Method for segmenting image based on wavelet domain concealed Markov tree model

An image segmentation and wavelet domain technology, applied in the field of image processing, can solve problems such as inappropriate initial parameter setting, initial parameter setting problems, and inability to obtain local optimum, and achieve the effect of solving initial parameter setting problems

CN101350099AActive Publication Date: 2009-01-21探知图灵科技(西安)有限公司
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
Publication Date
2009-01-21

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Abstract

The invention discloses an image segmentation method based on a wavelet domain hidden markov tree model, which substantially solves the defects existing in a method based on the wavelet domain hidden markov tree model that wavelet domain information is not fully used and a background which guides an image to be segmented is not completely used when back fusing. The processes are that an image block which corresponds to image texture awaiting to be segmented is extracted, training data which corresponds to the image block is extracted, an initial value of an EM algorithm parameter is calculated, a model parameter theta jc which corresponds to the training data is obtained, and a likelihood value jc which is needed in final fusion is obtained. Image multi-scale back fusion segmentation uses different context backgrounds on different scales, and a result on scale 0 is used as a final segmentation result. The invention has the advantages of excellent region homogeneity and accurate edge, can be used for the segmentation which synthesizes an aperture radar SAR image, a remote sensing image and a natural texture image.
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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 a classic problem in the field of image analysis and processing, it is also a key technology. It has always been the focus and hot spot of image engineering research, and it has played a key role in image classification, image retrieval, image understanding, target recognition and other issues. role.

[0003] Over the years, the research of multi-scale transform domain has been favored by people in many scientific fields such as mathematics, physics and signal processing. Because it overcomes the limitations of the c...

Examples

Embodiment Construction

[0029] refer to figure 1 , the specific implementation process of the present invention is as follows:

[0030] Image segmentation based on hidden Markov tree model is generally divided into two parts: initial segmentation and post-fusion. The initial segmentation part includes the extraction of training data, the model used and the model training algorithm. The initial segmentation result of the image is obtained by comparing the likelihood value; The feature of good edge localization is that the initial segmentation results on each scale are connected through the background marker tree to achieve a compromise between the regional consistency and edge accuracy of the final segmentation results.

[0031] Step 1, input the image to be segmented, and intercept N from the image to be segmented c class training image patches, N c Indicates the corresponding number of texture classes in the image to be segmented.

[0032] Step 2, extract the first set of training data from each...