doi:10.1016/j.cviu.2006.01.005 
Copyright © 2006 Elsevier Inc. All rights reserved.
Automatic selection of edge detector parameters based on spatial and statistical measures
Raz Korena and Yitzhak Yitzhakyb, 
, 
aBen-Gurion University, Department of Electrical Engineering, Israel
bBen-Gurion University, Department of Electro-Optics Engineering, Israel
Received 20 February 2005;
accepted 26 January 2006.
Available online 20 March 2006.
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Abstract
The basic and widely used edge detection operation in an image usually requires a prior step of setting the edge detector parameters (thresholds, blurring extent etc.). Finding the best detector parameters automatically in real-world images is a difficult challenge because no absolute ground truth exists. However, the advantage of automatic processing over manual operations done by humans motivates the development of automatic detector parameter selection. In this work, we propose an automatic 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 parameter range by incorporating saliency values in the statistical analysis. Automatic edge detection results show considerable improvement of the purely statistical method when a wrong initial parameter range is selected.
Keywords: Edge detection; Edge detector parameters; Edge detection evaluation; Saliency
Fig. 1. Original images [10] used for demonstrations. (A) Elephant, (B) grater, and (C) airplane.
Fig. 2. A sample of edge detection results using the Canny detector, produced by an initial set of parameters (sigma of smoothing-Gaussian range is 0.3:0.9, high threshold range is 0.04:0.10 and low threshold is 0.4 times the high threshold). (A–D) Present results from the most noisier to the quieter, respectively.
Fig. 3. Results of the purely statistical technique [7]. (A) The chi-square measure for the match results between the different detections and the EGT shows a maximum for the parameters (0.5, 0.08). (B) The resulting detection generated by the selected parameters is too noisy because of the initial range of parameters was not wide enough producing only noisy detections.
Fig. 4. The spatial-based gray-level saliency map.
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Fig. 5. Results of the combined spatial-statistical technique. (A) The chi-square measure for the match results between the different detections and the EGT shows a maximum for the parameters (0.9, 0.1) which is rightmost bar, indicating the highest thresholds and smoothing parameter size within the initial parameter range. (B) The resulting detection generated by these selected parameters is the least noisy among all the detections produced by the initial sets of parameters. (C) Examination of tuned sets of parameters (which produce less noisy detections). The chi-square measure shows a maximum for the parameters (0.5, 0.40) which is at the left side of the tuned parameter range indicating low thresholds and smoothing parameter size relative to the tuned range. (D) The resulting detection generated by these parameters. (E) The chi-square measure shows a maximum for the parameters (0.5, 0.24) which is at about the center of the tuned again parameter range. (F) The resulting detection generated by these parameters.
Fig. 6. Similar to Fig. 5, but for the “Airplane” image.
Fig. 7. Similar to Fig. 5, but for the “Grater” image.
Fig. 8. For a comparison purpose, edge detection results are shown here using two other methods that can be implemented without pre-definition of parameters by the user; (A–C) the method of Black et al. [11]. (D–F) The method of Meer et al. [12].
Table 1.
Definition of the outcome probabilities according to statistical decision theory terminology: (a) in the GT estimation process, and (b) for the best parameter set selection process [7]
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