Research On Image Segmentation Methods Of Ochotona Curzoniae Based On Level Set

Ochotona curzoniae is the key to species on the Tibetan plateau alpine meadow ecosystem and also it is a main kind of organism that destroyed the grassland ecology of the Tibetan plateau and neighborhoods. In order to prevent the dangers of the ochotona curzoniae and take useful prevention and control measures, we must research the ochotona curzoniae numbers and degree of hazared in this region. With the development of sensing technology and image processing, we can use intelligent monitoring system to detect and track the ochotona curzoniae so as to obtain ochotona curzoniae’s research data. These research data can provide new evidence for protecting grassland ecology. The ochotona curzoniae image segmentation become problem in the intelligent monitoring process, because of the ochotona curzoniae image possess the characteristics of complex background, low contrast, intensity inhomogeneity and much noise.According to the characteristics of the ochotona curzoniae image, an image segmentation method is proposed which be made up of local intensity information and global gradient information. As the local binary fitting model has a problem of falling into the local minimums easily during the process of evolution, the thesis construct the new energy function of improved local binary fitting model which linearly assemble local intensity energy and global gradient energy by consulting the thought of CV model energy function construction and leading into the global image gradient information. Under the local intensity information and global gradient information common action, the level set function could avoid falling into the local optimum. Through the contrast experiment, the improved local binary fitting model has advantages of high segmentation precision and less time consuming compared to local binary fitting model and RSF model. The experimental results show that the proposed method can not only improve segmentation accuracy but also has a positive effect on background suppression and contour location.Aiming at the characteristics of the ochotona curzoniae video sequence image, a space-time joint segmentation method is proposed whcich combined with fast target detecting algorithm and improved local binary fitting model. We can get initial active contour curve of the improved local binary fitting model through the fast target detecting algorithm in time domain. When we confirm the initial active contour curve of moving target, the initial active contour curve of the first frame ochotona curzoniae video sequence image is obtained by interactive segmentation, then judging the state of moving target by two adjacent framedifference and determining initial active contour curve of the current frame ochotona curzoniae image. The experimental results show that the space-time joint segmentation methods can get initial active contour curve accurately and quickly. Also the method make up for the disadvantages of improved local binary fitting model which obtain initial active contour curve by hand and it can divide accurately the ochotona curzoniae video sequence image continuous.