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Mathematical morphology-based image edge detection method

A mathematical morphology and image edge technology, applied in the field of image processing, can solve the problems of unsatisfactory suppression of noise and false edges, increase of image noise, etc.

Inactive Publication Date: 2010-12-29
ZHEJIANG UNIV
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Problems solved by technology

[0003] Traditional image edge detection methods, such as Roberts operator, Sobel operator, Prewitt operator, Laplacen operator, etc., mainly use the first-order or The second-order differential operation is used to extract the image edge, but since the noise and the edge in the image are high-frequency components, this type of differential operator will inevitably increase the noise in the image
The main idea of ​​the optimal operator method that appeared later is to use an appropriate smoothing filter to reduce the influence of noise in the high-frequency component before the differential operation, such as LoG (Laplacen of Gaussian) operator, Canny (Canny) operator, Sine operators, etc., although achieved some results, are still unsatisfactory in suppressing noise and false edges

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[0057] Attached below Figure 1-10 And the formulas (1)-(13) further illustrate the embodiment of the present invention.

[0058] For the four basic mathematical morphology operators used in the present invention, i.e. expansion operator, corrosion operator, opening operator and closing operator, the operation expressions of the grayscale images are as (3)-(6) defined by the formula. In the expansion operation, the gray value of a certain pixel point (r, c) in the input image f after being expanded by the structural element b is equal to the point (i, j) in the field of all structural elements and the point (i, j) falling in the field of f The maximum value of the sum of gray values ​​of points (r-i, c-j). In practical applications, the gray value of each point in the structural element is all zero, so the expansion operation becomes the search for the maximum gray value of (r-i, c-j), and the same erosion operation is the search for (r+i, c+j) minimum gray value. The open...

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Abstract

The invention relates to a mathematical morphology-based image edge detection method. The conventional method has defects in aspects of inhibition of noise and fake edges. The method has the following steps of: performing morphological processing of an image by using structuring elements of various scales to obtain an edge information image; performing weighted coalition of the edge information image to obtain a total edge information image; and finally performing thinning and thresholding segmentation of the total edge information image to obtain a final binary edge image. In the method, the structuring elements of various scales and the improved edge detection operators are adopted, which not only effectively filters noise and inhibits fake edges, but also keeps edge details to the maximum extent, so that the good edge effect is achieved.

Description

technical field [0001] The invention belongs to the field of image processing and relates to an image edge detection method based on mathematical morphology. Background technique [0002] The edge is the discontinuous part of the local grayscale change of the image, which contains the most important information of an image and is also the most sensitive part of the human eye. Image edge detection technology has a wide range of applications in various fields such as video processing, computer vision, biomedicine, and pattern recognition. [0003] Traditional image edge detection methods, such as Roberts operator, Sobel operator, Prewitt operator, Laplacen operator, etc., mainly use the first-order or The second-order differential operation is used to extract the image edge, but since the noise and the edge are high-frequency components in the image, this kind of differential operator will inevitably increase the noise in the image. The main idea of ​​the optimal operator me...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
Inventor 谢磊陈惠芳艾鑫
Owner ZHEJIANG UNIV
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