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Data Management and Internet Computing for Image/Pattern Analysis focuses on the data management issues and Internet computing aspect of image processing and pattern recognition research. The book presents a comprehensive overview of the state of the art, providing detailed case studies that emphasize how image and pattern (IAP) data are distributed and exchanged on sequential and parallel machines, and how the data communication patterns in low- and higher-level IAP computing differ from general numerical computation, what problems they cause and what opportunities they provide. The studies also describe how the images and matrices should be stored, accessed and distributed on different types of machines connected to the Internet, and how Internet resource sharing and data transmission change traditional IAP computing.
Data Management and Internet Computing for Image/Pattern Analysis is divided into three parts: the first part describes several software approaches to IAP computing, citing several representative data communication patterns and related algorithms; the second part introduces hardware and Internet resource sharing in which a wide range of computer architectures are described and memory management issues are discussed; and the third part presents applications ranging from image coding, restoration and progressive transmission.
Data Management and Internet Computing for Image/Pattern Analysis is an excellent reference for researchers and may be used as a text for advanced courses in image processing and pattern recognition.
Table des matières
Overview.- 1.1 PRIP and IAP.- 1.2 IAP Dada Management in PRIP Computations.- 1.3 Internet Computing for PRIP.- 1.4 PRIP Applications.- 1.5 Book Perspective 6 REFERENCES.- I Software Management: Models & Algorithms.- 2 Issues of Data Management.- 3 Typical PRIP Algorithms and IAP Data Management.- 4 Neural Evolution MOdel for Gray Level Image REstoration.- 5 Partial Fractal Model for Hybird Image Coding.- 6 Best Neighborhood Madle for Block-based Image Coding.- 7 Impulse Noise Removal Algorithms for IAP.- II Hardware Management: Architectures & Resource Sharing.- 8 Internet Resource Sharing.- 9 Parallel Processing for Image Restoration.- 10 Image Storage Management on Parallel Computers.- 11 Data Management for Sequential Computer Systems.- 12 Permutation Routing for Interconnection Network.- III Typical Examples: Applications & Implementations.- 13 Compression Coding for IAP Data.- 14 Reduction of Blocking Effects and Removal of Impulse Noise.- 15 Image Restoration from Internet Transmission Corruption.- 16 Encryption Coding for IAP Data.
Résumé
Data Management and Internet Computing for Image/Pattern Analysis focuses on the data management issues and Internet computing aspect of image processing and pattern recognition research. The book presents a comprehensive overview of the state of the art, providing detailed case studies that emphasize how image and pattern (IAP) data are distributed and exchanged on sequential and parallel machines, and how the data communication patterns in low- and higher-level IAP computing differ from general numerical computation, what problems they cause and what opportunities they provide. The studies also describe how the images and matrices should be stored, accessed and distributed on different types of machines connected to the Internet, and how Internet resource sharing and data transmission change traditional IAP computing.
Data Management and Internet Computing for Image/Pattern Analysis is divided into three parts: the first part describes several software approaches to IAP computing, citing several representative data communication patterns and related algorithms; the second part introduces hardware and Internet resource sharing in which a wide range of computer architectures are described and memory management issues are discussed; and the third part presents applications ranging from image coding, restoration and progressive transmission.
Data Management and Internet Computing for Image/Pattern Analysis is an excellent reference for researchers and may be used as a text for advanced courses in image processing and pattern recognition.
