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Shape Analysis and Classification: Theory and Practice (Image Processing Series) [Hardcover]

Luciano Da Fontoura Costa (Author), Roberto Marcondes Cesar Jr. (Author)
5.0 out of 5 stars  See all reviews (1 customer review)


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There is a newer edition of this item:
Shape Classification and Analysis: Theory and Practice, Second Edition (Image Processing Series) Shape Classification and Analysis: Theory and Practice, Second Edition (Image Processing Series)
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Book Description

0849334934 978-0849334931 December 20, 2000 1
Advances in shape analysis impact a wide range of disciplines, from mathematics and engineering to medicine, archeology, and art. Anyone just entering the field, however, may find the few existing books on shape analysis too specific or advanced, and for students interested in the specific problem of shape recognition and characterization, traditional books on computer vision are too general.

Shape Analysis and Classification: Theory and Practice offers an integrated and conceptual introduction to this dynamic field and its myriad applications. Beginning with the basic mathematical concepts, it deals with shape analysis, from image capture to pattern classification, and presents many of the most advanced and powerful techniques used in practice. The authors explore the relevant aspects of both shape characterization and recognition, and give special attention to practical issues, such as guidelines for implementation, validation, and assessment.

Shape Analysis and Classification provides a rich resource for the computational characterization and classification of general shapes, from characters to biological entities. Both students and researchers can directly use its state-of-the-art concepts and techniques to solve their own problems involving the characterization and classification of visual shapes.

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Editorial Reviews

From the Author

There has been considerable research in computer science directed on the problems associated with detecting, analyzing, and categorizing visual shape information. The authors of this book have done an admirable job of discussing the diverse approaches utilized in this area. They also provide a 170-page chapter (chapter 2) that covers a vast range of useful mathematical concepts in shape analysis (e.g., propositional logic, linear algebra, differential geometry, convolution and correlation, probability theory, and Fourier analysis). Chapter 3 deals with digital image processing. Chapter 4 covers the main concepts in 2D shape analysis, and in Chapter 5 the authors consider computer representations of shape information. Chapters 6 and 7 are focused on techniques of shape characterization. In Chapter 8 the authors cover the major methods for classifying and recognizing shapes. This comprehensive book is clearly written and is a valuable resource for scientists interested in visual shape analysis.

Roberto Marcondes Caesar Junior


Product Details

  • Hardcover: 680 pages
  • Publisher: CRC Press; 1 edition (December 20, 2000)
  • Language: English
  • ISBN-10: 0849334934
  • ISBN-13: 978-0849334931
  • Product Dimensions: 9.3 x 6.3 x 1.7 inches
  • Shipping Weight: 2.8 pounds
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Best Sellers Rank: #2,457,861 in Books (See Top 100 in Books)

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6 of 6 people found the following review helpful:
5.0 out of 5 stars Basic Image processing book for beginners, January 5, 2002
This review is from: Shape Analysis and Classification: Theory and Practice (Image Processing Series) (Hardcover)
The book is mostly discussed about shape representation that used for image processing or computer vision. It has good explanation regarding basic mathematical concepts with very simple numerical example for each concept. The explanation about image descriptors is clear for self study. The book however is not recommended for people who has knowledge already about the basic image processing and computer vision because not so much new things can be found in this book that are not explained in other similar books.
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Inside This Book (learn more)
First Sentence:
This chapter presents an introduction to computational shape analysis. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
dominant point detection, vertical skeleton, horizontal skeleton, shape analysis and classification, digital curvature estimation, respective feature vectors, maxima crests, high curvature points, shape representation techniques, morphic transformations, analyzing scale, shape analysis tools, image dilation, comer detection, maxima lines, parametric contours, centroid size, special orthogonal transformation, multiscale transform, filtered function, starting pixel, shape characterization, image erosion, contour segmentation, contour pixel
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Elsevier Science, Fractal Analysis of Cell Images, Journal of Neuroscience Methods, Review of Scientific Instruments, American Institute of Physics, Multi-Escala de Formas Bidimensionais, Perimeter Figure, Ball Radius, Ingrid Daubechies, Laplacian of Gaussian
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