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Image C-mean clustering algorithm

A mean clustering and image technology, applied in the field of image processing, can solve problems such as increasing the complexity of solving problems, and achieve the effect of good segmentation and clear edges.

Inactive Publication Date: 2015-12-30
CHENGDU RONGCHUANG ZHIGU SCI & TECH
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Problems solved by technology

People hope to introduce some artificial knowledge-oriented and artificial intelligence methods to correct some errors in segmentation, which is a promising method, but this increases the complexity of the problem

Method used

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Embodiment Construction

[0024] All features disclosed in this specification, or steps in all methods or processes disclosed, may be combined in any manner, except for mutually exclusive features and / or steps.

[0025] The information in an image includes three parts: target object, background and noise. Image binarization is an image processing method to obtain the target object in the image. After binarization, all pixels in the image will be will change to white or black. When the image contains only two parts of information, the foreground and the background, the pixel value of the foreground can be set to 1, and the pixel value of the background can be set to 0, so that the image is binarized. There are many methods of binarization, generally divided into global threshold method and local threshold method

[0026] The global threshold method refers to a method that uses only one global threshold T in the binarization process. It compares the gray value of each pixel of the image with T, if it i...

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Abstract

An image C-mean clustering algorithm includes the steps of conducting grey transformation on an image to obtain a grey image; freely selecting c different values from 0 to 255 to serve as central values for segmenting the image into c types, wherein in other words, the values of z<(k)>[1], z<(k)>[2]...z<(k)>[c], and k is made equal to 0; dividing grey values g(x,y) (x is equal to 1, 2...M, and y is equal to 1,2...N) of pixels of all different positions in the image into a certain type in the c types one by one according to the minimum distance principle; if the equation (please see the equation in the formula) works, x is equal to 1,2...M, y is equal to 1,2...N and a value belongs to {1,2...c} exists, judging that g(x,y) belongs to omega<(k+1)>[l], wherein omega<(k+1)>[l] (l is equal to 1,2...c) is a cluster, the equation (please see the equation in the specification) represents the distance between g(x,y) and the center z<(k)>[j] of omega<(k)>[j], the superscript represents the iterative times, and then a new cluster omega<(k+1)>[j] (j is equal to 1,2...c) is generated; calculating the centers of all newly-divided types, wherein in the equation, n<(k+1)>[j] represents the number of modes contained in the type omega<(k+1)>[j]; ending if z<(k+1)>[j] is equal to omega<(k)>[j] (j is equal to 1,2...c).

Description

technical field [0001] The invention relates to the technical field of image processing and provides an image C-means clustering algorithm. Background technique [0002] Data clustering is the process of dividing similar data elements into different subsets by classification, so that the data elements in the same subset have similar attributes, and the data elements in different subsets have different attributes. According to the way of data clustering, data clustering can be divided into hard clustering and fuzzy clustering (also known as soft clustering). In hard clustering, data elements are divided into different classes, and each data element can only belong to the same class; in fuzzy clustering, data elements can belong to multiple classes, and each data element is related to each Classes have corresponding degrees of membership to indicate the strength of association between data elements and a particular class. As far as the fuzzy method is concerned, the membersh...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N7/02
CPCG06N7/02G06F18/23213
Inventor 张岱齐弘文
Owner CHENGDU RONGCHUANG ZHIGU SCI & TECH
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