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Shape-Based Instance Detection Under Arbitrary Viewpoint

  • Chapter
Shape Perception in Human and Computer Vision

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

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Abstract

Shape-based instance detection under arbitrary viewpoint is a very challenging problem. Current approaches for handling viewpoint variation can be divided into two main categories: invariant and non-invariant. Invariant approaches explicitly represent the structural relationships of high-level, view-invariant shape primitives. Non-invariant approaches, on the other hand, create a template for each viewpoint of the object, and can operate directly on low-level features. We summarize the main advantages and disadvantages of invariant and non-invariant approaches, and conclude that non-invariant approaches are well-suited for capturing fine-grained details needed for specific object recognition while also being computationally efficient. Finally, we discuss approaches that are needed to address ambiguities introduced by recognizing shape under arbitrary viewpoint.

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Acknowledgements

This work was supported in part by the National Science Foundation under ERC Grant No. EEEC-0540865.

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Authors and Affiliations

  1. Robotics Institute, Carnegie Mellon University, Pittsburgh, USA

    Edward Hsiao & Martial Hebert

Authors
  1. Edward Hsiao
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  2. Martial Hebert
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Correspondence to Edward Hsiao .

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Editors and Affiliations

  1. Department of Computer Science, University of Toronto, King's College Road 6, Toronto, M5S 3G4, Ontario, Canada

    Sven J. Dickinson

  2. Department of Psychological Sciences, Purdue University, Third Street 703, West Lafayette, 47907, Indiana, USA

    Zygmunt Pizlo

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Hsiao, E., Hebert, M. (2013). Shape-Based Instance Detection Under Arbitrary Viewpoint. In: Dickinson, S., Pizlo, Z. (eds) Shape Perception in Human and Computer Vision. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-5195-1_33

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  • DOI: https://doi.org/10.1007/978-1-4471-5195-1_33

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-5194-4

  • Online ISBN: 978-1-4471-5195-1

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