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Orientation invariant features for multiclass object recognition

AuthorsVillamizar, Michael ; Sanfeliu, Alberto ; Andrade-Cetto, Juan
Object detection
Invariant feature
Pattern recognition
Pattern recognition systems
Issue Date2006
CitationProgress in Pattern Recognition, Image Analysis and Applications: 655-664 (2006)
SeriesLecture Notes in Computer Science 4225
AbstractWe 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.
DescriptionPresentado al 11th Iberoamerican Congress on Pattern Recognition (CIARP/2006) celebrado en Cancún (México).
Publisher version (URL)http://dx.doi.org/10.1007/11892755_68
Appears in Collections:(IRII) Libros y partes de libros
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