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Learning Hierarchical Feature Representation in Depth Image

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Computer Vision -- ACCV 2014 (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9005))

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Abstract

This paper presents a novel descriptor, geodesic invariant feature (GIF), for representing objects in depth images. Especially in the context of parts classification of articulated objects, it is capable of encoding the invariance of local structures effectively and efficiently. The contributions of this paper lie in our multi-level feature extraction hierarchy. (1) Low-level feature encodes the invariance to articulation. Geodesic gradient is introduced, which is covariant with the non-rigid deformation of objects and is utilized to rectify the feature extraction process. (2) Mid-level feature reduces the noise and improves the efficiency. With unsupervised clustering, the primitives of objects are changed from pixels to superpixels. The benefit is two-fold: firstly, superpixel reduces the effect of the noise introduced by depth sensors; secondly, the processing speed can be improved by a big margin. (3) High-level feature captures nonlinear dependencies between the dimensions. Deep network is utilized to discover the high-level feature representation. As the feature propagates towards the deeper layers of the network, the ability of the feature capturing the data’s underlying regularities is improved. Comparisons with the state-of-the-art methods reveal the superiority of the proposed method.

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Acknowledgment

This work is supported by NSFC (Grant No 61300161, 61371168 and 61273251), Doctoral Fund of Ministry of Education of China (Grant No 20133219120033), Open Project Program of Jiangsu Key Laboratory of Image and Video Understanding for Social Safety (Grant No JSKL201306) and Programme of Introducing Talents of Discipline to Universities (Grant NoB13022).

Author information

Authors and Affiliations

  1. Nanjing University of Science and Technology, Nanjing, China

    Yazhou Liu & Quansen Sun

  2. Panasonic R&D Center Singapore, Singapore, Singapore

    Pongsak Lasang

  3. Carnegie Mellon University, Pittsburgh, USA

    Mel Siegel

Authors
  1. Yazhou Liu
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  2. Pongsak Lasang
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  3. Quansen Sun
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  4. Mel Siegel
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Corresponding author

Correspondence to Yazhou Liu .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Bayern, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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© 2015 Springer International Publishing Switzerland

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Liu, Y., Lasang, P., Sun, Q., Siegel, M. (2015). Learning Hierarchical Feature Representation in Depth Image. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9005. Springer, Cham. https://doi.org/10.1007/978-3-319-16811-1_39

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  • DOI: https://doi.org/10.1007/978-3-319-16811-1_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16810-4

  • Online ISBN: 978-3-319-16811-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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