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Proceedings Paper

Fast and efficient indexing approach for object recognition
Author(s): Alaa Hefnawy; Samia A. Mashali; Mohsen Rashwan; Magdi Fikri
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Paper Abstract

This paper introduces a fast and efficient indexing approach for both 2D and 3D model-based object recognition in the presence of rotation, translation, and scale variations of objects. The indexing entries are computed after preprocessing the data by Haar wavelet decomposition. The scheme is based on a unified image feature detection approach based on Zernike moments. A set of low level features, e.g. high precision edges, gray level corners, are estimated by a set of orthogonal Zernike moments, calculated locally around every image point. A high dimensional, highly descriptive indexing entries are then calculated based on the correlation of these local features and employed for fast access to the model database to generate hypotheses. A list of the most candidate models is then presented by evaluating the hypotheses. Experimental results are included to demonstrate the effectiveness of the proposed indexing approach.

Paper Details

Date Published: 26 August 1999
PDF: 9 pages
Proc. SPIE 3837, Intelligent Robots and Computer Vision XVIII: Algorithms, Techniques, and Active Vision, (26 August 1999); doi: 10.1117/12.360311
Show Author Affiliations
Alaa Hefnawy, Electronics Research Institute (Egypt)
Samia A. Mashali, Electronics Research Institute (Egypt)
Mohsen Rashwan, Cairo Univ. (Egypt)
Magdi Fikri, Cairo Univ. (Egypt)

Published in SPIE Proceedings Vol. 3837:
Intelligent Robots and Computer Vision XVIII: Algorithms, Techniques, and Active Vision
David P. Casasent, Editor(s)

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