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Selective Change-Driven Image Processing: A Speeding-Up Strategy

机译:选择性变化驱动图像处理:加速策略

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Biologically inspired schemes are a source for the improvement of visual systems. Real-time implementation of image processing algorithms is constrained by the large amount of data to be processed. Full image processing is many times unnecessary since there are many pixels that suffer a small change or not suffer any change at all. A strategy based on delivering and processing pixels, instead of processing the complete frame, is presented. The pixels that have suffered higher changes in each frame, ordered by the absolute value of its change, are read-out and processed. Two examples are shown: a morphological motion detection algorithm and the Horn and Schunck optical flow algorithm. Results show that the implementation of this strategy achieves execution time speed-up while keeping results comparable to original approaches.
机译:受生物启发的方案是改善视觉系统的来源。图像处理算法的实时实施受到要处理的大量数据的限制。由于有许多像素发生很小的变化或根本没有任何变化,因此完全不需要进行多次图像处理。提出了一种基于传递和处理像素而不是处理整个帧的策略。读出并处理每一帧中变化较大的像素(按其变化的绝对值排序)。显示了两个示例:形态学运动检测算法以及Horn和Schunck光流算法。结果表明,该策略的实现可加快执行时间,同时保持结果可与原始方法媲美。

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