A self-organizing multiple-view representation of 3D objects

S. Edelman*, D. Weinshall

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

109 Scopus citations

Abstract

We explore representation of 3D objects in which several distinct 2D views are stored for each object. We demonstrate the ability of a two-layer network of thresholded summation units to support such representations. Using unsupervised Hebbian relaxation, the network learned to recognize ten objects from different viewpoints. The training process led to the emergence of compact representations of the specific input views. When tested on novel views of the same objects, the network exhibited a substantial generalization capability. In simulated psychophysical experiments, the network's behavior was qualitatively similar to that of human subjects.

Original languageAmerican English
Pages (from-to)209-219
Number of pages11
JournalBiological Cybernetics
Volume64
Issue number3
DOIs
StatePublished - Jan 1991
Externally publishedYes

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