A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature detection from local energy
Pattern Recognition Letters
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inferring global perceptual contours from local features
International Journal of Computer Vision - Special issue on computer vision research at the University of Southern California
A neural model of contour integration in the primary visual cortex
Neural Computation
Computational Framework for Segmentation and Grouping
Computational Framework for Segmentation and Grouping
A model of contextual interactions and contour detection in primary visual cortex
Neural Networks - 2004 Special issue Vision and brain
Extraction of salient contours from cluttered scenes
Pattern Recognition
Computer Vision and Image Understanding
Singularity detection and processing with wavelets
IEEE Transactions on Information Theory - Part 2
Contour detection based on nonclassical receptive field inhibition
IEEE Transactions on Image Processing
A single functional model of drivers and modulators in cortex
Journal of Computational Neuroscience
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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.