A neural contextual model for detecting perceptually salient contours

A computational model, inspired by visual cortical mechanisms of contextual modulation, is presented in this paper, and is applied to detect perceptually salient contours. The presented model incorporates two mechanisms of contextual modulation, surround suppression and collinear facilitation. An oriented filterbank generated by Gaussian derivatives and their Hilbert transform is proposed for pre-processing. The operators of surround suppression and collinear facilitation are applied to the orientation energy resulting from the outputs of oriented filterbank. To avoid augmenting the noise when the facilitation operator enhances the saliency parts, we employ a contrast enhancement transformation for the facilitation operator. For drawing the binary contours, we present an automatic thresholding approach for post-processing. The performance of our model is tested by artificial images with heavy noise and nature images with texture background. Results show that the model has a good performance on extracting the salient contours from images.