An method for Image Edge Recognition Based on Multi-Operator Dynamic Weight Detection

Showing a genuine 3 dimensional (3D) objects with the striking profundity data is dependably a troublesome and cost-devouring procedure. Speaking to 3D scene without a noise (raw image) is another case. With a honed technique for survey profundity measurement can be effortlessly gotten, without requiring any extraordinary instrument. In this paper, we have proposed an edge recognition process in a profundity picture dependent on the picture based smoothing and morphological activities.In this strategy, we have utilized the guideline of Median sifting, which has a prestigious element for edge safeguarding properties. The edge discovery was done dependent on the Canny Edge Detection Algorithm. Along these lines this strategy will help to identify edges powerfully from profundity pictures and add to advance applications top to bottom pictures

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Infrared image edge recognition and defect quantitative determination based on the algorithm of fuzzy C-means clustering and Canny operator

Infrared and Laser Engineering ◽

10.3788/irla20164509.928001 ◽

2016 ◽

Vol 45 (9) ◽

pp. 928001

Author(s):

唐庆菊 Tang Qingju ◽

刘俊岩 Liu Junyan ◽

王扬 Wang Yang ◽

刘元林 Liu Yuanlin ◽

梅晨 Mei Chen

Keyword(s):

Quantitative Determination ◽

Infrared Image ◽

Canny Operator ◽

Fuzzy C Means ◽

Fuzzy C Means Clustering ◽

Image Edge ◽

Edge Recognition

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Research on Clone Algorithm Applied in Edge Detection of Retinal Vessels

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.718-720.2302 ◽

2013 ◽

Vol 718-720 ◽

pp. 2302-2307

Author(s):

Dong Cui ◽

Min Min Liu ◽

Guang Yu Zhang ◽

Jiao Qing ◽

Di Chen ◽

...

Keyword(s):

Edge Detection ◽

Recognition Performance ◽

Noise Removal ◽

Retinal Vessels ◽

Edge Image ◽

Edge Connectivity ◽

Image Edge ◽

Simulation Results ◽

Edge Recognition

A marginal clone algorithm is proposed in order to make edge detection of retinal vessels image have better edge continuity and less detection points. It is applied in edge detection of retinal vessels with a combination of edge connectivity and noise removal algorithm based on analyzing image edge cloning theory and algorithms. Simulation results show that the edge image detected by the algorithm has solved problems of traditional edge discontinuities and too much noise. Moreover, it has better edge recognition performance.

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