Abstract
In this work we present a method to integrate driving model and depth for the identification of 3D models in a scene where they can be partially occluded. The method is based on the identification of objects through the identification of projective invariant features and the validation of them by means of geometric and depth information. The depth information is used in several stages of the identification process for reducing the number of candidates in the initial hypothesis process and to augment the reliability of other modules. Two examples are shown in order to show the goodness of the method.
This work has been partially supported by a grant from the Fundació Areces.
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© 1993 Springer-Verlag Berlin Heidelberg
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Sanfeliu, A., Añaños, M., Dunjó, M.J. (1993). Integrating Driving Model and Depth for Identification of Partially Occluded 3D Models. In: Aggarwal, J.K. (eds) Multisensor Fusion for Computer Vision. NATO ASI Series, vol 99. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-02957-2_12
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DOI: https://doi.org/10.1007/978-3-662-02957-2_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-08135-4
Online ISBN: 978-3-662-02957-2
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