Paper
23 June 2003 Recognition and location of real objects using eigenimages and a neural network classifier
Maria Asuncion Vicente, Oscar Reinoso, Carlos Perez, Cesar Fernandez, Jose Maria Sabater
Author Affiliations +
Proceedings Volume 5150, Visual Communications and Image Processing 2003; (2003) https://doi.org/10.1117/12.502688
Event: Visual Communications and Image Processing 2003, 2003, Lugano, Switzerland
Abstract
A representation using eigenimages that achieves in two stages identify the object and locate its pose is addressed.In this paper we demonstrate how a mixture of two approaches based on eigenspaces with some little modifications resolve the problem of identification and the location of the object. In the first stage we recognize the object by means of PCA* (Principal Component Analysis) method combined with a neural network classifier, and in the second step, the object’s pose is obtained using a modification of typical PCA (we name as PCA2 method). We present the results obtained using a database made with 25 real objects.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Maria Asuncion Vicente, Oscar Reinoso, Carlos Perez, Cesar Fernandez, and Jose Maria Sabater "Recognition and location of real objects using eigenimages and a neural network classifier", Proc. SPIE 5150, Visual Communications and Image Processing 2003, (23 June 2003); https://doi.org/10.1117/12.502688
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Cited by 2 scholarly publications.
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KEYWORDS
Principal component analysis

Neural networks

Distance measurement

Neurons

Databases

Mahalanobis distance

Binary data

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