Color Perceived by Humans
Color Spaces
Palette Images
Color Constancy
Cameras: An Overview
Photosensitive Sensors
A Monochromatic Camera
A Color Camera
The Image, its Mathematical and Physical Background Overview
The Dirac Distribution and Convolution
Linear Integral Transforms
Images as Linear Systems
Introduction to Linear Integral Transforms
1D Fourier Transform
2D Fourier Transform
Sampling and the Shannon Constraint
Discrete Cosine Transform
Wavelet Transform
Singular Value Decomposition
Principle Component Analysis
Other Orthogonal Image Transforms
Images as Stochastic Processes
Image Formation Physics
Images as Radiometric Measurements
Image Capture and Geometric Optics
Lens Aberrations and Radial Distortion
Image Capture from a Radiometric Point of View
Surface Reflectance
Data Structures for Image Analysis Levels of Image Data Representation
Traditional Image Data Structures
Topological Data Structures
Relational Structures
Hierarchical Data Structures
Other Pyramidal Structures
Image Pre-Processing Pixel Brightness Transformations
Position-Dependent Brightness Correction
Gray-Scale Transformation
Geometric Transformations
Pixel Co-ordinate Transformations
Brightness Interpolation
Local Pre-Processing
Image Smoothing
Edge Detectors
Zero-Crossings of the Second Derivative
Scale in Image Processing
Canny Edge Detection
Parametric Edge Models
Edges in Multi-Spectral Images
Local Detection by Local Pre-Processing Operators
Detection of Corners (Interest Points)
Detection of Maximally Stable Extremal Regions
Image Restoration
Degradations That are Easy to Restore
Inverse Filtration
Wiener Filtration
Segmentation I Thresholding
Threshold Detection Methods
Optimal Thresholding
Multi-Spectral Thresholding
Edge Based Segmentation
Edge Image Thresholding
Edge Relaxation
Border Tracing
Border Detection as graph Searching
Border Detection as Dynamic Programming
Hough Transforms
Border Detection Using Border Location Information
Region Construction from Borders
Region Based Segmentation
Region Merging
Region Splitting
Splitting and Merging
Watershed Segmentation
Region Growing Post-Processing
Matching Criteria
Control Strategies of Matching
Evaluation Issues in Segmentation
Supervised Evaluation
Unsupervised Evaluation
Segmentation II Mean Shift Segmentation
Active Contour Models ? Snakes
Traditional Snakes and Balloons
Gradient Vector Flow Snakes
Geometric Deformable Models ? Level Sets and Geodesic Active Contours
Fuzzy Connectivity
Towards 3D Graph-Based Image Segmentation
Simultaneous Detection of Border Pairs
Sub-optimal Surface Detection
Graph Cut Segmentation
Optimal Single and Multiple Surface Segmentation
Shape Representation and Description Region Identification
Contour-Based Shape Representation and Description
Chain Codes
Simple Geometric Border Representation
Fourier Transforms of Boundaries
Boundary Description using Segment Sequences
B-Spline Representation
Other Contour-Based Shape Description Approaches
Shape Invariants
Region-Based Shape Representation and Description
Simple Scalar Region Descriptors