Learning to detect contours in natural images via biologically motivated schemes

• Published in: 2013 IEEE International Conference on Image Processing pp. 123 - 126
• Main Authors: Qiling Tang, Nong Sang, Haihua Liu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2013
• Subjects: Brain modeling; Computational modeling; contour detection; Detectors; Histograms; Image color analysis; Image edge detection; learning; visual perception; Visualization
• ISSN: 1522-4880; 2381-8549
• DOI: 10.1109/ICIP.2013.6738026

A model for detecting contours in natural images is presented by combining the visual perceptual mechanisms and machine learning. The surround stimuli will enhance the response of the central stimulus if they can form a precise spatial configuration. On the other hand, surround inhibition will reduce the responses to homogeneous elements. Facilitation and inhibition activities in the primary visual cortex (V1) are used to enhance the well-organized structures and to reduce the non-meaningful distractors engendering from texture fields, respectively. We approach the task of facilitatory and inhibitory cue integration as a supervised learning problem using the logistic regression model. Our experiments demonstrate that the model can dramatically reduce texture edges and spurious contours, and meanwhile can to some extent avoid ground-truth contours missed by the detector.