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2017 | OriginalPaper | Buchkapitel

Parametric Image Segmentation of Humans with Structural Shape Priors

verfasst von : Alin-Ionut Popa, Cristian Sminchisescu

Erschienen in: Computer Vision – ACCV 2016

Verlag: Springer International Publishing

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Abstract

The figure-ground segmentation of humans in images captured in natural environments is an outstanding open problem due to the presence of complex backgrounds, articulation, varying body proportions, partial views and viewpoint changes. In this work we propose class-specific segmentation models that leverage parametric max-flow image segmentation and a large dataset of human shapes. Our contributions are as follows: (1) formulation of a sub-modular energy model that combines class-specific structural constraints and data-driven shape priors, within a parametric max-flow optimization methodology that systematically computes all breakpoints of the model in polynomial time; (2) design of a data-driven class-specific fusion methodology, based on matching against a large training set of exemplar human shapes (100,000 in our experiments), that allows the shape prior to be constructed on-the-fly, for arbitrary viewpoints and partial views.

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Notice, however, that the methodology we propose is also applicable to other categories than people. Here we focus on humans because for now, large training sets of segmented shapes with structural annotations are available only for them, through Human3.6M [7]. But, as large datasets for other object categories emerge, we expect our methodology to generalize well. In this respect, our results on a challenging visual category, humans, are indicative of the performance bounds one can expect.

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Titel: Parametric Image Segmentation of Humans with Structural Shape Priors
verfasst von: Alin-Ionut Popa
Cristian Sminchisescu
Verlag: Springer International Publishing
Buch: Computer Vision – ACCV 2016
Print ISBN: 978-3-319-54183-9

Electronic ISBN: 978-3-319-54184-6

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-54184-6_5

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