Analysing the patterns of spatial contrast discontinuities in natural images for robust edge detection

Article Type

Research Article

Publication Title

Pattern Analysis and Applications


The pattern of spatial contrast discontinuities in natural images has been analysed in the present work, and based on it, a new adaptive model of the bio-inspired Difference of Gaussian (DOG)-based edge detector has been designed. The distinguishing feature of the proposed filter is that the magnitude of surround suppression in receptive field of the DOG is adaptively adjusted depending on the nature of discontinuity of the edge profile. The model is based on the biological evidences indicating the possibility that human brain may be endowed with the ability to perform Fourier decomposition of visual images into its various components of spatial frequencies. It may be shown that information obtained from such a Fourier decomposition may help to measure the strength of contrast (sharpness of discontinuity) in the intensity profile across any possible edge in the natural image. In the present model, it is assumed that the magnitude of surround suppression in an excitatory–inhibitory receptive field is dependent on the sharpness of discontinuity. The suppression is strong when the edge contrast is poor, while it becomes weaker as the edge contrast is high. At a biphasic edge, the surround suppression is vanishingly small. Natural images collected from benchmark databases are used to evaluate the efficiency and robustness of the proposed model for the detection of edges. The result shows that the edge maps generated through the proposed model are at par, if not more effective as compared to the classical edge detectors like Canny. The performance of the proposed model is also compared with a number of recently proposed alternative adaptive models for edge detection.

First Page


Last Page




Publication Date


This document is currently not available here.