A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Generalized Gabor Scheme of Image Representation in Biological and Machine Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Graphical Models and Image Processing
Logical/Linear Operators for Image Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
A neural network model for extraction of salient contours
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Contour detection based on nonclassical receptive field inhibition
IEEE Transactions on Image Processing
Mumford-Shah regularizer with contextual feedback
Journal of Mathematical Imaging and Vision
Review article: Edge and line oriented contour detection: State of the art
Image and Vision Computing
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A contour detection model, inspired by the behavior of the primary visual cortex, is presented. The response of a central stimulus in the receptive field is affected by the presence of surrounding stimuli - for some stimulus conditions, the response is suppressed and for other conditions the response is enhanced. The visual mechanisms of contextual influences are utilized to extract ''coherent'' configurations. This is mainly due to the following two reasons: (1) on the one hand, a smooth contour can yield collinear excitation, which highlights smooth contours from irregularly textured surround; (2) on the other hand, similar orientation textures receive iso-orientation surround suppression and region boundary is subjected to the less inhibition, which makes boundary more salient for perceptual pop-out. Accordingly, smooth contours progressively stand out from their surround and at the same time textures are gradually suppressed by their surround through dynamic fine-tuning of contextual information. The proposed method which distinguishes between contours and texture edges is more effective for contour-based object recognition tasks. Initial experiments show that the model can be successfully applied to contour detection. Especially, when object contours are lumped together with unwantedly cluttered surround, the advantage of our approach is more prominent. This study provides a biological scheme for contour detection in computer vision.