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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Kong, H., Audibert, J.-Y., Ponce, J.: General road detection from a single image. IEEE Trans. Image Processing 19, 2211–2220 (2010)
Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE PAMI 26, 1124–1137 (2004)
Beauchemin, S.S., Barron, J.L.: The computation of optical flow. ACM Computer Surveys 26, 433–467 (1995)
Fischer, B., Modersitzki, J.: Ill-posed medicine- an introduction to image registration. Inverse Problem 24, 1–17 (2008)
Varga, R.S.: Matrix Iteration Analysis, 2nd edn. Springer (2000)
Hwan, S., Hwang, S.-H., Lee, U.K.: A hierarchical optical flow estimation algorithm based on the interlevel motion smoothness constraint. Pattern Recognition 26, 939–952 (1993)
Brox, T., Malik, J.: Large displacement optical flow: descriptor matching in variational motion estimation. IEEE PAMI 33, 500–513 (2011)
Ohnishi, N., Imiya, A.: Featureless robot navigation using optical flow. Connection Science 17, 23–46 (2005)
Braillon, C., Pradalier, C., Crowley, J.L., Laugier, C.: Real-time moving obstacle detection using optical flow models. In: Intelligent Vehicles Symposium, pp. 466–471 (2006)
Liang, B., Pears, N.: Visual navigation using planar homographies. In: Proc. IEEE ICRA 2002, pp. 205–210 (2002)
Young-Geun, K., Hakil, K.: Layered ground floor detection for vision-based mobile robot navigation. In: Proc. IEEE ICRA 2004, pp. 13–18 (2004)
Ohnishi, N., Imiya, A.: Dominant plane detection from optical flow for robot navigation. Pattern Recognition Letters 27, 1009–1021 (2006)
Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Comm. of the ACM 24, 381–395 (1981)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press (2000)
Pinggera, P., Franke, U., Mester, R.: Highly accurate depth estimation for objects at large distances. In: Weickert, J., Hein, M., Schiele, B. (eds.) GCPR 2013. LNCS, vol. 8142, pp. 21–30. Springer, Heidelberg (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
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
eBook Packages: Computer ScienceComputer Science (R0)