Improved Contour Detection by Non-classical Receptive Field Inhibition

Authors:
Cosmin Grigorescu;Nicolai Petkov;Michel A. Westenberg
Affiliations:
-;-;-
Venue:
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Year:
2002

Citing 5
Cited 0

A Computational Approach to Edge Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Localization Performance Measure and Optimal Edge Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling surround suppression in V1 neurons with a statistically-derived normalization model

Proceedings of the 1998 conference on Advances in neural information processing systems II
Shape Matching and Object Recognition Using Shape Contexts

IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour detection based on nonclassical receptive field inhibition

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a biologically motivated computational step, called nonclassical receptive field (non-CRF) inhibition, to improve the performance of contour detectors. We introduce a Gabor energy operator augmented with non-CRF inhibition, which we call the bar cell operator. We use natural images with associated ground truth edge maps to assess the performance of the proposed operator regarding the detection of object contours while suppressing texture edges. The bar cell operator consistently outperforms the Canny edge detector.