A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
A neural model of contour integration in the primary visual cortex
Neural Computation
A model of contextual interactions and contour detection in primary visual cortex
Neural Networks - 2004 Special issue Vision and brain
Salient Closed Boundary Extraction with Ratio Contour
IEEE Transactions on Pattern Analysis and Machine Intelligence
How Close Are We to Understanding V1?
Neural Computation
A model of surround suppression through cortical feedback
Neural Networks
2006 Special Issue: Pre-attentive visual selection
Neural Networks
Surrounding Suppression and Facilitation in the Determination of Border Ownership
Journal of Cognitive Neuroscience
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 new image edge detection method inspired from biological visual cortex
WSEAS Transactions on Computers
Contextual modulation via low-level vision processing
Image and Vision Computing
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Contextual modulation is a universal phenomenon in the primary visual cortex (V1). It is often allocated to the two categories of suppression and facilitation, which are either weakened or strengthened by the contextual stimuli, respectively. A number of experiments in neurophysiology have elucidated their important functions in visual information processing, such as contour integration, figure-ground segregation, saliency map and so on. A computational model, inspired by visual cortical mechanisms of contextual modulation, is presented in this paper. We first give separate models for surround suppression (SS), collinear facilitation (CF) and cross-orientation facilitation (COF), respectively, then unify them to a mixed model. Model behavior has then been tested using synthetical images and nature images, and is consistent with the data of physiological experimentation. We achieve fine results using the model to extract salient structures and contours from images. This work develops a computational model, using the perceptual mechanisms in V1, and provides a biologically plausible strategy for computer vision.