Abstract
Visual object recognition using single cue information has been successfully applied in various tasks, in particular for near range. While robust classification and probabilistic representation enhance 2D pattern recognition performance, they are ‘per se’ restricted due to the limited information content of single cues. The contribution of this work is to demonstrate performance improvement using multi-cue information integrated within a probabilistic framework. 2D and 3D visual information naturally complement one another, each information source providing evidence for the occurrence of the object of interest. We demonstrate preliminary work describing Bayesian decision fusion for object detection and illustrate the method by robust recognition of traffic infrastructure.
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Paletta, L., Paar, G. (2003). Information Selection and Probabilistic 2D – 3D Integration in Mobile Mapping. In: Crowley, J.L., Piater, J.H., Vincze, M., Paletta, L. (eds) Computer Vision Systems. ICVS 2003. Lecture Notes in Computer Science, vol 2626. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36592-3_15
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DOI: https://doi.org/10.1007/3-540-36592-3_15
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