Abstract
In this paper, we present a neuron-like network able to build 3D dense maps from stereoscopic image pair. The process of stereopsis is encoded by an energy function, which controls the evolving of the network. This one has the same structure than original images, and evolutes on a simple gradient steep. Thus, the system is fully parallel and could be hardware implemented for a real time use.
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© 1995 Springer-Verlag/Wien
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Fauvel, MN., Aubry, P. (1995). Connectionnist Algorithm for a 3D Dense Image Building from Stereoscopy. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7535-4_108
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DOI: https://doi.org/10.1007/978-3-7091-7535-4_108
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-82692-8
Online ISBN: 978-3-7091-7535-4
eBook Packages: Springer Book Archive