Color Edge Detection Based on Morphology

Zou, Jin; Li, Hongsong; Liu, Bin; Zhang, Renfei

doi:10.1109/cce.2006.350812

Cited by 8 publications

(2 citation statements)

References 7 publications

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“…In mathematic morphology, image is regarded as a set, which coming of Minkowski's morphological transform of addition and subtraction. Jin Zou [1] proposed color edge detection algorithm based on Morphology, Won Yeol Lee [2] completes edge detection by using morphological amoebasin noisy Images, In 1990's, Sinha and Doughterty [3] introduce fuzzy mathematic to mathematical morphology, De Baets [4][5] applies fuzzy mathematical morphology in image analysis and achieves better effect than hard calculation method on certain occasion. However, image information is often complicated, and the processing precision may not be good.…”

Section: Introductionmentioning

confidence: 99%

Multi-direction Fuzzy Morphology Algorithm for Image Edge Detection

Wang¹

2011

JNW

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A multi-direction fuzzy morphology algorithm, for image edge detection is proposed to deal with edge blur and inaccuracy of boundary localization. In the algorithm, two thresholds are selected to conduct image segmentation and image obtaining respectively, fuzzy enhancement for the image is adopted to resolve the loss of edge information and multi-directional structural elements are used to detect image edge. Based on it, experiments are carried out, the results show the proposed algorithm has strong anti-noise ability and superior to traditional edge detection algorithms

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Section: Introductionmentioning

confidence: 99%

Multi-direction Fuzzy Morphology Algorithm for Image Edge Detection

Wang¹

2011

JNW

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“…Besides being fast to calculate, the intended resulting image must be absent of noise as much as possible, with well defined contours and be tolerant to the motion blur introduced by the movement of the ball and the robots. Some popular edge detectors were tested, namely Sobel (Zin et al, 2007;Zou et al, 2006;Zou and Dunsmuir, 1997), Laplace (Blaffert et al, 2000;Zou and Dunsmuir, 1997) and Canny (Canny, 1986). According to our experiments, the Canny edge detector was the most demanding in terms of processing time.…”

Section: Arbitrary Ball Detectionmentioning

confidence: 99%