Please use this identifier to cite or link to this item:
|Type:||Artigo de evento|
|Title:||A 2d/3d Vision Based Approach Applied To Road Detection In Urban Environments|
|Abstract:||This paper presents an approach for road detection based on image segmentation. This segmentation is resulted from merging 2D and 3D image processing data from a stereo vision system. The 2D layer returns a matrix containing pixel's clusters based on the Watershed transform. Whereas the 3D layer return labels, that are classified by the V-Disparity technique, to free spaces, obstacles and non-classified area. Thus, a feature's descriptor for each cluster is composed with features from both layers. The road pattern recognition was performed by an artificial neural network, trained to obtain a final result from this feature's descriptor. The proposed work reports real experiments carried out in a challenging urban environment to illustrate the validity and application of this approach. © 2013 IEEE.|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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