CEDContours: A high speed contour detector for color images

Abstract

We propose a high-speed contour detector for color images that produces its contours as set of edge segments, each a chain of pixels. The proposed algorithm performs a multi-scale analysis of the input image by combining the edge segments produced by Color Edge Drawing (CED) at different scales; thus, the name CEDContours. We evaluate the performance of CEDContours both qualitatively by presenting visual experimental results, and quantitatively within the precision-recall framework of the Berkeley Segmentation Dataset and Benchmark (BSDS300 and BSDS500). Experimental results show that CEDContours with the DiZenzo gradient operator, named CEDContours-DiZenzo, surpasses many of the prominent contour detectors found in the literature (0.70 and 0.71 F-score for BSDS300 and BSDS500 respectively), and gives comparable results to the leading contour detectors, i.e., the global Probability of boundary ultrametric contour maps (gPb-ucm: 0.71 and 0.73), and the sparse code gradients (scg: 0.72 and 0.74), but runs up to 100 times faster than these contour detectors (700ms for 481×321 images as opposed to 40s for gPb-ucm and 70s for scg), making it suitable for high-speed image processing and computer vision applications.