English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/30176
Title: Learning bidimensional context dependent models using a context sensitive language
Authors: Saínz, Miguel; Sanfeliu, Alberto
Keywords: Pattern recognition
Pattern recognition systems
Issue Date: 1996
Publisher: Institute of Electrical and Electronics Engineers
Citation: 13th International Conference on Pattern Recognition: 565-569 (1996)
Abstract: Automatic generation of models from a set of positive and negative samples and a-priori knowledge (if available) is a crucial issue for pattern recognition applications. Grammatical inference can play an important role in this issue since it can be used to generate the set of model classes, where each class consists on the rules to generate the models. In this paper we present the process of learning context dependent bidimensional objects from outdoors images as context sensitive languages. We show how the process is conceived to overcome the problem of generalizing rules based on a set of samples which have small differences due to noisy pixels. The learned models can be used to identify objects in outdoors images irrespectively of their size and partial occlusions. Some results of the inference procedure are shown in the paper.
Description: International Conference on Pattern Recognition (ICPR), 1996, Viena (Austria)
URI: http://hdl.handle.net/10261/30176
DOI: http://dx.doi.org/10.1109/ICPR.1996.547628
Appears in Collections:(IRII) Comunicaciones congresos
Files in This Item:
File Description SizeFormat 
doc1.pdf505,17 kBAdobe PDFThumbnail
Show full item record

WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.