Active contour evolved by joint probability classification on Riemannian manifold

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• 作者：Qi Ge ; Fumin Shen ; Xiao-Yuan Jing ; Fei Wu…
• 关键词：Image segmentation ; Joint probability classification ; Active contour ; Nonparametric distribution ; Riemannian manifold
• 刊名：Signal, Image and Video Processing
• 出版年：2016
• 出版时间：October 2016
• 年：2016
• 卷：10
• 期：7
• 页码：1257-1264
• 全文大小：2,856 KB
• 刊物类别：Engineering
• 刊物主题：Signal,Image and Speech Processing
  Image Processing and Computer Vision
  Computer Imaging, Vision, Pattern Recognition and Graphics
  Multimedia Information Systems
  
• 出版者：Springer London
• ISSN：1863-1711
• 卷排序：10

文摘

In this paper, we present an active contour model for image segmentation based on a nonparametric distribution metric without any intensity a priori of the image. A novel nonparametric distance metric, which is called joint probability classification, is established to drive the active contour avoiding the instability induced by multimodal intensity distribution. Considering an image as a Riemannian manifold with spatial and intensity information, the contour evolution is performed on the image manifold by embedding geometric image feature into the active contour model. The experimental results on medical and texture images demonstrate the advantages of the proposed method.