Description
The brand new edition of IMAGE PROCESSING, ANALYSIS, AND MACHINE VISION is a robust text providing 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 concepts, and a wealth of carefully selected problems and examples.教学资源：PPT、教师手册。
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Chapter
2 The image,its representations and properties
2.1 Image representations,a few concepts
2.3 Digital image properties
3 The image,its mathematical and physical background
3.2 Linear integral transforms
3.3 Images as stochastic processes
3.4 Image formation physics
4 Data structures for image analysis
4.1 Levels of image data representation
4.2 Traditional image data structures
4.3 Hierarchical data structures
5.1 Pixel brightness transformations
5.2 Geometric transformations
6.2 Edge-based segmentation
6.3 Region-based segmentation
6.5 Evaluation issues in segmentation
7.1 Mean shift segmentation
7.2 Active contour models—snakes
7.3 Geometric deformable models—level sets and geodesic active contours
7.5 Towards 3D graph-based image segmentation
7.6 Graph cut segmentation
7.7 Optimal single and multiple surface segmentation—LOGISMOS
8 Shape representation and description
8.1 Region identification
8.2 Contour-based shape representation and description
8.3 Region-based shape representation and description
9.1 Knowledge representation
9.2 Statistical pattern recognition
9.4 Syntactic pattern recognition
9.5 Recognition as graph matching
9.6 Optimization techniques in recognition
9.8 Boosting in pattern recognition
10.1 Image understanding control strategies
10.2 SIFT:Scale invariant feature transform
10.3 RANSAC:Fitting via random sample consensus
10.4 Point distribution models
10.5 Active appearance models
10.6 Pattern recognition methods in image understanding
10.7 Boosted cascades of classifiers
10.8 Image understanding using random forests
10.9 Scene labeling and constraint propagation
10.10 Semantic image segmentation and understanding
10.11 Hidden Markov models
10.12 Markov random fields
10.13 Gaussian mixture models and expectation–maximization
11 3D geometry,correspondence,3D from intensities
11.2 Basics of projective geometry
11.3 A single perspective camera
11.4 Scene reconstruction from multiple views
11.5 Two cameras,stereopsis
11.6 Three cameras and trifocal tensor
11.7 3D information from radiometric measurements
12.3 2D view-based representations of a 3D scene
12.4 3D reconstruction from an unorganized set of 2D views,and Structure from Motion
12.5 Reconstructing scene geometry
13 Mathematical morphology
13.1 Basic morphological concepts
13.2 Four morphological principles
13.3 Binary dilation and erosion
13.4 Gray-scale dilation and erosion
13.5 Skeletons and object marking
13.7 Morphological segmentation and watersheds
14 Image data compression
14.1 Image data properties
14.2 Discrete image transforms in image data compression
14.3 Predictive compression methods
14.5 Hierarchical and progressive compression methods
14.6 Comparison of compression methods
14.9 JPEG and MPEG image compression
15.1 Statistical texture description
15.2 Syntactic texture description methods
15.3 Hybrid texture description methods
15.4 Texture recognition method applications
16.1 Differential motion analysis methods
16.3 Analysis based on correspondence of interest points
16.4 Detection of specific motion patterns
16.6 Motion models to aid tracking
Image Processing, Analysis, and Machine Vision
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