Perceptual edge detection via entropy-driven gradient evaluation

This study presents a novel method based on entropy-driven gradient evaluation (P-EdGE) for detecting perceptual edges that represent boundaries of objects as perceived by human eyes. P-EdGE is characterised by iteratively employing a shape-changeable mask centred at a target pixel to sample gradient orientations of neighbouring pixels for measuring the directivity of the target pixel. The mask deforms to automatically cover pixels most suitable for exhibiting the directivity of the target pixel. The authors show that such an iterative scheme satisfies the similarity and proximity laws in Gestalt theory. The converged directivity in conjunction with the gradient magnitude are subjected to a Bayesian process, they show that doing so can help effectively determine whether the target pixel belongs to a perceptual edge. Experimental results are presented to justify the superiority of P-EdGE over other methods in detecting perceptual edges.