TY - JOUR
T1 - A neural contextual model for detecting perceptually salient contours
AU - Huang, Wentao
AU - Jiao, Licheng
AU - Jia, Jianhua
AU - Yu, Hang
N1 - Funding Information:
The authors are grateful to the anonymous reviews for their valuable suggestions and comments, which have improved this paper. This work was supported by the National High Technology Research and Development Program (863 Program) of China (No. 2009 AA12Z210 ), the National Natural Science Foundation of China (Nos. 60703108, 60703107 and 60803098), the Provincial Natural Science Foundation of Shaanxi of China (No. 2007F32), the China Postdoctoral Science Foundation funded project (No. 20080431228), the China Postdoctoral Science Foundation Special funded project (No. 200801426) and the Program for Cheung Kong Scholars and Innovative Research Team in University (No. IRT0645).
PY - 2009/8/1
Y1 - 2009/8/1
N2 - 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.
AB - 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.
KW - Collinear facilitation
KW - Computational model
KW - Contour detection
KW - Surround suppression
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U2 - 10.1016/j.patrec.2009.05.006
DO - 10.1016/j.patrec.2009.05.006
M3 - Article
AN - SCOPUS:67649243574
SN - 0167-8655
VL - 30
SP - 985
EP - 993
JO - Pattern Recognition Letters
JF - Pattern Recognition Letters
IS - 11
ER -