Abstract
We present the "Parametrized Self-Organizing Map" (PSOM) as a method for 3D object recognition and pose estimation. The PSOM can be seen as a continuous extension of the standard Self-Organizing Map which generalizes the discrete set of reference vectors to a continuous manifold. In the context of visual learning, manifolds based on PSOMs can be used to represent the appearance of various objects. We demonstrate this approach and its merits in an application example. © Springer-Verlag Berlin Heidelberg 2002.
Cite
CITATION STYLE
Saalbach, A., Heidemann, G., & Ritter, H. (2002). Parametrized SOMs for object recognition and pose estimation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2415 LNCS, pp. 902–907). Springer Verlag. https://doi.org/10.1007/3-540-46084-5_146
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
