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Object Recognition in the Geometric Era: A Retrospective

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Toward Category-Level Object Recognition

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4170))

Abstract

Recent advances in object recognition have emphasized the integration of intensity-derived features such as affine patches with associated geometric constraints leading to impressive performance in complex scenes. Over the four previous decades, the central paradigm of recognition was based on formal geometric object descriptions with a focus on the properties of such descriptions under perspective image formation. This paper will review the key advances of the geometric era and investigate the underlying causes of the movement away from formal geometry and prior models towards the use of statistical learning methods based on appearance features.

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  1. Division of Engineering, Brown University, Providence, Rhode Island

    Joseph L. Mundy

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  1. Joseph L. Mundy
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Editors and Affiliations

  1. Département d’Informatique, Ecole Normale Supérieure, P.O. Box, Paris, France

    Jean Ponce

  2. Carnegie Mellon University, Pittsburgh, USA

    Martial Hebert

  3. GRAVIR-INRIA, 655 avenue de l’Europe, P.O. Box, 38330, Montbonnot, France

    Cordelia Schmid

  4. Department of Engineering Science, University of Oxford, Parks Road, OX1 3PJ, Oxford, UK

    Andrew Zisserman

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Mundy, J.L. (2006). Object Recognition in the Geometric Era: A Retrospective. In: Ponce, J., Hebert, M., Schmid, C., Zisserman, A. (eds) Toward Category-Level Object Recognition. Lecture Notes in Computer Science, vol 4170. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11957959_1

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  • DOI: https://doi.org/10.1007/11957959_1

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  • Print ISBN: 978-3-540-68794-8

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