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Image segmentation for 3D object recognition using bidirectional networks

  • Part VI: Speech, Vision, and Pattern Recognition
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Artificial Neural Networks — ICANN'97 (ICANN 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1327))

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Abstract

This paper proposes a 3D object recognition model that integrates the image segmentation and the correspondence estimation based on bidirectional processing. The model can cope with both the view variation and the multiple objects occluding each other in the image. We evaluated the performance of the proposed model through computer experiments using gray-level images of 3D objects.

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Wulfram Gerstner Alain Germond Martin Hasler Jean-Daniel Nicoud

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© 1997 Springer-Verlag Berlin Heidelberg

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Fujita, T., Ando, H. (1997). Image segmentation for 3D object recognition using bidirectional networks. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020274

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  • DOI: https://doi.org/10.1007/BFb0020274

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63631-1

  • Online ISBN: 978-3-540-69620-9

  • eBook Packages: Springer Book Archive

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