Abstract
A popular framework for the interpretation of image sequences is based on the layered model; see e.g. Wang and Adelson [8], Irani et al. [2]. Jojic and Frey [3] provide a generative probabilistic model framework for this task. However, this layered models do not explicitly account for variation due to changes in the pose and self occlusion. In this paper we show that if the motion of the object is large so that different aspects (or views) of the object are visible at different times in the sequence, we can learn appearance models of the different aspects using a mixture modelling approach.
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© 2005 Springer-Verlag Berlin Heidelberg
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Titsias, M.K., Williams, C.K.I. (2005). Unsupervised Learning of Multiple Aspects of Moving Objects from Video. In: Bozanis, P., Houstis, E.N. (eds) Advances in Informatics. PCI 2005. Lecture Notes in Computer Science, vol 3746. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573036_71
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DOI: https://doi.org/10.1007/11573036_71
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29673-7
Online ISBN: 978-3-540-32091-3
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