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- W2181869181 abstract "Abstract Many problems in the fields of computer vision deal with image data that is embedded in very high-dimensional spaces. However, a typical assumption behind many algorithms is that the data lie on a low-dimensional manifold. Modeling the visual manifolds is quite challenging. Typically, image manifolds are neither smooth nor differentiable. This chapter presents the theory and applications of the concept of homeomorphic manifold analysis (HMA). Given a set of topologically equivalent manifolds, HMA models the variation in their geometries in the space of functions that map between a topologically equivalent common representation and each of them. This setting is suitable to different problems in visual learning. In particular, this chapter focuses on the applications of the framework to modeling the manifold of human motion in the image space." @default.
- W2181869181 created "2016-06-24" @default.
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- W2181869181 date "2015-01-01" @default.
- W2181869181 modified "2023-09-27" @default.
- W2181869181 title "Homeomorphic Manifold Analysis (HMA): Untangling Complex Manifolds" @default.
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- W2181869181 doi "https://doi.org/10.1016/bs.aiep.2014.12.002" @default.
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