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Proceedings Paper

Combining evidence from multiple views of 3-D objects
Author(s): Michael Seibert; Allen M. Waxman
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Paper Abstract

We summarize a recently developed modular neural system which exploits sequences of 2D views for learning and recognizing 3D objects. An aspect network is an unsupervised module of our complete artificial vision system for detecting and learning the view transitions (as the appearance of a rotating object changes), and for later recognizing objects from sequences of views. By processing sequences of views, the system accumulates evidence over time, thereby increasing the confidence of its recognition decisions. Also, when new views are revealed following views recognized previously by an aspect network during the course of observation, the new views and view-transitions are used to refine the evolving 3D object representation automatically. Recognition is possible even from novel (previously unexperienced) view sequences. The objects used for illustration are model aircraft in flight. The computations are formulated as differential equations among analog nodes and synapses to model the temporal dynamics explicitly.

Paper Details

Date Published: 30 April 1992
PDF: 12 pages
Proc. SPIE 1611, Sensor Fusion IV: Control Paradigms and Data Structures, (30 April 1992); doi: 10.1117/12.57921
Show Author Affiliations
Michael Seibert, Lincoln Lab./MIT (United States)
Allen M. Waxman, Lincoln Lab./MIT (United States)


Published in SPIE Proceedings Vol. 1611:
Sensor Fusion IV: Control Paradigms and Data Structures
Paul S. Schenker, Editor(s)

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