Semantic Segmentation of Low Frame-Rate Image Sequence Using Statistical Properties of?Optical Flow for Remote Exploration

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• 作者：Shun Inagaki (27)
  Atsushi Imiya (28)
  
• 刊名：Lecture Notes in Computer Science
• 出版年：2014
• 出版时间：2014
• 年：2014
• 卷：8887
• 期：1
• 页码：477-488
• 全文大小：451 KB
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• 作者单位：Shun Inagaki (27)
  Atsushi Imiya (28)
  
  27. School of Advanced Integration Science, Chiba University, Yayoi-cho 1-33, Inage-ku, Chiba, 263-8522, Japan
  28. Institute of Management and Information Technologies, Chiba University, Yayoi-cho 1-33, Inage-ku, Chiba, 263-8522, Japan
  
• ISSN：1611-3349

文摘

For the application of well-established image analysis algorithms to low frame-rate image sequences, which are common in bio-imaging and long-distance extrapolation, we are required to up-convert the frame-rate of image sequences. For the motion analysis of low frame-rate image sequences, we introduce a two-step method for semantic segmentation of the dominant plane, which is the largest planar area on an image plane, from a low frame-rate image sequence. The algorithm first extracts candidate pixels using statistics of optical flow vectors derived by temporal optical flow super-resolution. Subsequently, the algorithm extracts a planar region by semantic labelling, accepting these candidate pixels as seed points. The minimisation of the semantic segmentation is achieved by the graph-cut method.