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Feature-Based Methods in 3D Shape Analysis

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3D Imaging, Analysis and Applications

Abstract

The computer vision and pattern recognition communities have recently witnessed a surge in feature-based methods for numerous applications including object recognition and image retrieval. Similar concepts and analogous approaches are penetrating the world of 3D shape analysis in a variety of areas including non-rigid shape retrieval and matching. In this chapter, we present both mature concepts and the state-of-the-art of feature-based approaches in 3D shape analysis. In particular, approaches to the detection of interest points and the generation of local shape descriptors are discussed. A wide range of methods is covered including those based on curvature, those based on difference-of-Gaussian scale space, and those that employ recent advances in heat kernel methods.

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Notes

  1. 1.

    In some literature, this is also known as interest point detection or keypoint detection.

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Authors and Affiliations

  1. Department of Computer Science, Technion—Israel Institute of Technology, Haifa, 32000, Israel

    Alexander M. Bronstein & Michael M. Bronstein

  2. Department of Computer Science, Stanford University, Stanford, CA, USA

    Maks Ovsjanikov

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  1. Alexander M. Bronstein
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  2. Michael M. Bronstein
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  3. Maks Ovsjanikov
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Correspondence to Alexander M. Bronstein .

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Editors and Affiliations

  1. Department of Computer Science, University of York, Deramore Lane, Heslington, York, YO10 5GH, United Kingdom

    Nick Pears

  2. Department of Computer Science, Aberystwyth University, Llandinam Building, Ceredigion, Aberystwyth, SY23 3DB, United Kingdom

    Yonghuai Liu

  3. Institute of Geography and Earth Science, Aberystwyth University, Penglais Campus, Ceredigion, Aberystwyth, SY23 3DB, United Kingdom

    Peter Bunting

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Bronstein, A.M., Bronstein, M.M., Ovsjanikov, M. (2012). Feature-Based Methods in 3D Shape Analysis. In: Pears, N., Liu, Y., Bunting, P. (eds) 3D Imaging, Analysis and Applications. Springer, London. https://doi.org/10.1007/978-1-4471-4063-4_5

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  • DOI: https://doi.org/10.1007/978-1-4471-4063-4_5

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