Abstract
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.
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Inagaki, S., Imiya, A. (2014). Semantic Segmentation of Low Frame-Rate Image Sequence Using Statistical Properties of Optical Flow for Remote Exploration. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8887. Springer, Cham. https://doi.org/10.1007/978-3-319-14249-4_45
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DOI: https://doi.org/10.1007/978-3-319-14249-4_45
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14248-7
Online ISBN: 978-3-319-14249-4
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