Conference proceeding
Local Statistical Active Contour Energy Functional based on Cauchy-Schwarz Divergence for Image Segmentation
2019 Chinese Control Conference (CCC), Vol.2019-, pp.3508-3513
07/2019
DOI: 10.23919/ChiCC.2019.8866119
Abstract
Active contour model is a widely used method for image segmentation. However, traditional active contour models often fail to segment objects in images with intensity inhomogeneity. In this paper, a local statistical active contour energy functional is proposed for image segmentation. It employs Cauchy-Schwarz divergence as the statistical distance measuring the difference between the estimated probability density inside and outside the evolving contour, which extracts local intensity information to guide the evolution of the contour. The devised energy functional can well integrate with traditional active contour model, thus it can help achieve improvement for traditional active contour models when they are used for segmenting objects out of intensity inhomogeneous backgrounds. Experimental results on synthetic and real images show that the proposed local energy functional improves the performance of traditional active contour models in images with intensity inhomogeneity.
Details
- Title: Subtitle
- Local Statistical Active Contour Energy Functional based on Cauchy-Schwarz Divergence for Image Segmentation
- Creators
- Muchao Ye - South China University of TechnologyMing Dai - South China University of TechnologyZhiheng Zhou - South China University of TechnologyRuzheng Zhao - Tencent
- Resource Type
- Conference proceeding
- Publication Details
- 2019 Chinese Control Conference (CCC), Vol.2019-, pp.3508-3513
- Publisher
- Technical Committee on Control Theory, Chinese Association of Automation
- DOI
- 10.23919/ChiCC.2019.8866119
- ISSN
- 1934-1768
- eISSN
- 2161-2927
- Language
- English
- Date published
- 07/2019
- Academic Unit
- Computer Science
- Record Identifier
- 9984696579002771
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