A New Fuzzy Approach for Edge Detection

Becerikli, Yaşar; Karan, Tayfun M.

doi:10.1007/11494669_116

Cited by 42 publications

(24 citation statements)

References 4 publications

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“…They proposed an edge detection and enhancement algorithm based on fuzzy system for recovering missing edges and discarding false edges caused by noise. Becerikli and Karan [29] proposed the detection of edges based on simple if-then rules using FL. They asserted that fuzzy rules based algorithm is more flexible in handling the thickness of edges.…”

Section: E Methods Based On Computational Intelligencementioning

confidence: 99%

A Framework to Select Edge Detection Method Using Multi-criteria Decision Making

Naeem

Siraj

2013

2013 IEEE International Conference on Systems, Man, and Cybernetics

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In the fields of computer vision and image processing, edge detection refers to the identification and localization of significant changes in a digital image. This article presents a survey of widely-used edge detection techniques including linear approaches, morpohlogical operations, multi-resolution analysis, and machine learning methods. Since there exists no single method that is applicable in all situations, different methods are deployed for different applications. A hierarchical framework based on multiple criteria has been proposed here that can facilitate the process of selecting the most appropriate edge detection method in a given scenario. Use of the proposed framework has been explained through an example of medical images. Finally, possible areas for further exploration have been highlighted.

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Section: E Methods Based On Computational Intelligencementioning

confidence: 99%

A Framework to Select Edge Detection Method Using Multi-criteria Decision Making

Naeem

Siraj

2013

2013 IEEE International Conference on Systems, Man, and Cybernetics

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“…It is widely used in various fields of signal and image processing in recent times [2,3,4,5]. In fuzzy set theory, a degree of membership is assigned to each element, where the degree of non-membership is just automatically equal to 1 minus the degree of membership.…”

Section: Introductionmentioning

confidence: 99%

A new method to construct intuitionistic fuzzy sets deducing from fuzzy sets via fuzzy entropy

Zhou¹,

YuFeng²,

Song-wei

et al. 2014

2014 IEEE 5th International Conference on Software Engineering and Service Science

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In this paper, a new attempt has been made using fuzzy set to construct intuitionistic fuzzy set basing on fuzzy entropy. A new intuitionistic fuzzy set generator is defined, which obtained hesitation degree by applying so-called 'complement' to membership. The relationship between fuzzy entropy and hesitation degree of intuitionistic fuzzy set is discussed. The method to calculate hesitation degree of intuitionistic fuzzy set basing on fuzzy entropy is illustrated. With this proposed constructing method, edge detection has been carried out, and the results have been found better with respect to previous methods.

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“…The process of edge detection reduces an image to its edge details that appear as the outlines of image objects that are often used for feature extraction and object recognition. Recent researchers that have deployed and exploited fuzzy techniques includes [3], [4], [5], [6], [7], [8] and [9]. Tizhoosh [9] introduced heuristic memberships function, simple fuzzy rules and fuzzy complements in his edge detector system.…”

Section: Introductionmentioning

confidence: 99%

“…Tizhoosh [9] introduced heuristic memberships function, simple fuzzy rules and fuzzy complements in his edge detector system. Becerikli and Karan [3], designed a new fuzzy approach using a rule based for an edge detection. Kim, Lee and Kweon [6] too took the advantage of fuzzy technique in their automated edge detection by adopting fuzzy-based edge thresholding.…”

Section: Introductionmentioning

confidence: 99%

Localized bone cortical width measurements using various fuzzy edge detectors

Khalid¹,

Manaf²,

Aziz³

2010

2010 the 2nd International Conference on Computer and Automation Engineering (ICCAE)

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Nature of medical images makes segmentation a difficult problem. Often the human anatomy in medical images blend with its surrounding due to low contrast thus making changes in the edge gray level too low and difficult to detect. Medical images are also noisy and fuzzy. The main challenge of this paper is to detect the outer cortical (OC) and inner cortical (IC) of the bone for the measurement of the inner diameter (ID) and the outer diameter (OD) of the cortical. The fact that bone radiographs are often grainy and inconsistent in gray level, prompt us to compare the performance of three fuzzy edge detection algorithms: Gaussian Rule based Fuzzy (GRBF), Robust Fuzzy Complement (RFC) and Classical Fuzzy Heuristics (CFH). To conduct a systematic evaluation of this fuzzy edge detector, we proposed to combine visual observation and number of edge pixels detected. The experimental data used as test samples consist of a hundred and thirty non dominant hand radiograph of normalKelantanese female between the ages of twenty to seventy. Only metacarpal 2, 3 and 4 are considered. The results prove to be quite promising because most edges of both IC and OC are well detected.

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A New Fuzzy Approach for Edge Detection

Cited by 42 publications

References 4 publications

A Framework to Select Edge Detection Method Using Multi-criteria Decision Making

A Framework to Select Edge Detection Method Using Multi-criteria Decision Making

A new method to construct intuitionistic fuzzy sets deducing from fuzzy sets via fuzzy entropy

Localized bone cortical width measurements using various fuzzy edge detectors

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