ANN hybrid ensemble learning strategy in 3d object recognition and pose estimation based on similarity

Authors:
Rui Nian;Guangrong Ji;Wencang Zhao;Chen Feng
Affiliations:
College of Information Science and Engineering, Ocean University of China, Qingdao, China;College of Information Science and Engineering, Ocean University of China, Qingdao, China;College of Information Science and Engineering, Ocean University of China, Qingdao, China;College of Information Science and Engineering, Ocean University of China, Qingdao, China
Venue:
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Year:
2005

Citing 6
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Abstract

In this paper, we present an ANN hybrid ensemble scheme for simultaneous object recognition and pose estimation from 2D multiple-view image sequence, and realized human vision simulation within an intelligent machine. Based on the notion of similarity measure at various metrics, the paradox between information simplicity and accuracy is balanced by a model view generation procedure. An ANN hierarchical hybrid ensemble framework, much like a decision tree, is then set up, with multiple weights and radial basis function neural networks respectively employed for different tasks. The strategy adopted not only determines object identity by spatial geometrical cognition and omnidirectional accumulation through connectivity, but also assigns an initial pose estimation on a viewing sphere in a coarse to fine process. Simulation experiment has achieved encouraging results, proved the approach effective, superior and feasible in large-scale database and parallel computation.