3D Object Recognition Using Unsupervised Feature Extraction

Intrator, N; Gold, JI; Bülthoff, HH; Edelman, S

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3D Object Recognition Using Unsupervised Feature Extraction

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https://papers.nips.cc/paper/574-3d-object-recognition-using-unsupervised-feature-extraction.pdf
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Intrator, N., Gold, J., Bülthoff, H., & Edelman, S. (1992). 3D Object Recognition Using Unsupervised Feature Extraction. In J. Moody, S. Hanson, & R. Lippmann (Eds.), Advances in Neural Information Processing Systems 4 (pp. 368-377). San Mateo, CA, USA: Kaufmann.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-EDF4-A

Abstract

Intrator (1990) proposed a feature extraction method that is related to recent statistical theory (Huber, 1985; Friedman, 1987) and is based on a biologically motivated model of neuronal plasticity (Bienenstock et al., 1982). This method has been recently applied to feature extraction in the context of recognizing 3D objects from single 2D views (Intrator and Gold, 1991). Here we describe experiments designed to analyze the nature of the extracted features, and their relevance to the theory and psychophysics of object recognition.