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Digital image noise-proof categorizing technology based on geodesic distance

A digital image and geodesic distance technology, applied in the field of image processing, can solve problems such as segmentation, inability to obtain clear region boundaries, and neglect of connectivity between pixels

Inactive Publication Date: 2014-08-06
SHANDONG UNIV OF FINANCE & ECONOMICS
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  • Abstract
  • Description
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  • Application Information

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Problems solved by technology

[0006] The K-means clustering algorithm is successfully applied to image segmentation, but it still has several shortcomings: (1) The original K-means clustering algorithm is based on the Euclidean distance between pixels, only considering whether the pixels are adjacent in physical space, ignoring (2) The original K-means algorithm successfully handles isotropic images, but it may appear over-segmented for noisy images, and cannot obtain clear region boundaries

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  • Digital image noise-proof categorizing technology based on geodesic distance
  • Digital image noise-proof categorizing technology based on geodesic distance
  • Digital image noise-proof categorizing technology based on geodesic distance

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Embodiment Construction

[0043] The present invention will be further described below in conjunction with the accompanying drawings.

[0044] As shown in Figure 1 flow chart, the present invention comprises the following steps:

[0045] 1. Find k lines in the image to be segmented as cluster centers, and the cluster center set is C=(C 1 , C 2 ......C k ), the clustering region set is W=(W １ , W ２ ......W k ), cluster center C i The cluster where it is located is W i , k is the number of cluster centers, that is, it needs to be divided into k regions.

[0046] 2. Use the color value of the image to perform color clustering on its pixels:

[0047] 2-1-1. Calculate the probability distribution function of the area where each cluster center is located:

[0048] P(x|W i ), (i=1, 2...k)

[0049] 2-1-2. Calculate each pixel belongs to the cluster W i The probability:

[0050] P Wi ( x ) = ...

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Abstract

The invention discloses a digital image noise-proof categorizing technology based on geodesic distance. The technology includes: by an algorithm, properly selecting the initial centers of k categories; in the ith iteration, calculating the similarity of an optional pixel to k centers, and categorizing the pixel to category of the center with the shortest distance; calculating the mean value of each area, and updating clustering centers; if the values of all the k clustering centers are unchanged, stopping iteration, or else continuing iteration. By the technology, the influence of noise can be eliminated effectively to obtain clear cutting effect, the color space and the physical space of an image are combined to allow the pixels of each cluster to be close in color and space, and problems of overcutting and unobvious boundary are solved effectively.

Description

(1) Technical field [0001] The invention relates to a digital image anti-noise classification technology and belongs to the field of image processing. (2) Background technology [0002] Images are the most intuitive source for human beings to obtain information about objective things, and they can play a unique role in information transmission. With the development of high technology, digital image processing is increasingly used in military, meteorological, medical, transportation and other industries. [0003] In order to reduce the error of image processing, it is necessary to extract the region of interest, so the image needs to be segmented. Image segmentation is to divide an image into several disjoint sub-regions, and the pixels in the same sub-region have certain same or similar characteristics (such as grayscale, color, texture, etc.). Image segmentation is an important image processing technology, which is a key step from image processing to image analysis, and i...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06K9/62
Inventor 高珊珊张彩明迟静沈晓红
Owner SHANDONG UNIV OF FINANCE & ECONOMICS