Image segmentation method based on two-channel texture segmentation active contour model

Abstract

The invention discloses an image segmentation method based on a two-channel texture segmentation active contour model in the technical field of digital picture processing. The image segmentation method based on the two-channel texture segmentation active contour model comprises the steps that the gray level, the horizontal gradient field and the vertical gradient field of each pixel in an image are extracted; textural features corresponding to the gray level, the horizontal gradient field and the vertical gradient field of each pixel in the image are calculated; a gray feature channel and an edge feature channel are obtained according to the textural features; the two-channel texture segmentation active contour model is created; a texture segmentation model is minimized through evolvement of a horizontal set function to complete image segmentation. The image segmentation method improves algorithm efficiency, avoids incorrect segmentation caused by gray information, and improves accuracy of an algorithm.

Description

technical field [0001] The invention belongs to the technical field of digital image processing, in particular to an image segmentation method based on a two-channel texture segmentation active contour model. Background technique [0002] Image segmentation, especially texture image segmentation, has always been an important content and difficult problem in the field of computer vision and digital image processing. Texture segmentation is to divide the target image into several non-overlapping regions according to the consistency of texture features in the image region. At present, the commonly used method is to first extract the feature information of the image, and then segment the image in the feature space according to a certain model. Among them, the active contour method based on level set theory has attracted the attention of researchers because it can automatically realize the splitting and merging of evolution curves, and is widely used in texture segmentation. ...

AI Technical Summary

Problems solved by technology

At present, the commonly used technology is to combine Gaussian fitting method, Wasserstein distance measurement method, local scale measurement method, etc. with the structure tensor, which has achieved good results in the segmentation of natural texture images. The problem, the processing of high-dimensional features makes the calculation speed of image segmentation slower

In addition, histogram features and some local information are also used for image segmentation. Due to the complexity and variety of natural texture images, all algorithm models can only be applied to specific types of texture images. How to improve the computational efficiency and segmentation performance of the algorithm has always been an issue Problems to be solved

Images