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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Titsias, M. K., & Williams, C. K. I. (2005). Unsupervised learning of multiple aspects of moving objects from video. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3746 LNCS, pp. 746–756). https://doi.org/10.1007/11573036_71
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