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Combining multiple views and temporal associations for 3-D object recognition

  • Amin Massad
  • Bärbel Mertsching
  • Steffen Schmalz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1407)

Abstract

This article describes an architecture for the recognition of three-dimensional objects on the basis of viewer centred representations and temporal associations. Considering evidence from psychophysics, neurophysiology, as well as computer science we have decided to use a viewer centred approach for the representation of three-dimensional objects. Even though this concept quite naturally suggests utilizing the temporal order of the views for learning and recognition, this aspect is often neglected. Therefore we will pay special attention to the evaluation of the temporal information and embed it into the conceptual framework of biological findings and computational advantages. The proposed recognition system consists of four stages and includes different kinds of artificial neural networks: Preprocessing is done by a Gabor-based wavelet transform. A Dynamic Link Matching algorithm, extended by several modifications, forms the second stage. It implements recognition and learning of the view classes. The temporal order of the views is recorded by a STORE network which transforms the output for a presented sequence of views into an item- and-order coding. A subsequent Gaussian-ARTMAP architecture is used for the classification of the sequences and for their mapping onto object classes by means of supervised learning. The results achieved with this system show its capability to autonomously learn and to recognize considerably similar objects. Furthermore the given examples illustrate the benefits for object recognition stemming from the utilization of the temporal context. Ambiguous views become manageable and a higher degree of robustness against misclassifications can be accomplished.

Keywords

Object Recognition Temporal Order Object Class Temporal Association View Class 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Amin Massad
    • 1
  • Bärbel Mertsching
    • 1
  • Steffen Schmalz
    • 1
  1. 1.Dep. of Computer Science, AG IMAUniversity of HamburgHamburgGermany

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