Image Processing, Analysis and Machine Vision
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1 Image Processing, Analysis and Machine Vision Milan Sonka PhD University of Iowa Iowa City, USA Vaclav Hlavac PhD Czech Technical University Prague, Czech Republic and Roger Boyle DPhil, MBCS, CEng University of Leeds Leeds, UK CHAPMAN & HALL COMPUTING London Glasgow Weinheim New York Tokyo Melbourne Madras
2 Contents Colour plates appear between pages 268 and 269 List of Algorithms List of symbols and abbreviations Preface xi xiii xv 1 Introduction 1 Th«: digitized image and its properties 2.1 Basic concepts Image functions The Dirac distribution and convolution The Fourier transform Images as a stochastic process Images as linear systems 2.2 Image digitization Sampling Quantization Colour images 2.3 Digital image properties Metric and topological properties of digital images Histograms Visual perception of the image Image quality Noise in images Dat ;a structures for image analysis Levels of image data representation Traditional image data structures Matrices Chains Topological data structures Relational structures 3.3 Hierarchical data structures V
3 VI Contents Pyramids Quadtrees 4 Image pre-processing 4.1 Pixel brightness transformations Position-dependent brightness correction Grey scale transformation 4.2 Geometric transformations Pixel co-ordinate transformations Brightness interpolation 4.3 Local pre-processing Image smoothing Edge detectors Zero crossings of the second derivative Scale in image processing Canny edge detection Edges in multispectral images Other local pre-processing operators Adaptive neighbourhood pre-processing 4.4 Image restoration Image restoration as inverse convolution of the whole image Degradations that are easy to restore Inverse filtration Wiener filtration 5 Segmentation 5.1 Thresholding Threshold detection methods Multispectral thresholding Thresholding in hierarchical data structures 5.2 Edge-based segmentation Edge image thresholding Edge relaxation Border tracing Edge following as graph searching Edge following as dynamic programming Hough transforms Border detection using border location information Region construction from borders 5.3 Region growing segmentation Region merging Region splitting Splitting and merging
4 Contents Vll 5.4 Matching Matching criteria Control strategies of matching 178 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 a segment sequence; polygonal representation B-spline representation Other contour-based shape description approaches Shape invariants Region-based shape representation and description Simple scalar region descriptors Moments Convex hull Graph representation based on region skeleton Region decomposition Region neighbourhood graphs 241 Object recognition Knowledge representation Statistical pattern recognition Classification principles Classifier setting Classifier learning Cluster analysis Neural nets Feed-forward nets Kohonen feature maps Hybrid neural nets Hopfield neural nets Syntactic pattern recognition Grammars and languages Syntactic analysis, syntactic classifier Syntactic classifier learning, grammar inference Recognition as graph matching Isomorphism of graphs and subgraphs Similarity of graphs Optimization techniques in recognition Genetic algorithms, 301
5 VUl Contents Simulated annealing 8 Image understanding 8.1 Image understanding control strategies Parallel and serial processing control Hierarchical control Bottom-up control strategies Model-based control strategies Combined control strategies Non-hierarchical control 8.2 Active contour models - snakes 8.3 Pattern recognition methods in image understanding Contextual image classification 8.4 Scene labelling and constraint propagation Discrete relaxation Probabilistic relaxation Searching interpretation trees 8.5 Semantic image segmentation and understanding Semantic region growing Semantic genetic segmentation and interpretation 9 3D Vision 9.1 Strategy Marr's theory Modelling strategies 9.2 Line labelling 9.3 Shape from X Shape from stereo Shape from shading Shape from motion Shape from texture 9.4 Approaches to the recognition of 3D objects Goad's algorithm Features for model-based recognition of curved objects 9.5 Depth map technologies 9.6 Summary 10 Mathematical morphology 10.1 Basic principles and morphological transformations Morphological transformations Dilation Erosion Opening and closing 10.2 Skeleton and other topological processing
6 Contents ix Homotopic transformations Skeleton Thinning and thickening Conditional dilation and ultimate erosion Linear discrete image transforms Basic theory The Fourier transform Hadamard transform Discrete cosine transform Other discrete image transforms Applications of discrete image transforms Image data compression Image data properties Discrete image transforms in image data compression Predictive compression methods Vector quantization Pyramid compression methods Comparison of compression methods Other techniques Texture Statistical texture description Methods based on spatial frequencies Co-occurrence matrices Edge frequency Primitive length (run length) Other statistical methods of texture description Syntactic texture description methods Shape chain grammars Graph grammars Primitive grouping in hierarchical textures Hybrid texture description methods Texture recognition method applications Motion analysis Differential motion analysis methods Optical flow Optical flow computation Global and local optical flow estimation Optical flow in motion analysis Motion analysis based on detection of interest points Detection of interest points 525
7 x Contents Correspondence of interest points 525 Index 543
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