An improved Chan–Vese model by regional fitting for infrared image segmentation |
| |
Institution: | 1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan 650500, PR China;2. School of Information Science and Engineering, Yunnan University, Kunming, Yunnan 650500, PR China;3. Faculty of Science and Technology, University of Macau, Macau 999078, PR China;4. Yunnan Union Visual Innovation Technology, Kunming, Yunnan 650500, PR China |
| |
Abstract: | In this paper, a regional fitting method is proposed for infrared image segmentation. In our model, the intensity of each pixel in a region is described by using the sum of the class center and the weighted variance of the region, in order to build energy function for encouraging the similarity pixels to be clustered together. The adoption of such way can thereby eliminate the issue associated with the drift of the class center that is existed in Chan–Vese model. Particularly, followed by incorporating energy function into the level set evolution without re-initialization framework, the variational formulation can force the level set function to be closed to object boundaries. Experiments on some representative and real infrared images have demonstrated that our model has higher performance of segmentation in comparison with Chan–Vese model without re-initialization, and some existing methods, including LBF and LCV model. |
| |
Keywords: | Image segmentation Level set method Infrared image Curve evolution |
本文献已被 ScienceDirect 等数据库收录! |
|