Method for segmenting moveable outline model image based on edge flow

Abstract

The invention provides a method for segmenting a moveable outline model image based on edge flow and belongs to the field of image processing. The method comprises the following steps: extracting initial image outline information, namely performing gaussian mixed distribution statistics on the image information, judging a foreground area and a background area by utilizing a corresponding gaussian mixed distribution function of the image information, removing the background area and maintaining the foreground area, so as to obtain the initial image outline information; and evolving the initial image outline information, namely evolving the initial image outline information till an evolving energy function reaches a minimum value by adopting a moveable outline model technique based on the edge flow, so as to obtain final segmented information of the image. The method disclosed by the invention meets the demand on boundary continuity of a segmenting target; the problems of weak image boundaries and unsatisfactory segmenting effect of gray level progressing are solved; and the method disclosed by the invention has the advantages of high segmenting efficiency and high precision, is slightly influenced by noise, so that a segmenting effect with high robustness can be obtained.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a method for image segmentation of an active contour model based on edge flow. Background technique [0002] More than 80% of the external information that human beings receive comes from vision. It can be seen that vision is the most important way of information exchange for human beings. With the emergence of signal processing theory and computers, people try to imitate the human visual process by using a camera to obtain image information and convert it into a digital signal, that is, use a computer to achieve image processing. Image segmentation is the basis of image processing. Through segmentation technology, objects of interest can be extracted from complex backgrounds, making higher-level image analysis and understanding possible. Image segmentation technology has a very wide range of applications, covering almost every field of image processing. In general, as long as i...

AI Technical Summary

Problems solved by technology

Data-driven image segmentation technology can directly operate on the current image. Although some methods also use information related to the target to be segmented, they do not rely on this information as a whole, such as the relatively mature Canny operator segmentation, threshold Segmentation and fuzzy clustering segmentation, etc.; as the complexity of the problem to be solved increases, more and more high-level knowledge is required to be integrated into image segmentation, and then there is a model-driven image segmentation technology, which directly establishes Based on the relevant information of the target to be segmented, the relevant information is mainly the understanding of the image to be segmented. Adding this knowledge can make the segmentation method have certain intelligence. For example, the image segmentation technology based on the active contour model is based on the target to be segmented. The closedness of the boundary and the absence of fracture are prior knowledge, but most of the active contour models that have emerged so far are based on image edge information or image area information, and the active contour model based on image edge information is difficult to segment the homogeneous area, and the active contour model based on image area information may produce inaccurate image edge positioning defects in some segmentation applications, especially for images with uneven gray levels

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