Data Management and Internet Computing for Image/Pattern Analysis

Data Management and Internet Computing for Image/Pattern Analysis

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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.
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Product details

  • Hardback | 367 pages
  • 154.9 x 231.1 x 17.8mm | 612.36g
  • Dordrecht, Netherlands
  • English
  • 2001 ed.
  • XVI, 367 p.
  • 0792374568
  • 9780792374565

Table of contents

Preface. Acknowledgements. 1. Overview. Part 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 Hybrid Image Coding. 6. Best Neighborhood Model for Block-Based Image Coding. 7. Impulse Noise Removal Algorithms For IAP. Part 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. Part 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. Index.
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