2009 16th IEEE International Conference on Image Processing (ICIP) 2009
DOI: 10.1109/icip.2009.5414086
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A new methodology for evaluation of edge detectors

Abstract: This paper defines a new methodology for evaluating edge detectors through measurements on edginess maps instead of on binary edge maps as previous methodologies do. These measurements avoid possible bias introduced by the applicationdependent process of generating binary edge maps from edginess maps. The features of completeness, discriminability, precision and robustness, which a general-purpose edge detector must comply with, are introduced. The R, DS, P and F AR-measurements in addition to P SNR applied to… Show more

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Cited by 30 publications
(30 citation statements)
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References 10 publications
(18 reference statements)
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“…This property is related to discriminability as described in Moreno et al (2009). This property can be assessed by comparing the mean vesselness inside a generalized cylinder of radius δ 1 to the one at the region between that cylinder and a larger concentric generalized cylinder (cf.…”
Section: Resultsmentioning
confidence: 99%
“…This property is related to discriminability as described in Moreno et al (2009). This property can be assessed by comparing the mean vesselness inside a generalized cylinder of radius δ 1 to the one at the region between that cylinder and a larger concentric generalized cylinder (cf.…”
Section: Resultsmentioning
confidence: 99%
“…Many evaluation methodologies have been proposed to evaluate performance at the second e.g., [26,10,4,15] and third points [29,2]. On the other hand, to the best of our knowledge, the methodology introduced in [22] has been the first attempt to measure performance at the first point.…”
Section: Evaluation Methodologymentioning
confidence: 99%
“…Recently, we have also introduced a general methodology to evaluate edge detectors directly in gray-scale [22]. This methodology avoids possible biases generated by post-processing of edginess maps by directly comparing the algorithms in gray-scale.…”
Section: Introductionmentioning
confidence: 99%
“…So, filters output would be grater (either negative or positive) when there are outstanding variations in the values of pixel in the right and left sides of the pixel site. A set of weights, which includes some smoothing in averaging over 3 rows is shown by [11,12]:…”
Section: Edge Detection (Ed)mentioning
confidence: 99%