Abstract
A novel architecture for real time pick up of 3D motion and 3D layout information of the visible surface of the rigid world, from the flow of the optic array, is presented.
The flow of the optic array contains the information of the motion of the physical world due to its “specificity” of the coherent changes. Such “specificity” is a one to one correspondence of 3D motions of a visible surface and its induced transformations of images.
The neural network architecture we propose is of the Pitts and McCulloch type. However, based on the second canonical coordinate system of the Lie transformation group, to facilitate image transformations induced by rigid motion of the visible surfaces in three dimensional space, one only needs 12 N intermediate nodes, where N is the number of neurons in the image plane. Thus the intractable complexity of Pitts and McCulloch’s neural network is overcome.
Research supported by grant DCR-8504011 from the Intelligent Systems Program, U.S. National Science Foundation.
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© 1988 Springer-Verlag Berlin Heidelberg
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Tsao, T., Kanal, L. (1988). A Novel Architecture for Real Time Pick Up of 3D Motion and 3D Layout Information from The Flow of The Optic Array. In: Jain, A.K. (eds) Real-Time Object Measurement and Classification. NATO ASI Series, vol 42. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-83325-0_17
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DOI: https://doi.org/10.1007/978-3-642-83325-0_17
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