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By J. Torreao

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Psychol Rev 113: 766–786. , Simeon, T. & Alami, R. (2000). Incremental topological modeling using local voronoï-like graphs, Proc. of IEEE/RSJ Int. Conf. on Intelligent Robots and System (IROS 2000), Vol. 2, pp. 897–902. , Zhuang, Y. & Wang, W. (2006). Large-scale topological environmental model based particle filters for mobile robot indoor localization, Robotics and Biomimetics, IEEE International Conference on 0: 858–863. , Mouche, F. & Potter, C. (2003). Automatic particle detection through efficient hough transforms, IEEE Trans.

38 22 Advances inWill-be-set-by-IN-TECH Stereo Vision (a) (b) (c) (d) (e) Fig. 17. Modeling process during an autonomous exploration of the scene of figure 18. Work in order to relax the rectangle assumption, allowing the robot to work with more general models such as polylines, is currently in progress. We are also studying the advantages of using formal grammars for topological modeling. In addition, we are also improving and testing the system for much bigger and cluttered environments. Attentional Behaviors for Environment Modeling by a Mobile Robot Attentional Behaviors for Environment Modeling by a Mobile Robot 39 23 Fig.

2 Identification of homogeneous textures: combining classifiers Any classification process in general and in particular the identification of textures in natural images has associated two main phases: training and decision. We refer to the first phase as learning phase also, by identifying both concepts in the literature. By the nature of processing in the time sometimes appear as off-line and on-line processes respectively. This is due to the fact that the training phase is usually carried out during system downtime, being at this time when the parameters involved in the process are estimated or learned.

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