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Conference Paper

Three-Dimensional Object Recognition Using an Unsupervised Neural Network: Understanding the Distinguishing Features

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons83839

Gold JI, Bülthoff,  HH
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Intrator, N., Gold JI, Bülthoff, H., & Edelman, S. (1991). Three-Dimensional Object Recognition Using an Unsupervised Neural Network: Understanding the Distinguishing Features. In 8th Israeli Conference on AICV (pp. 113-123). Amsterdam, Netherlands: Elsevier.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-EE65-6
Abstract
A novel method for feature extraction has been applied to a problem of three-dimensional object recognition (Intrator and Gold, 1991). The method is related to recent statistical theory (Huber, 1985; Friedman, 1987) and is derived from a biologically motivated computational theory (Bienenstock, Cooper and Munro, 1982). Results of an initial study replicating recent psychophysical experiments (Bulthoff and Edelman 1991) demonstrated the utility of the proposed method for feature extraction. We describe further experiments designed to analyze the nature of the extracted features, and their relevance to the theory and psychophysics of object recognition.