Abstract
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.
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Mittal, A., Sofat, S., Hancock, E., Mousset, S. (2012). A Statistical Operator for Detecting Weak Edges in Low Contrast Images. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2012. Lecture Notes in Computer Science, vol 7324. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31295-3_11
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DOI: https://doi.org/10.1007/978-3-642-31295-3_11
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