A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Stereo Vision Technique Using Curve-Segments and Relaxation Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast and automatic stereo vision matching algorithm based on dynamic programming method
Pattern Recognition Letters
Digital Image Processing: PIKS Scientific Inside
Digital Image Processing: PIKS Scientific Inside
A Contour-Based Moving Object Detection and Tracking
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Temporal consistent real-time stereo for intelligent vehicles
Pattern Recognition Letters
Automatic target recognition by matching oriented edge pixels
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
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Edge detection is an indispensible initial step in many contour-based computer vision applications like edge-based obstacle detection, edge-based target recognition, etc. The performance of these applications is highly dependent on the quality of edges detected in the initial step. Most of the edge detectors used in these applications only detect boundaries separating two regions with high intensity gradient. However, certain computer vision applications require detection of low contrast boundaries. This paper presents a statistical operator for detecting low contrast boundaries. The proposed operator is highly suited for obstacle detection systems for poor visibility conditions. To evaluate its edge detection capability under normal and low contrast conditions, it is tested on a dataset of 40 object images and on MARS/PRESCAN dataset containing foggy virtual images. The quantitative evaluations using Matthew's correlation coefficient and Pratt's figure of merit indicate that the proposed method outperforms other edge detectors.