Image edge extraction algorithm based on coverage segmentation and active contour model

Abstract

The invention discloses an image edge extraction algorithm based on coverage segmentation and an active contour model. The image edge extraction algorithm mainly comprises, from coarse to fine, a coverage segmentation method and an active contour model. The method comprises the steps as follows: first, segmenting an image; then, getting pixels intersecting with the boundary of a continuous imaging object through use of a morphological edge detection operator; next, getting a new coverage value through an improved coverage segmentation method; and finally, extracting the boundary of the image using an active contour model. In the coverage segmentation method, a space position information item is added, and coverage values are reassigned to boundary pixels which may be segmented wrongly, so that the accuracy and anti-noise ability of the segmentation result are improved. The active contour model effectively overcomes the defect that only image area information is used in a classic C-V model and the image edge may be located inaccurately, and a more ideal image edge can be obtained. Experimental results show that the result of edge extraction using the algorithm is better for an image with a fuzzy boundary.

Description

technical field [0001] The invention relates to coverage segmentation, active contour image segmentation, and image edge extraction, in particular to an algorithm for image edge extraction for objects with fuzzy boundaries. Background technique [0002] Image segmentation is one of the most important contents in the field of computer vision. It is the primary problem to realize automatic image analysis and pattern recognition, and it is also one of the classic problems in image processing. Image segmentation refers to the technology and process of dividing an image into regions with different characteristics and extracting objects of interest. Image boundary extraction is generally considered to be both the most important and the most challenging task in image processing. So far, various algorithms have been proposed to solve the difficult problem of image segmentation. Researchers have also made great efforts to improve the performance of image segmentation algorithms. ...

AI Technical Summary

Problems solved by technology

[0006] The main purpose of the present invention is to propose a coarse-to-fine image edge extraction method to improve the accuracy of target object edge extraction in view of the unsatisfactory segmentation effect of traditional algorithms on images with blurred borders and the problem of many burrs in the segmentation results.

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