Automatic edge detection using 3×3 ideal binary pixel patterns and fuzzy-based edge thresholding

Kim, Dong–Su; Lee, Wang-Heon; Kweon, In-So

doi:10.1016/j.patrec.2003.09.010

Cited by 76 publications

(25 citation statements)

References 6 publications

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“…We adopted the F- measure metric to test sensibility and precision of the method. We also used it to confirm the adoption of a fixed threshold, handling the threshold choosing problem [33].…”

Section: Discussionmentioning

confidence: 99%

An Edge Detection Method using a Fuzzy Ensemble Approach

2017

APH

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Section: Discussionmentioning

confidence: 99%

An Edge Detection Method using a Fuzzy Ensemble Approach

2017

APH

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“…It is noteworthy to mention at this point the fact that other detectors (specifically those based on patterns and rules [14,15,23]) generate edginess values in a different way (generally using inference models). That is, even if they map the gradients to membership degrees using triangular functions, they combine more information to obtain edginess values.…”

Section: Using Membership Functionsmentioning

confidence: 99%

Generation of fuzzy edge images using trapezoidal membership functions

López-Molina¹,

Bustince²,

Fernández³

et al. 2011

Proceedings of the 7th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011)

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The fuzzy representation of the edges has been widely studied in different works. Generally, for each pixel, the authors use membership degrees linearly proportional to the magnitude of the gradient at that position of the image. This would be equivalent to using a triangular membership functions on the gradient magnitude. In this work we study the use of parametric functions in the transformation of the gradient images into fuzzy sets. The results obtained with different parametrical functions are studied using Baddeley's Delta Metric.

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“…Contours corresponding to earlobes are significantly diversified and contain enormous amount of information allowing ear identification [24]. In our work we use local method based on pixel illumination values and changes [25,26]. Our local contour extraction algorithms had been presented in detail in our previous work [27,28].…”

Section: Contour Detection Based On Illumination Changesmentioning

confidence: 99%

Perspective methods of human identification: Ear biometrics

Choraś

2008

Opto-Electronics Review

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Geometrical methods of feature extraction from ear images in order to perform human identification are presented. Geometrical approach is motivated by the actual procedures used by police and forensic experts (so-called ear otoscopy). In their work, geometrical features of ears such as size, height, width, and shapes of earlobe are useful and valid proofs of identity. The contribution of the article is development of the new and original methods of geometrical feature extraction from 2D ear images. Four novel algorithms of ear feature extraction from contour images are described in detail. Moreover, identification results obtained for each of the methods, based on the distance of feature vectors in the feature space, are presented.

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Automatic edge detection using 3×3 ideal binary pixel patterns and fuzzy-based edge thresholding

Cited by 76 publications

References 6 publications

An Edge Detection Method using a Fuzzy Ensemble Approach

An Edge Detection Method using a Fuzzy Ensemble Approach

Generation of fuzzy edge images using trapezoidal membership functions

Perspective methods of human identification: Ear biometrics

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