A quality-concordance metric based contour detection by utilizing composite-cue information and particle swarm optimisation

Abstract

Contour detection forms a significant module of computer vision frameworks, and is still an active area of research. This paper presents a feature-based edge detection strategy on color images, where the likeliness of a pixel to lie on a border separating two distinct regions is estimated by utilizing joint information obtained from two different visual cues. The first cue draws special attention to regions with presence of discontinuities and is constructed by exploiting standard deviation, busyness and entropy measures on the input image and its intrinsic map. The second cue diminishes the chances of broken edge generation by utilizing a population-based global optimisation heuristic (Particle Swarm Optimization) to detect the final edges from highlighted regions of the former cue. The result achieves noteworthy performance that is orders of magnitude better than most of the competing standard approaches, while attaining promising detection results on BSDS300 dataset.