Image Segmentation Method Based on Probability Map Model in Stationary Directional Wave Domain
A probabilistic graph model and directional wave domain technology, applied in the field of image processing, can solve the problems of not fully mining the texture information of the image to be segmented and the obvious effect of image misclassification, so as to maintain consistency, improve the correct rate, and improve the accuracy Effect
Active Publication Date: 2016-08-17
XIDIAN UNIV
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Although this method has the advantages of accurate edges and good regional consistency, it still has the disadvantage that the method does not fully exploit the texture information in the image to be segmented when extracting the training image, resulting in a more obvious misclassification effect in the segmented image.
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[0038] The present invention will be further described below in conjunction with the accompanying drawings.
[0039] Refer to attached figure 1 , the concrete steps of the present invention are as follows:
[0040] Step 1: Input image.
[0041] Input an optional image to be segmented. The images to be segmented used in the embodiment of the present invention are respectively as attached figure 2 (a) and attached image 3 (a) shown. Among them, attached figure 2 (a) is a second-class texture image selected from the Brodatz texture image library, with a size of 256×256, attached image 3 (a) are three types of texture images selected from the Brodatz texture image library, with a size of 256×256.
[0042] Step 2: Calculate the eigenvectors.
[0043] Perform multi-scale stationary directional wave transformation on the image to be segmented to obtain low-frequency sub-band coefficients and high-frequency sub-band coefficients of different scales. The implementation step...
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The invention discloses a smooth direction wave domain probability graph model-based image segmentation method and solves the problems that in the prior art, directionality information of an image to be segmented cannot be sufficiently mined, and training image blocks containing more image information cannot be extracted. The method comprises the implementation steps of (1) inputting the image; (2) calculating feature vectors; (3) extracting the training image blocks; (4) solving prior probability; (5) building a hidden Markov chain model parameter set; (6) updating the hidden Markov chain model parameter set; (7) solving a maximum likelihood value; (8) obtaining a final segmented image; (9) outputting the segmented image. The smooth direction wave domain probability graph model-based image segmentation method has the advantages of high edge accuracy and area consistency and can be used for acquiring an area-of-interest in object identification.
Description
technical field [0001] The invention belongs to the technical field of image processing, and further relates to an image segmentation method based on a stationary directional wave domain probability map model in the technical field of image segmentation. The invention can be applied to the acquisition of interest regions in object recognition. Background technique [0002] Image segmentation is a key technology in the field of image processing and computer vision. It is a key step from image processing to image analysis. The quality of the segmentation results directly affects the subsequent image analysis, understanding and solving problems. The purpose of image segmentation is to divide the image into different regions with different characteristics and extract the objects of interest. The feature here can be the grayscale, color, or texture of the pixel, and the corresponding target can be a single area or multiple areas. [0003] In recent years, as a directed acyclic...
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IPC IPC(8): G06T7/00G06K9/62
Inventor 白静焦李成韩雪云马文萍马晶晶王爽赵佳琦张向荣
Owner XIDIAN UNIV