Abstract
We present a new parallel approach for the computation of an optical flow field from a video image sequence. This approach incorporates the various local smoothness, spatial and temporal coherence constraints transparently by the application of fuzzy image processing techniques. Our Vector Coherence Mapping VCM approach accomplishes this by a weighted voting process in “local vector space,” where the weights provide high level guidance to the local voting process. Our results show that VCM is capable of extracting flow fields for video streams with global dominant fields (e.g. owing to camera pan or translation, moving camera and moving object(s), and multiple moving objects. Our results also show that VCM is able to operate under strong image noise and motion blur, and is not susceptible to boundary oversmoothing.
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© 1997 Springer-Verlag Berlin Heidelberg
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Quek, F.K.H., Bryll, R.K. (1997). Vector coherence mapping: A parallelizable approach to image flow computation. In: Chin, R., Pong, TC. (eds) Computer Vision — ACCV'98. ACCV 1998. Lecture Notes in Computer Science, vol 1352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63931-4_266
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DOI: https://doi.org/10.1007/3-540-63931-4_266
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