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Proceedings Paper

Eigen local color histograms for object recognition and orientation estimation
Author(s): D. Muselet; B. Funt; L. Macaire
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Paper Abstract

Color has been shown to be an important clue for object recognition and image indexing. We present a new algorithm for color-based recognition of objects in cluttered scenes that also determines the 2D pose of each object. As with so many other color-based object recognition algorithms, color histograms are also fundamental to our new approach; however, we use histograms obtained from overlapping subwindows rather than the entire image. An object from a database of prototypes is identified and located in an input image whenever there are many good histogram matches between the respective subwindow histograms of the input image and the image prototype from the database. In essence, local color histograms are the features to be matched. Once an object's position in the image has been determined, its 2D pose is determined by approximating the geometrical transformation most consistently mapping the locations of the prototype's subwindows to their matching locations in the input image.

Paper Details

Date Published: 12 February 2007
PDF: 8 pages
Proc. SPIE 6492, Human Vision and Electronic Imaging XII, 64921R (12 February 2007); doi: 10.1117/12.711007
Show Author Affiliations
D. Muselet, Lab. LIGIV, Univ. Jean Monnet (France)
B. Funt, Simon Fraser Univ. (Canada)
L. Macaire, Lab. LAGIS, CNRS, Univ. des Sciences et Technologies de Lille (France)

Published in SPIE Proceedings Vol. 6492:
Human Vision and Electronic Imaging XII
Bernice E. Rogowitz; Thrasyvoulos N. Pappas; Scott J. Daly, Editor(s)

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