2nd Edition

Shape Classification and Analysis Theory and Practice, Second Edition

    692 Pages 388 B/W Illustrations
    by CRC Press

    Because the properties of objects are largely determined by their geometric features, shape analysis and classification are essential to almost every applied scientific and technological area. A detailed understanding of the geometrical features of real-world entities (e.g., molecules, organs, materials and components) can provide important clues about their origin and function. When properly and carefully applied, shape analysis offers an exceedingly rich potential to yield useful applications in diverse areas ranging from material sciences to biology and neuroscience.

    Get Access to the Authors’ Own Cutting-Edge Open-Source Software Projects—and Then Actually Contribute to Them Yourself!

    The authors of Shape Analysis and Classification: Theory and Practice, Second Edition have improved the bestselling first edition by updating the tremendous progress in the field. This exceptionally accessible book presents the most advanced imaging techniques used for analyzing general biological shapes, such as those of cells, tissues, organs, and organisms. It implements numerous corrections and improvements—many of which were suggested by readers of the first edition—to optimize understanding and create what can truly be called an interactive learning experience.

    New Material in This Second Edition Addresses

    • Graph and complex networks
    • Dimensionality reduction
    • Structural pattern recognition
    • Shape representation using graphs

    Graphically reformulated, this edition updates equations, figures, and references, as well as slides that will be useful in related courses and general discussion. Like the popular first edition, this text is applicable to many fields and certain to become a favored addition to any library.

    Visit http://www.vision.ime.usp.br/~cesar/shape/ for Useful Software, Databases, and Videos

    INTRODUCTION

    INTRODUCTION TO SHAPE ANALYSIS

    CASE STUDIES

    COMPUTATIONAL SHAPE ANALYSIS

    ADDITIONAL MATERIAL

    ORGANIZATION OF THE BOOK

     

    BASIC MATHEMATICAL CONCEPTS

    BASIC CONCEPTS

    LINEAR ALGEBRA

    DIFFERENTIAL GEOMETRY

    MULTIVARIATE CALCULUS

    CONVOLUTION AND CORRELATION

    PROBABILITY AND STATISTICS

    FOURIER ANALYSIS

    GRAPHS AND COMPLEX NETWORKS

     

    SHAPE ACQUISITION AND PROCESSING

    IMAGE REPRESENTATION

    IMAGE PROCESSING AND FILTERING

    IMAGE SEGMENTATION: EDGE DETECTION

    IMAGE SEGMENTATION: ADDITIONAL ALGORITHMS

    BINARY MATHEMATICAL MORPHOLOGY

    FURTHER IMAGE PROCESSING

    REFERENCES

     

    SHAPE CONCEPTS

    INTRODUCTION TO TWO-DIMENSIONAL SHAPES

    CONTINUOUS TWO-DIMENSIONAL SHAPES

    PLANAR SHAPE TRANSFORMATIONS

    CHARACTERIZING 2D SHAPES IN TERMS OF FEATURES

    CLASSIFYING 2D SHAPES

    REPRESENTING 2D SHAPES

    SHAPE OPERATIONS

    SHAPE METRICS

    MORPHIC TRANSFORMATIONS

     

    SHAPE REPRESENTATION

    INTRODUCTION

    PARAMETRIC CONTOURS

    SETS OF CONTOUR POINTS

    CURVE APPROXIMATIONS

    DIGITAL STRAIGHT LINES

    HOUGH TRANSFORMS

    EXACT DILATIONS

    DISTANCE TRANSFORMS

    EXACT DISTANCE TRANSFORM THROUGH EXACT DILATIONS

    VORONOI TESSELLATIONS

    SCALE-SPACE SKELETONIZATION

    BOUNDING REGIONS

     

    SHAPE CHARACTERIZATION

    STATISTICS FOR SHAPE DESCRIPTORS

    SOME GENERAL DESCRIPTORS

    FRACTAL GEOMETRY AND COMPLEXITY DESCRIPTORS

    CURVATURE

    FOURIER DESCRIPTORS

     

    MULTISCALE SHAPE CHARACTERIZATION

    MULTISCALE TRANSFORMS

    FOURIER-BASED MULTISCALE CURVATURE

    WAVELET-BASED MULTISCALE CONTOUR ANALYSIS

    MULTISCALE ENERGIES

     

    SHAPE RECOGNITION

    INTRODUCTION TO SHAPE CLASSIFICATION

    SUPERVISED PATTERN CLASSIFICATION

    UNSUPERVISED CLASSIFICATION AND CLUSTERING

    A CASE STUDY: LEAVES CLASSIFICATION

    EVALUATING CLASSIFICATION METHODS

     

    STRUCTURAL SHAPE RECOGNITION

    INTRODUCTION

    SYNTACTIC PATTERN RECOGNITION

    REGION DECOMPOSITION

    GRAPH MODELS

    SPATIAL RELATIONS

    GRAPH MATCHING

    CASE STUDY: INTERACTIVE IMAGE SEGMENTATION

    COMPLEX NETWORKS FOR IMAGE AND SHAPE ANALYSIS

     

    EPILOGUE

    FUTURE TRENDS IN SHAPE ANALYSIS AND CLASSIFICATION

    Biography

    Luciano da Fontoura Costa is a full professor at the Instituto de Física de São Carlos, University of Sao Paulo, Brazil, and head of the Cybernetic Vision Research Group and the Multidisciplinary Computing Group. He holds BScs in Electronic Engineering and Computer Science, as well as a MSc in Applied Physics and a PhD in Electronic Engineering. Roberto M. Cesar Jr. holds a BSc in Computer Science, a M.Sc. in Electrical Engineering, and a Ph.D in Computational Physics. He is currently a lecturer at the University of Sao Paulo.