Level set image segmentation method based on self-adaptive parameter

Abstract

The invention discloses a level set image segmentation method based on a self-adaptive parameter, for mainly solving the problem that various parameters in a curved surface evolution equation of a conventional level set image segmentation algorithm need to be preset in advance. The realization steps comprise: (1), inputting an image to be segmented; (2), setting a time step length, and giving iteration frequency; (3), carrying out Gauss filtering processing on the input image; (4), adding the self-adaptive parameter to a level set evolution equation to replace an original parameter constant; (5), starting level set iteration operation; (6), determining whether the iteration frequency reaches an upper limit or is convergent; and (7), determining whether a termination condition is achieved, if not, returning to step (5), and otherwise, outputting a segmentation result graph. The method provided by the invention has the advantages of short time, accurate segmentation result and high stability, thereby being capable of application in such technical fields as image enhancement, mode identification and object tracking.

Description

Technical field [0001] The invention relates to the field of image processing, and can be used in the technical fields of image segmentation, pattern recognition, target tracking and the like, and particularly relates to the application of level set methods in the field of image segmentation. Background technique [0002] Image segmentation is one of the most basic and important steps in the field of image processing and computer vision. Image segmentation refers to the segmentation of the input image into independent regions based on the image’s grayscale, color, texture and other characteristics, so that the same region has the same attributes, and different regions have different attributes, so that the target region can be removed from the image Separated out. Image segmentation has a wide range of applications. Any work that requires image target extraction and measurement cannot be separated from image segmentation. In the field of intelligent transportation, image segmen...

AI Technical Summary

Problems solved by technology

Parameters must be set during initialization, which takes a lot of training time and slows down curve convergence

[0006] Due to the shortcomings of the image segmentation method of the above-mentioned traditional level set method, the application of the level set algorithm in image segmentation is limited. Therefore, it is an urgent task for those skilled in the art to study an effective image segmentation method.

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