关键词: 几何主动轮廓, Chan-Vese算法, 自动局部比, 边缘信息, 欧氏距离, 边缘变换图

Abstract: In order to realize the automatic displacing of the bird in the orchard under the environment of the Internet of things so that it can accurately drive the birds in the complex conditions, an image segmentation algorithm based on improved Chan-Vese model and edge conversion is proposed in this paper. Through the accurate identification of flying birds, it can provide accurate information for system automatically emitting ultrasonic waves to drive away flying birds. Firstly, the Canny operator is used to obtain the edge information of the bird image. And the Euclidean distance is used to get the distance map of binary edge. Then the edge conversion map is constructed by introducing S shape function. The automatic local ratio is introduced to improve the Chan-Vese model for accurately segmenting edge map. Experimental results show that this algorithm has higher segmentation accuracy compared with the SBGFRLS algorithm, G-CV algorithm and FAST EDGE algorithm, which the region matching rate is the highest, about 70%, while the root mean square error ratio is only 13% in the face of single target image segmentation, and the region matching rate is the highest, about 85%, while the root mean square error ratio is only 5% for the image segmentation with double targets.

Key words: geometric active contours, Chan-Vese algorithm, automatic local ratio, edge information, Euclidean distance, edge transform graph