Image segmentation algorithm for local region characteristics through nonsubsampled contourlet transform

A local area feature and non-subsampling technology, which is applied in image analysis, image data processing, calculation, etc., can solve the problems of insufficient statistical information, insufficient local statistical information, and difficulty in forming large and consistent texture areas in segmentation results.

Inactive Publication Date: 2013-08-14
NORTHEAST FORESTRY UNIVERSITY
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Problems solved by technology

However, multi-scale segmentation is often difficult to obtain accurate segmentation results due to insufficient statistical information, especially insufficient local statistical information, which will make it difficult for the final segmentation results to form large and consistent texture regions.

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  • Image segmentation algorithm for local region characteristics through nonsubsampled contourlet transform
  • Image segmentation algorithm for local region characteristics through nonsubsampled contourlet transform
  • Image segmentation algorithm for local region characteristics through nonsubsampled contourlet transform

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[0016] figure 1 Be based on the image segmentation algorithm feature extraction flow chart of non-subsampling Contourlet transform local area feature; The image segmentation algorithm based on non-down sampling Contourlet transform local area feature of the present invention comprises the following steps:

[0017] (1) Perform non-subsampling Contourlet transformation on the image to be segmented to obtain low-frequency subbands and high-frequency subbands in all directions l=1,2,...,L,k=1,2,...,2 n , where L is the maximum number of decomposition layers, 2 n For the number of directions decomposed in each layer, perform nonlinear transformation and smoothing operations on each sub-band;

[0018] (2) The feature extraction method for each point in the image within the neighborhood of each subband is extracted according to the following formula: Assuming that x(i, j) is the gray value of a point in the image, then the (2n The extreme point of the neighborhood D of +1)×(2n+...

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Abstract

An image segmentation algorithm for local region characteristics through nonsubsampled contourlet transform comprises the following steps: firstly the nonsubsampled contourlet transform is carried out on an image; then the expression abilities of a local extreme value and local standard deviation on the edge of the image are utilized, and the local standard deviation and the local extreme value on each sub-band are extracted to be used as characteristic vectors; and the characteristic vectors are classified by using FCM, and accordingly the image is segmented. The method provided by the invention can be used for effectively segmenting texture images of multiple combinations, and has an excellent segmentation effect and superior segmentation performance. According to the algorithm, multiscale segmentation is taken into account, and image local region statistical characteristics are also taken into account, so the algorithm is a promising image segmentation technology.

Description

Technical field [0001] The invention relates to a texture image segmentation algorithm, in particular to an image segmentation algorithm of non-subsampling Contourlet transformation of local area features. Background technique [0002] Image segmentation has a wide range of applications in computer vision, object recognition, etc., and the quality of the segmentation has a very important impact on the subsequent processing results. The segmentation method of the image is also different due to the different methods applied. In recent years, with the popularity of wavelet transform, the application of multi-scale segmentation methods based on transform domain is increasing. For example, Liu Guoying and others used the wavelet domain hierarchical Markov model to realize texture image segmentation in Document 1 "Texture Segmentation Based on Wavelet Domain Hierarchical Markov Model" (Journal of Wuhan University·Information Science Edition, 2009, 34(5)). Although the wavelet tr...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00
Inventor 任洪娥王海丰
Owner NORTHEAST FORESTRY UNIVERSITY
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