Abstract
In this paper, we address the topic of estimating two-frame dense optical flow from the monogenic curvature tensor. The monogenic curvature tensor is a novel image model, from which local phases of image structures can be obtained in a multi-scale way. We adapt the combined local and global (CLG) optical flow estimation approach to our framework. In this way, the intensity constraint equation is replaced by the local phase vector information. Optical flow estimation under the illumination change is investigated in detail. Experimental results demonstrate that our approach gives accurate estimation and is robust against noise contamination. Compared with the intensity based approach, the proposed method shows much better performance in estimating flow fields under brightness variations.
This work was supported by German Research Association (DFG) Graduiertenkolleg No. 357 (Di Zang), DFG grant So-320/4-2 (Lennart Wietzke), DFG grant We-2602/5-1 (Christian Schmaltz) and DFG grant So-320/2-3 (Gerald Sommer).
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Zang, D., Wietzke, L., Schmaltz, C., Sommer, G. (2007). Dense Optical Flow Estimation from the Monogenic Curvature Tensor. In: Sgallari, F., Murli, A., Paragios, N. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2007. Lecture Notes in Computer Science, vol 4485. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72823-8_21
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DOI: https://doi.org/10.1007/978-3-540-72823-8_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72822-1
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