Department of Computer Engineering
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Browsing Department of Computer Engineering by Subject "3-D space"
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Item Open Access Extraction of 3D navigation space in virtual urban environments(IEEE, 2005-09) Yılmaz, Türker; Güdükbay, UğurUrban scenes are one class of complex geometrical environments in computer graphics. In order to develop navigation systems for urban sceneries, extraction and cellulization of navigation space is one of the most commonly used technique providing a suitable structure for visibility computations. Surprisingly, there is not much work done for the extraction of the navigable area automatically. Urban models, except for the ones where the building footprints are used to generate the model, generally lack navigation space information. Because of this, it is hard to extract and discretize the navigable area for complex urban scenery. In this paper, we propose an algorithm for the extraction of navigation space for urban scenes in threedimensions (3D). Our navigation space extraction algorithm works for scenes, where the buildings are in high complexity. The building models may have pillars or holes where seeing through them is also possible. Besides, for the urban data acquired from different sources which may contain errors, our approach provides a simple and efficient way of discretizing both navigable space and the model itself. The extracted space can instantly be used for visibility calculations such as occlusion culling in 3D space. Furthermore, terrain height field information can be extracted from the resultant structure, hence providing a way to implement urban navigation systems including terrains.Item Open Access Laboratuar hayvanlarının davranışlarının görü tabanlı çözümlenmesi: 3 Boyutlu gradyan tabanlı bir yaklaşım(IEEE, 2009-04) Sandıkçı, Selçuk; Duygulu-Şahin, Pınar; Özgüler, Arif BülentIn pharmacological experiments behavior pattern of laboratory mice, which are under the influence of psychotherapeutic drugs, reveals important clues about effects of the drug. Behavior analysis of laboratory mice by video processing saves both time and labor. In this work a method which was previously used to recognize human behaviors is adapted to laboratory mice case. Method is based on fitting histograms of spatio-temporal gradients extracted from 3D space-time volumes to multidimensional statistical distributions and class(lj'ing according to distances between the distributions. In this work the method is tested on a common mice video dataset, compared to other methods in the literature and found to be successful. ©2009 IEEE.