Automatic Recognition of Bidimensional Models Learned by Grammatical Inference in Outdoor Scenes

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 is one of the methodologies that 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 recognition methodology to identify models in a outdoor scenes generated through a grammatical inference process. We will summarize how the set of model classes are generated and will explain the recognition process. An example of traffic sign identification will be shown.