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 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, which is common for image sequence obtained by remote extrapolation.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
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. Intelligent Vehicles Symposium, 466–471 (2006)
Liang, B., Pears, N.: Visual navigation using planar homographies. In: IEEE International Conference on Robotics and Automation, pp. 205–210 (2002)
Young-Geun, K., Hakil, K.: Layered ground floor detection for vision-based mobile robot navigation. In: International Conference on Robotics and Automation, pp. 13–18 (2004)
Fischer, B., Modersitzki, J.: Ill-posed medicine- an introduction to image registration. Inverse Problem 24, 1–17 (2008)
Vardy, A., Moller, R.: Biologically plausible visual homing methods based on optical flow techniques. Connection Science 17, 47–89 (2005)
Beauchemin, S.S., Barron, J.L.: The computation of optical flow. ACM Computer Surveys 26, 433–467 (1995)
Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artificial Intelligence 17, 185–204 (1991)
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)
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 (1991)
Varga, R.S.: Matrix Iteration Analysis, 2nd edn. Springer (2000)
Brox, T., Malik, J.: Large displacement optical flow: descriptor matching in variational motion estimation. IEEE Trans. PAMI 33, 500–513 (2011)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press (2000)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Sys., Man., Cyber. 9, 62–66 (1979)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Inagaki, S., Imiya, A. (2014). Statistical Method for Semantic Segmentation of Dominant Plane from Remote Exploration Image Sequence. In: Fränti, P., Brown, G., Loog, M., Escolano, F., Pelillo, M. (eds) Structural, Syntactic, and Statistical Pattern Recognition. S+SSPR 2014. Lecture Notes in Computer Science, vol 8621. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44415-3_27
Download citation
DOI: https://doi.org/10.1007/978-3-662-44415-3_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-44414-6
Online ISBN: 978-3-662-44415-3
eBook Packages: Computer ScienceComputer Science (R0)