Image Processing, Analysis & and Machine Vision - A MATLAB Companion 1st Edición
de Tomas Svoboda (Author), Jan Kybic (Author), Vaclav Hlavac (Author) - THOMSON - 2008 - 256 oldal This book is a companion book to the comprehensive text entitled Image Processing, Analysis, and Machine Vision by M. Sonka, V. Hlavac, and R. Boyle. This workbook provides additional material for readers of Sonka and is similarly structured. Written for students, teachers and practitioners to acquire practical understanding in a hands on fashion, this book provides the reader with short-answer questions, problems and selected algorithms from the main text using MATLAB in levels of varying difficulty. These resources can be used as extra practice for students to reinforce the material studied within the main text or can be useful as test materials for teachers. Sonka, Milan; Hlavac, Vaclav; Boyle, Roger - Image Processing, Analysis, and Machine Vision - - THOMSON - 2008 - 829 oldal Product Information
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 concepts, and a wealth of carefully selected problems and examples.
1. IntroductionMotivation / Why is Computer Vision Difficult? / Image Representation and Image Analysis Tasks / Summary / References2. The Image, its Representations and PropertiesImage Representations, a Few Concepts / Image Digitization / Sampling / Quantization / Digital Image Properties / Metric and Topological Properties of Digital Images / Histograms / Entropy / 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 / Summary / References3. The Image, its Mathematical and Physical BackgroundOverview / Linearity / 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 / Eigen-Analysis / 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 / Summary / References4. Data Structures for Image AnalysisLevels of Image Data Representation / Traditional Image Data Structures / Matrices / Chains / Topological Data Structures / Relational Structures / Hierarchical Data Structures / Pyramids / Quadtrees / Other Pyramidal Structures / Summary / References5. Image Pre-ProcessingPixel 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 / Summary / References6. Segmentation IThresholding / 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 / Matching Criteria / Control Strategies of Matching / Evaluation Issues in Segmentation / Supervised Evaluation / Unsupervised Evaluation / Summary / References7. Segmentation IIMean Shift Segmentation / Active Contour Models - Snakes / Traditional Snakes and Balloons / Extensions / 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 / Summary / References8. 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 / Momen
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