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Learning bidimensional context dependent models using a context sensitive language

AuthorsSaínz, Miguel; Sanfeliu, Alberto ; Sanfeliu, Alberto
KeywordsPattern recognition
Pattern recognition systems
Issue Date1996
PublisherInstitute of Electrical and Electronics Engineers
Citation13th International Conference on Pattern Recognition: 565-569 (1996)
AbstractAutomatic 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.
DescriptionInternational Conference on Pattern Recognition (ICPR), 1996, Viena (Austria)
Appears in Collections:(IRII) Comunicaciones congresos
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