Disparity map computation on scalable computing

Jesús Ortiz, H. Calderon, J. Fontaine
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引用次数: 1

Abstract

This paper addresses the evaluation of a new disparity map computing algorithm characterized by a novel spurious removal strategy. Using this algorithm we eliminate a high percentage of wrong values with a low performance penalty. When testing images, incorrect percentages were reduced by 65% and 85%. This algorithm has been designed for scalable architectures with massive parallel processing elements. It works line by line with low memory requirements.
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基于可伸缩计算的视差图计算
本文讨论了一种新的视差图计算算法的评价,该算法的特点是采用了一种新的杂散去除策略。使用该算法,我们以较低的性能代价消除了较高比例的错误值。当测试图像时,不正确的百分比分别减少了65%和85%。该算法是为具有大量并行处理元素的可扩展架构而设计的。它逐行工作,内存要求低。
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