Detecting salient contours using orientation energy distribution

How does our visual system detect prominent contours? Our investigation begins with the observation that neurons in the visual cortex have receptive fields similar to oriented Gabor filters. Unlike plain gray-level intensity histograms which greatly vary across images, we found that Gabor orientation-response (or orientation-energy) histograms of natural images have a fairly uniform shape. Based on this observation, we derived a threshold criterion which only depends on the standard deviation of the orientation-energy distribution. Thus, the same principle could be uniformly applied to different natural images, either locally or globally. Comparison with thresholds chosen by humans showed that the criterion can accurately predict human performance. Further, the proposed criterion can be easily implemented in a neural network, which is currently under investigation.