Image segmentation method combining cloud model and level set

Abstract

The invention relates to an image segmentation method combining a cloud model and a level set, which comprises the steps of S1, acquiring a histogram of an image, and performing curve fitting on the histogram; S2, dividing valley value intervals through the fitted curve; S3, acquiring three digital characteristics Ex, En and He by using a reverse Gaussian cloud algorithm, and obtaining a foreground cloud model or a background cloud model through software or cloud; and S4, constructing an energy function, and solving the energy function to obtain a segmentation result. The image segmentation method combines a cloud model and a level set algorithm, and de-linearization processing is performed on boundaries in the image by using the cloud model, so that the occurrence probability and the degree of a problem of instable convergence caused by manual intervention are reduced, and the convergence corresponding to a level set function is enabled to be accelerated; and meanwhile, the operationof initializing the level set function by the aid of a cloud model algorithm effectively reduces the noise sensitivity of the function and solves a problem of continuously initializing the level set function in the segmentation process.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a cloud model image segmentation method, a level set image segmentation method and the combination of the two methods. Background technique [0002] At present, many famous scholars at home and abroad are committed to the research of image segmentation algorithms. There are mainly four image segmentation methods: threshold-based, edge detection-based, region-based, and energy-based segmentation. [0003] Threshold-based segmentation methods mainly include histogram concave analysis method, maximum inter-class variance method, threshold interpolation method, etc. This type of method is intuitive, simple and efficient, but due to the complexity of the image, the selection of threshold value has become a major challenge for this type of method; The more classic algorithms in the segmentation method based on edge detection include Sobel, Prewitt, Laplace and Canny operator, ...

AI Technical Summary

Problems solved by technology

An image segmentation method combining cloud model and level set is proposed to effectively reduce the noise sensitivity of the function itself and deal with the problem of poor image segmentation.

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