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Architectures

  • Jorge J. C. Sanz
  • Eric B. Hinkle
  • Anil K. Jain
Part of the Springer Series in Information Sciences book series (SSINF, volume 16)

Abstract

As mentioned previously, the algorithms presented in Sects. 2.1 and 2.2 are suitable for pipeline processing. They may be implemented in either commercially available short-pipelines for image processing (e.g., those marketed by Gould, Vicom, Grinnel, etc.), or completely new pipeline architectures composed of both general- and special-purpose hardware components. Pipeline architectures are well known for their ease of interconnection [Kent85]. In this chapter, we concentrate on a new pipeline implementation for Radon-based image processing. However, other image-oriented architectures have been explored. Particularly appealing are the MIMD/SIMD approaches considered in [Rice85] and [Silb85], M/SIMD machines [Ibra85], and systolic networks [Hara85], among many others.

Keywords

Projection Data Throughput Rate Pipeline Architecture Arithmetic Unit Contour Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1988

Authors and Affiliations

  • Jorge J. C. Sanz
    • 1
  • Eric B. Hinkle
    • 1
  • Anil K. Jain
    • 2
  1. 1.Computer Science DepartmentIBM Almaden Research CenterSan JoseUSA
  2. 2.Electrical and Computer Engineering DepartmentUniversity of California at DavisDavisUSA

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