Abstract
A new robust matching algorithm for motion detection and computation of precise estimates of motion vectors of moving objects in a sequence of images is presented. Common matching algorithms of dynamic image analysis usually utilize local smoothness constraints. The proposed method exploits global motion smoothness. The suggested matching algorithm is robust to motion discontinuity as well as to noise degradation of a signal. Computer simulation and experimental results demonstrate an excellent performance of the method in terms of dynamic motion analysis.
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References
Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artificial Intelligence 17, 185–204 (1981)
Watson, A.B., Ahumada, A.J.: Motion: Perception and Representation. In: Tsotsos, J.K. (ed.), pp. 1–10 (1983)
Ohta, Y., Kanade, T.: Stereo by intra- and inter-scanline search using dynamic programming. IEEE Trans. Pattern Anal. Machine Intell. 7, 139–154 (1985)
Anandan, P.: Measuring Visual Motion from Image Sequences. PhD thesis, Univ. of Massachusetts, Amherst (1987)
Heitz, F., Bouthemy, P.: Multimodal estimation of discontinuous optical flow using Markov random fields. IEEE Transactions on Pattern Analysis and Machine Intelligence 15, 1213–1232 (1993)
Kanade, T., Okutomi, M.: A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment. IEEE Trans. Pattern Analysis and Machine Intelligence 16, 920–932 (1994)
Cedaras, C., Shah, M.: Motion based recognition: A survey. Image and Vision Computing 13, 129–154 (1995)
Anthony, Y.K.H., Pong, T.C.: Cooperative fusion of stereo and motion. Pattern Recognition 28, 553–562 (1995)
Chung, H.Y., Yung, N.H.C., Cheung, P.Y.S.: Fast motion estimation with search-center prediction. Optical Engineering 40, 952–963 (2001)
Petrakis, E.G.M., Diplaros, A., Milios, E.: Matching and retrieval of distorted and occluded shapes using dynamic programming. IEEE Trans. Pattern Anal. Machine Intell. 24, 1501–1516 (2002)
Mozerov, M., Kober, V., Tchernykh, A., Choi, T.S.: Motion estimation with a modified dynamic programming. Optical Engineering 41, 2592–2598 (2002)
Mozerov, M., Kober, V.: Motion Estimation Based on Hidden Segmentation. IEICE Transaction on Fund. E88-A, 376–381 (2005)
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Mozerov, M., Kober, V. (2005). A Robust Matching Algorithm Based on Global Motion Smoothness Criterion. In: Sanfeliu, A., Cortés, M.L. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2005. Lecture Notes in Computer Science, vol 3773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11578079_31
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DOI: https://doi.org/10.1007/11578079_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29850-2
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