A fast edge detection using fuzzy rules

Talai, Zoubir; Talai, Abdelouaheb

doi:10.1109/ccca.2011.6031499

Cited by 14 publications

(7 citation statements)

References 4 publications

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“…Clustering Instruction is used to cluster the image using K means algorithm. Finally the output pixel writing is used to extract the pixel intensity value of the output image and then it calls for the next function [8]. By applying this algorithm any kind of gray scale image of all edges can be determined.…”

Section: Output Results and Discussionmentioning

confidence: 99%

A Paper On Edge Detection In Images Using Fuzzy K Means Clustering Approach

Dhivya

Prakash²,

Thilepa³

2016

IJECS

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Abstract:Edge Detection plays a major role in Image processing applications. K means clustering using fuzzy logic is a technique which produces high quality images. There are existences of many edge detection methods. Among these this approach using Fuzzy logic elevates the performance in the output for Gray scale images. The features considered for edge detection are Mean, Standard Deviation, Entropy and image gradient. Using these features the image quality has been improved by this Fuzzy K means Clustering approach.

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Section: Output Results and Discussionmentioning

confidence: 99%

A Paper On Edge Detection In Images Using Fuzzy K Means Clustering Approach

Dhivya

Prakash²,

Thilepa³

2016

IJECS

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show abstract

“…However, ultrasound image is characterized by the multiplicative speckle noise and low signal-to-noise ratio (SNR), wherefore traditional edge detection operators are not suitable for its interpretation. 3 Over time a variety of anti-noise edge detection methods have been proposed, such as multiscale-based methods, [4][5][6] fuzzy rules-based methods, 7,8 adaptive threshold-based methods, 9,10 and machine learning-based methods, 11,12 which could improve the detection accuracy and noise robustness to a certain extent. However, most of these methods applies additive operators, which are not suitable for ultrasound images with multiplicative speckle noise.…”

Section: Introductionmentioning

confidence: 99%

“…Compared with the MG operator proposed by Marco, improvements of the novel MG include two aspects: The first is a two-dimensional operator template is used instead of the one-dimensional template, which can improve the detection accuracy; the second is an unsupervised threshold determination method is incorporated, which can improve the noise robustness. To verify the efficiency of the new method, we compared the proposed method with the traditional Canny, the original MG, and two edge detection methods proposed in Talai and Talai 8 and Ray 9 (called Fuzzy and ADM_NMS in the following contents, respectively), with experiments on the simulated speckle noised images and in vivo medical ultrasound images.…”

Section: Introductionmentioning

confidence: 99%

Ultrasound Image Edge Detection Based on a Novel Multiplicative Gradient and Canny Operator

Zheng

Zhou

et al. 2014

Ultrason Imaging

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To achieve the fast and accurate segmentation of ultrasound image, a novel edge detection method for speckle noised ultrasound images was proposed, which was based on the traditional Canny and a novel multiplicative gradient operator. The proposed technique combines a new multiplicative gradient operator of non-Newtonian type with the traditional Canny operator to generate the initial edge map, which is subsequently optimized by the following edge tracing step. To verify the proposed method, we compared it with several other edge detection methods that had good robustness to noise, with experiments on the simulated and in vivo medical ultrasound image. Experimental results showed that the proposed algorithm has higher speed for real-time processing, and the edge detection accuracy could be 75% or more. Thus, the proposed method is very suitable for fast and accurate edge detection of medical ultrasound images.

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“…Morphological based operations used for extraction of one pixel thin edges [2], [10] and results obtained were further improved by the fuzzy logic [5], [6], [7], [8], [9], neural networks [14] and ant colony algorithms.…”

Section: Introductionmentioning

confidence: 99%

Efficient fuzzy edge detection using successive Otsu's method

Gautam

Ahmed

2014

International Conference for Convergence for Technology-2014

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In this paper we propose fuzzy based edge detection using Successive Otsu's method. Edge detection has always been a vital aspect of majority in image processing techniques. In this work we describe the algorithm of Successive Otsu technique and utilize same in grouping image histogram into different partitions. The algorithm proposed here is implemented to provide the threshold parameter to classical Sobel Operator in order to enhance its edge detection capability using the fuzzy logic. The results are compared with Canny Edge Detector and finally concludes that proposed method based Sobel edge detection process behaves much alike to Canny edge detection process.

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A fast edge detection using fuzzy rules

Cited by 14 publications

References 4 publications

A Paper On Edge Detection In Images Using Fuzzy K Means Clustering Approach

A Paper On Edge Detection In Images Using Fuzzy K Means Clustering Approach

Ultrasound Image Edge Detection Based on a Novel Multiplicative Gradient and Canny Operator

Efficient fuzzy edge detection using successive Otsu's method

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