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A Probabilistic Cascade of Detectors for Individual Object Recognition

Moreels, Pierre and Perona, Pietro (2008) A Probabilistic Cascade of Detectors for Individual Object Recognition. In: Computer Vision – ECCV 2008. Lecture Notes in Computer Science. Vol.III. No.5304. Springer , Berlin, pp. 426-439. ISBN 978-3-540-88689-1. https://resolver.caltech.edu/CaltechAUTHORS:20150903-114413098

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Abstract

A probabilistic system for recognition of individual objects is presented. The objects to recognize are composed of constellations of features, and features from a same object share the common reference frame of the image in which they are detected. Features appearance and pose are modeled by probabilistic distributions, the parameters of which are shared across features in order to allow training from few examples. In order to avoid an expensive combinatorial search, our recognition system is organized as a cascade of well-established, simple and inexpensive detectors. The candidate hypotheses output by our algorithm are evaluated by a generative probabilistic model that takes into account each stage of the matching process. We apply our ideas to the problem of individual object recognition and test our method on several data-set s. We compare with Lowe’s algorithm[7] and demonstrate significantly better performance.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1007/978-3-540-88690-7_32DOIArticle
http://link.springer.com/chapter/10.1007%2F978-3-540-88690-7_32PublisherArticle
ORCID:
AuthorORCID
Perona, Pietro0000-0002-7583-5809
Additional Information:© 2008 Springer-Verlag.
Series Name:Lecture Notes in Computer Science
Issue or Number:5304
Record Number:CaltechAUTHORS:20150903-114413098
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20150903-114413098
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:60049
Collection:CaltechAUTHORS
Deposited By: Caroline Murphy
Deposited On:16 Sep 2015 23:13
Last Modified:03 Oct 2019 08:53

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