5 July 1995 Analysis of transputer processor networks for image processing
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This paper presents a performance analysis of the transputer T805 processor networks for implementing low-level image processing algorithms. The influence of communication on the performance of transputer networks is analyzed. The network implementation constraints for using transputer networks for image processing are discussed. The paper also presents the results of implementing a texture feature extraction algorithm, called the Spatial Gray Level Dependence Method (SGLDM), on transputer networks and the results are studied with respect to communication. This algorithm is used for image texture analysis. The algorithm estimates the second-order joint conditional probabilities, of transition from one gray level to another, between two pixels that are at a specific distance and at a specific angle to the horizontal axis. Many statistical texture features can be derived from the estimated co- occurrence matrices. The transputer networks are configured as hypercubes which provides embedded tree, ring, and mesh topologies. The algorithm is implemented on different transputer hypercube configurations with tree topologies mapped onto them. The communication overheads in parallel transputer networks have a major influence on the optimal number of processors that can be used in an application, and on the maximum speedup that can be achieved. By resolving the communication issues transputer based real-time image processing applications can be developed.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Vidya B. Manian, Vidya B. Manian, Ramon E. Vasquez, Ramon E. Vasquez, } "Analysis of transputer processor networks for image processing", Proc. SPIE 2484, Signal Processing, Sensor Fusion, and Target Recognition IV, (5 July 1995); doi: 10.1117/12.213034; https://doi.org/10.1117/12.213034


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