Horizontal set image segmentation method fusing uneven local gray scale and local variance

Abstract

The invention discloses a horizontal set image segmentation method fusing uneven local gray scale and local variance. A problem that segmentation effects of images with uneven gray scales should be improved in the prior art is solved. The method comprises following steps of 1, establishing a mathematic model with the uneven gray scale for features of an image with the uneven gray scale; 2, drawing image gray scale distribution through a Gauss distribution function; 3, processing an original image through low-pass filtering; 4, establishing an image local data item according to the step 3, establishing a regularization item simultaneously and constructing an overall level set energy function by combining a local statistical item established in the step 2; and 5, converting the energy function into a partial differential equation according to the Euler-Lagrange theorem, thereby achieving minimization of the energy function through gradient descent. According to the invention, in the improved model with the uneven gray scale, difference between the original image and an estimated image is considered, problems in the improved model with the uneven gray scale of the traditional image is analyzed and segmentation results are improved after the difference is introduced.

Description

technical field [0001] The invention relates to an image processing method, in particular to a level set image segmentation method which combines local gray level inhomogeneity and local variance. Background technique [0002] Image segmentation is an old and classic problem in the field of computer vision. Its fundamental purpose is to automatically obtain the target areas of interest through computers and extract these areas from complex backgrounds. It is widely used in medical diagnosis, video surveillance, remote sensing image analysis, text recognition, robot vision system and so on. At the same time, with the continuous development of imaging technology, people have acquired a huge amount and various types of image information through imaging, ranging from cosmic celestial bodies to microscopic particles, which also makes it difficult to use a general algorithm to process and solve All image segmentation problems have resulted in many classical image segmentation met...

AI Technical Summary

Problems solved by technology

[0005] The present invention overcomes the problem in the prior art that the segmentation effect of uneven gray scale images still needs to be improved, and provides a level set image segmentation that can further improve the segmentation effect of uneven gray scale images and integrate local uneven gray scale and local variance. method

Images