Real-time, parallel segmentation of high-resolution images on multi-core platforms

Abstract

Many high-level vision applications apply image segmentation during preprocessing to improve their overall execution efficiency. The applied segmentation technique must, therefore, be highly efficient, or otherwise would counteract any system performance improvement. Existing segmentation approaches often fail to meet real-time processing standards and exhibit extremely slow frame rates when applied to high-resolution images. This paper presents a highly optimized serial implementation of a novel image segmentation approach known as leap segmentation, which achieves frame rates exceeding that of the state-of-the art: it segments more than 80 fps on 640 × 360 images and more than 20 fps on high-resolution (1,280 × 720) images. (All images used for evaluation in this paper use a 24-bit red-green-blue color representation with 8 bits per color). We analyze leap segmentation further for areas of possible parallelization and restructure it for use on a parallel processing system to achieve additional speed-up. On a multi-core, mobile processing system with four threads, our multi-core leap segmentation implementation achieves frame rates of over 114 fps on 640 × 360 images and more than 31 fps on 1,280 × 720 images, thus easily exceeding real-time processing standards.