Automatic selection of edge detector parameters based on spatial and statistical measures

Raz Koresh; Yitzhak Yitzhaky

doi:10.1117/12.560586

2 November 2004 Automatic selection of edge detector parameters based on spatial and statistical measures

Raz Koresh, Yitzhak Yitzhaky

Proceedings Volume 5558, Applications of Digital Image Processing XXVII; (2004) https://doi.org/10.1117/12.560586
Event: Optical Science and Technology, the SPIE 49th Annual Meeting, 2004, Denver, Colorado, United States

Abstract

The basic and widely used operation of edge detection in an image usually requires a prior step of setting the edge detector parameters (thresholds, blurring extent etc.). In real-world images this step is usually done subjectively by human observers. Finding the best detector parameters automatically is a problematic challenge because no absolute ground truth exists when real-world images are considered. However, the advantage of automatic processing over manual operations done by humans motivates the development of automatic detector parameter selection which will produce results agreeable by human observers. In this work we propose an automatic method for detector parameter selection which considers both, statistical correspondence of detection results produced from different detector parameters, and spatial correspondence between detected edge points, represented as saliency values. The method improves a recently developed technique that employs only statistical correspondence of detection results, and depends on the initial range of possible parameters. By incorporating saliency values in the statistical analysis, the detector parameters adaptively converge to best values. Automatic edge detection results show considerable improvement of the purely statistical method when a wrong initial parameter range is selected.

Citation Download Citation

Raz Koresh and Yitzhak Yitzhaky "Automatic selection of edge detector parameters based on spatial and statistical measures", Proc. SPIE 5558, Applications of Digital Image Processing XXVII, (2 November 2004); https://doi.org/10.1117/12.560586

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KEYWORDS

Sensors

Edge detection

Detector development

Probability theory

Statistical analysis

Statistical methods

Detection and tracking algorithms

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