Image segmentation algorithm based on ALR-CV model and edge transition

Abstract

The present invention discloses an image segmentation algorithm based on an ALR (Automatic Local Ratio)-CV (Chan Vese) model and edge transition. The algorithm comprises the following steps of: 1) obtaining image edge information; 2) calculating and obtaining a distance map of a binary system edge mask according to the binary system edge mask of the edge information; 3) constructing an ETM (Edge Conversion Map) (x, y) according to the distance map; 4) minimizing an energy functional ECV of a CV model, obtaining an equation (2) of a level set function, removing regular terms in the equation (2)to obtain an equation (3), dividing the sides of the equal sign of the equation (3) by an energy item weight coefficient [Lambda]1, performing normalization in the execution process, obtaining an equation (6), replacing a general ratio of the energy item weight coefficient in the original CV model with a local ratio of the energy item weight coefficient, and obtaining an ALR-CV model; and 5) employing the ALR-CV model to segment the edge conversion map ETM (x, y). The automatic local ratio is introduced to automatically perform parameter regulation of the CV model to enhance segmentation performances of the CV model, so that curve evolution cannot be forced to always stay at a fixed position, and part of boundaries can be flexibly neglected.

Description

technical field [0001] The invention relates to the field of image segmentation algorithms, in particular to an image segmentation algorithm based on ALR-CV model and edge conversion. Background technique [0002] Flying bird recognition is a very important link in the bird repelling system, and computer vision recognition is one of the commonly used methods; image segmentation is an important task in computer vision and a key step in image processing. In recent years, with the rapid development of computer technology, image segmentation technology has made great progress, and has been successfully applied in remote sensing, medicine, astronomy, industry and other fields; at the same time, the quality of image segmentation directly determines the subsequent image analysis. , image recognition and other aspects of performance, so how to accurately and efficiently detect the target object in the image is the top priority of image segmentation. [0003] In recent years, many e...

AI Technical Summary

Problems solved by technology

The document "Zhang Aihua, Wang Fan, Chen Haiyan. Plateau pika image segmentation based on improved CV model [J]. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2017,45(8):32-37." discloses the third method The improved Local Binary Fitting (LBF) model, by introducing a kernel function to define the energy functional, extends SBGFRLS by using additional area information, which can achieve better segmentation performance than SBGFRLS, but because each iteration core operations, the cost of which increases significantly

The latter two improved models based on the Chan Vese model have a certain segmentation effect, and can realize the extraction of part of the target contour in the image, but because the Euclidean distance is used to measure the distance between the position point and the fitting center, the distance measurement cannot be done well. Identify the differences in pixels, noise points and real pixels may be counted

Images