CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 1995 vol.17 Issue No.01 - January

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Issue No.01 - January (1995 vol.17)

pp: 47-56

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.368153

ABSTRACT

<p><it>Abstract</it>—We develop efficient algorithms for low and intermediate level image processing on the scan line array processor, a SIMD machine consisting of a linear array of cells that processes images in a scan line fashion. For low level processing, we present algorithms for block DFT, block DCT, convolution, template matching, shrinking, and expanding which run in real-time. By real-time, we mean that, if the required processing is based on neighborhoods of size <it>m</it>×<it>m</it>, then the output lines are generated at a rate of <it>O</it>(<it>m</it>) operations per line and a latency of <it>O</it>(<it>m</it>) scan lines, which is the best that can be achieved on this model. We also develop an algorithm for median filtering which runs in almost real-time at a cost of <it>O</it>(<it>m</it><math><tmath>${\rm log}$</tmath></math><it>m</it>) time per scan line and a latency of <math><tmath>${\bf \lfloor^{\underline m}_{\,\, 2}\rfloor}$</tmath></math> scan lines. For intermediate level processing, we present optimal algorithms for translation, histogram computation, scaling, and rotation. We also develop efficient algorithms for labelling the connected components and determining the convex hulls of multiple figures which run in <it>O</it>(<it>n</it><math><tmath>${\rm log}$</tmath></math><it>n</it>) and <it>O</it>(<it>n</it><math><tmath>${\rm log}$</tmath></math><super>2</super><it>n</it>) time, respectively. The latter algorithms are significantly simpler and easier to implement than those already reported in the literature for linear arrays.</p>

INDEX TERMS

Image processing, parallel algorithms, SIMD algorithms, linear array, scan line array processors, video procesor.

CITATION

David Helman, Joseph JáJá, "Efficient Image Processing Algorithms on the Scan Line Array Processor",

*IEEE Transactions on Pattern Analysis & Machine Intelligence*, vol.17, no. 1, pp. 47-56, January 1995, doi:10.1109/34.368153