Abstract
In this paper we describe how kernel-based novelty detection can be used effectively to model 3D objects from unconstrained image sequences, in order to deal with object identification and recognition. In this framework, we introduce a similarity measure based on the Hausdorff distance, well suited to represent, identify, and recognize 3D objects from grey-level images. The effectiveness of the method is shown on the representation and identification of rigid 3D objects in cluttered environments. © Springer-Verlag Berlin Heidelberg 2002.
Cite
CITATION STYLE
Barla, A., & Odone, F. (2002). Kernel-based 3D object representation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2415 LNCS, pp. 1195–1200). Springer Verlag. https://doi.org/10.1007/3-540-46084-5_193
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