Mathematical Morphology and Its Applications to Image and Signal Processing (Computational Imaging and Vision, 5)

ISBN 10: 0792397339 / ISBN 13: 9780792397335

Edité par Springer, 1996

A propos de cet article

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Détails bibliographiques

• Titre: Mathematical Morphology and Its Applications to Image and Signal Processing (Computational Imaging and Vision, 5)
• Éditeur: Springer
• Date de parution: 1996
• Langue: anglais
• ISBN à 10 chiffres: 0792397339
• ISBN à 13 chiffres: 9780792397335
• Reliure: hardcover
• État de l'article: Good

A propos de ce titre

Synopsis

Mathematical morphology (MM) is a powerful methodology for the quantitative analysis of geometrical structures. It consists of a broad and coherent collection of theoretical concepts, nonlinear signal operators, and algorithms aiming at extracting, from images or other geometrical objects, information related to their shape and size. Its mathematical origins stem from set theory, lattice algebra, and integral and stochastic geometry.
MM was initiated in the late 1960s by G. Matheron and J. Serra at the Fontainebleau School of Mines in France. Originally it was applied to analyzing images from geological or biological specimens. However, its rich theoretical framework, algorithmic efficiency, easy implementability on special hardware, and suitability for many shape- oriented problems have propelled its widespread diffusion and adoption by many academic and industry groups in many countries as one among the dominant image analysis methodologies.
The purpose of Mathematical Morphology and its Applications to Image and Signal Processing is to provide the image analysis community with a sampling from the current developments in the theoretical (deterministic and stochastic) and computational aspects of MM and its applications to image and signal processing. The book consists of the papers presented at the ISMM'96 grouped into the following themes:

• Theory
• Connectivity
• Filtering
• Nonlinear System Related to Morphology
• Algorithms/Architectures
• Granulometries, Texture
• Segmentation
• Image Sequence Analysis
• Learning
• Document Analysis
• Applications