Publication
Orientation invariant features for multiclass object recognition
Conference Article
Conference
Iberoamerican Congress on Pattern Recognition (CIARP)
Edition
11th
Pages
655-664
Doc link
http://dx.doi.org/10.1007/11892755_68
File
Abstract
We present a framework for object recognition based on simple scale and orientation invariant local features that when combined with a hierarchical multiclass boosting mechanism produce robust classifiers for a limited number of object classes in cluttered backgrounds. The system extracts the most relevant features from a set of training samples and builds a hierarchical structure of them. By focusing on those features common to all trained objects, and also searching for those features particular to a reduced number of classes, and eventually, to each object class. To allow for efficient rotation invariance, we propose the use of non-Gaussian steerable filters, together with an Orientation Integral Image for a speedy computation of local orientation.
Categories
automation, pattern recognition.
Author keywords
vision, object detection, invariant feature
Scientific reference
M. Villamizar, A. Sanfeliu and J. Andrade-Cetto. Orientation invariant features for multiclass object recognition, 11th Iberoamerican Congress on Pattern Recognition, 2006, Cancún, Mèxic, in Progress in Pattern Recognition, Image Analysis and Applications, Vol 4225 of Lecture Notes in Computer Science, pp. 655-664, 2006, Springer, Berlin, Alemanya.
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