Translator Disclaimer
30 April 1992 Combining evidence from multiple views of 3-D objects
Author Affiliations +
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.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Michael Seibert and Allen M. Waxman "Combining evidence from multiple views of 3-D objects", Proc. SPIE 1611, Sensor Fusion IV: Control Paradigms and Data Structures, (30 April 1992);


High-speed, high-accuracy large range 3D measurement
Proceedings of SPIE (May 01 2017)
A Massively Parallel Approach To Object Recognition
Proceedings of SPIE (March 27 1989)
Range data from stereo images of edge points
Proceedings of SPIE (February 01 1991)
Behaviors for active object recognition
Proceedings of SPIE (August 20 1993)

Back to Top