Method for segmenting HMT image on the basis of nonsubsampled Contourlet transformation

Abstract

The invention discloses a method for segmenting HMT images which is based on the nonsubsampled Contourlet transformation. The method mainly solves the problem that the prior segmentation method has poor area consistency and edge preservation, and comprises the following steps: (1) performing the nonsubsampled Contourlet transformation to images to be segmented and training images of all categories to obtain multi-scale transformation coefficients; (2) according to the nonsubsampled Contourlet transformation coefficients of the training images and the hidden markov tree which represents the one-to-one father and son state relationship, reckoning the model parameters; (3) calculating the corresponding likelihood values of the images to be segmented in all scale coefficient subbands, and classifying by examining possibility after integrating a labeled tree with a multi-scale likelihood function to obtain the maximum multi-scale; (4) updating category labels for each scale based on the context information context-5 model; and (5) with the consideration of the markov random field model and the information about correlation between two adjacent pixel spaces in the images to be segmented, updating the category labels to obtain the final segmentation results. The invention has the advantages of good area consistency and edge preservation, and can be applied to the segmentation for synthesizing grainy images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to the application of a multi-scale geometric analysis technology in the field of image segmentation, in particular to an image segmentation method, which can be used for the segmentation processing of texture images. Background technique [0002] Image segmentation is an important image processing technique. In the research and application of images, people are often interested in some parts of the image, which generally correspond to specific regions with unique properties in the image. In order to identify and analyze the targets, they need to be separated and extracted, and on this basis, it is possible to further process the targets. Image segmentation is the technology and process of dividing the image into regions with different characteristics and extracting the target of interest. Here, the feature can be the gray scale, color, texture, etc. of the pixel, and the c...

AI Technical Summary

Problems solved by technology

This method has good segmentation results for texture images, aerial images and SAR images, and the edge information of the images is well maintained, but the segmentation effect for uniform regions is not good.

Images