Abstract
This paper investigates how the network scheme that consists of a set of bidirectional networks can be used to recognize gray-level images of 3D objects. The proposed scheme is based on the networks' ability to both compress and generate the input images. The paper also presents a bidirectional relaxation method that can be used to identify transformed or distorted images of 3D objects. We demonstrate through computer experiments that the proposed recognition model can learn to classify gray-level images of 3D objects and exhibit flexible alignment for transformed images.
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© 1996 Springer-Verlag Berlin Heidelberg
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Ando, H. (1996). 3D object recognition using bidirectional modular networks. In: Li, S.Z., Mital, D.P., Teoh, E.K., Wang, H. (eds) Recent Developments in Computer Vision. ACCV 1995. Lecture Notes in Computer Science, vol 1035. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60793-5_100
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DOI: https://doi.org/10.1007/3-540-60793-5_100
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Print ISBN: 978-3-540-60793-9
Online ISBN: 978-3-540-49448-5
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