Method of image segmentation based on character selection and hidden Markov model

A feature selection and image segmentation technology, applied in the field of image processing, can solve the problems of insufficient prior information, the segmentation results cannot obtain regional consistency and the unity of edge accuracy, etc., to achieve accurate segmentation results, good edges, strong robustness, etc. awesome effect

Inactive Publication Date: 2008-12-24
XIDIAN UNIV
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Problems solved by technology

[0004] The methods based on the characteristics of a single transform domain, such as the method based on the wavelet domain, the method based on the complex wavelet domain, and the method based on the contourlet domain, because they do not represent the information of different images from multiple perspectives, the prior information is not sufficient, so it cannot The images obtained by different methods have obtained better segmentat

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  • Method of image segmentation based on character selection and hidden Markov model
  • Method of image segmentation based on character selection and hidden Markov model
  • Method of image segmentation based on character selection and hidden Markov model

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Embodiment Construction

[0030] refer to figure 1 , the specific implementation process of the present invention is as follows:

[0031] 1. Calculate the final training feature set corresponding to each training image block.

[0032] The research field of multi-scale geometric analysis has been developed so far. When the hidden Markov statistical model is applied to image segmentation, it is only applied to wavelet domain features, complex wavelet domain features and contourlet domain features. As a time-frequency analysis tool with excellent performance, wavelet transform has its specific three-level statistical characteristics. Its secondary statistical characteristics: the marginal distribution of subband coefficients all meet the marginal distribution form of "sharp peak value and heavy tail", which is usually the object of Gaussian mixture modeling. Secondly, the dual-tree complex wavelet transform not only retains the good video localization analysis ability of the traditional wavelet transfor...

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Abstract

The invention discloses an image segmentation method based on feature selection and hidden Markov model, which comprises the processes that: an image block corresponding to the texture of an image to be segmented is extracted, and a corresponding training feature set is extracted; a model parameter theta j<c> is obtained; a likelihood value corresponding to data blocks of various scales corresponding to scale analysis of the image to be segmented and a likelihood value corresponding to the pixel points of the image to be segmented are respectively obtained and combined together to obtain likelihood value k<c> that is required in final fusion; the initial segmentation results of various scales are obtained; context-2 and context-6 are adopted in sequence to carry out the multi-scale post fusion segmentation to the image; the result of scale 0 is taken as a final segmentation result; the image segmentation method aims at solving the defects that the traditional image segmentation method based on hidden Markov model does not make full use of image information and background guiding the segmentation of the image when in post fusion can not completely retain edge information on fine scales, and can be used for the segmentation of synthetic aperture radar (SAR) images, remote sensing images and textured images.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to an image segmentation method, which can be applied to the segmentation of synthetic aperture radar SAR images, remote sensing images, and natural texture images. Background technique [0002] Image segmentation is an image processing method that divides a given image into regions with different characteristics according to certain segmentation criteria. As an important branch in the image analysis hierarchy, image segmentation has always been the focus and hotspot in the field of image engineering. Texture image analysis and segmentation is one of the most classic research topics in image processing and computer vision. It plays an important role in national defense and national economy. It is used in image classification, image retrieval, image understanding, target recognition and other issues. played a key role. The purpose of texture segmentation is to divide the i...

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Application Information

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IPC IPC(8): G06K9/62G06K9/46
Inventor 侯彪刘凤王爽焦李成张向荣马文萍
Owner XIDIAN UNIV
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