In this paper we describe our 3D object signature for 3D object classification. The signature is based on a learning approach that finds salient points on a 3D object and represent these points in a 2D spatial map based on a longitude-latitude transformation. Experimental results show high classification rates on both pose-normalized and rotated objects and include a study on classification accuracy as a function of number of rotations in the training set. © 2008 Springer Berlin Heidelberg.
CITATION STYLE
Atmosukarto, I., & Shapiro, L. G. (2008). A learning approach to 3D object representation for classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5342 LNCS, pp. 267–276). https://doi.org/10.1007/978-3-540-89689-0_31
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