A connectionist approach to multiple-view based 3-D object recognition

Wei Chung Lin*, Fong Yuan Liao, Chen Kuo Tsao, Theresa Lingutla

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

A hierarchical approach is proposed for solving the surface and the vertex correspondence problems in multiple-view-based 3-D object recognition systems. The scheme is a coarse-to-fine search process, and a Hopfield network is used at each stage. Compared with conventional object matching schemes, the technique provides a more general and compact formulation of the problem and a solution more suitable for parallel implementation. At the coarse search stage, the surface matching rates between the input image and each object model in the database are computed through a Hopfield network and used to select the candidates for further consideration. At the fine search stage, the object models selected from the previous stage are fed into another Hopfield network for vertex matching. The object model that has the best surface and vertex correspondences with the input image is finally singled out as the best matched model. Experimental results using both line drawings and real range images to corroborate the proposed theory are also reported.

Original languageEnglish (US)
Title of host publicationIJCNN. International Joint Conference on Neural Networks
PublisherPubl by IEEE
Pages835-844
Number of pages10
StatePublished - Dec 1 1990
Event1990 International Joint Conference on Neural Networks - IJCNN 90 Part 3 (of 3) - San Diego, CA, USA
Duration: Jun 17 1990Jun 21 1990

Other

Other1990 International Joint Conference on Neural Networks - IJCNN 90 Part 3 (of 3)
CitySan Diego, CA, USA
Period6/17/906/21/90

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Lin, W. C., Liao, F. Y., Tsao, C. K., & Lingutla, T. (1990). A connectionist approach to multiple-view based 3-D object recognition. In IJCNN. International Joint Conference on Neural Networks (pp. 835-844). Publ by IEEE.