Combinatorial Image Analysis

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17th International Workshop, IWCIA 2015, Kolkata, India, November 24-27,2015. Proceedings, Lecture Notes in Computer Science 9448 – Image Processing, Computer Vision, Pattern Recognition, and Graphics

ISBN: 3319261444
ISBN 13: 9783319261447
Herausgeber: Reneta P Barneva/Bhargab B Bhattacharya/Valentin E Brimkov
Verlag: Springer Verlag GmbH
Umfang: xiii, 363 S., 177 farbige Illustr., 363 p. 177 illus. in color.
Erscheinungsdatum: 04.11.2015
Auflage: 1/2015
Produktform: Kartoniert
Einband: Kartoniert

This volume constitutes the refereed proceedings of the 17th International Workshop on Combinatorial Image Analysis, IWCIA 2015, held in Kolkata, India, in November 2015. The 24 revised full papers and 2 invited papers presented were carefully reviewed and selected from numerous submissions. The workshop provides theoretical foundations and methods for solving problems from various areas of human practice. In contrast to traditional approaches to image analysis which implement continuous models, float arithmetic and rounding, combinatorial image analysis features discrete modelsusing integer arithmetic. The developed algorithms are based on studying combinatorial properties of classes of digital images, and often appear to be more efficient and accurate than those based on continuous models.

Artikelnummer: 8657845 Kategorie:

Beschreibung

This volume constitutes the refereed proceedings of the 17th International Workshop on Combinatorial Image Analysis, IWCIA 2015, held in November 2015. The 25 revised full papers and 2 invited papers presented were carefully reviewed and selected from numerous submissions. The workshop provides theoretical foundations and methods for solving problems from various areas of human practice. In contrast to traditional approaches to image analysis which implement continuous models, float arithmetic and rounding, combinatorial image analysis features discrete modelsusing integer arithmetic. The developed algorithms are based on studying combinatorial properties of classes of digital images, and often appear to be more efficient and accurate than those based on continuous models.

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