3D Object Recognition Using Unsupervised Feature Extraction (1992)
| Venue: | S.J., & Lippman, R.P. (eds), Advances in Neural Information Processing Systems |
| Citations: | 1 - 0 self |
BibTeX
@INPROCEEDINGS{Intrator923dobject,
author = {Nathan Intrator and Josh I. Gold and Heinrich H. Bülthoff and Shimon Edelman},
title = {3D Object Recognition Using Unsupervised Feature Extraction},
booktitle = {S.J., & Lippman, R.P. (eds), Advances in Neural Information Processing Systems},
year = {1992},
publisher = {Morgan Kaufmann}
}
Bookmark
 |
 |
 |
 |
 |
 |
OpenURL
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. 1 Introduction Results of recent computational studies of visual recognition (e.g., Poggio and Edelman, 1990) indicate that the problem of recognition of 3D objects can be effectively reformulated in terms of standard pattern classification theory. According to this approach, an object is represented by a few of its 2D views, encoded as clusters in multidimentional space. Recognition of a novel view is then carried out by interpo- lating among the stored ...
