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Proceedings Paper

Stochastic structure estimation by motion
Author(s): Arcangelo Distante; Francesco P. Lovergine; Giovanni Attolico; Maria Teresa Chiaradia; Laura Caponetti
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Paper Abstract

A field of great interest in computer vision is depth reconstruction by motion. The final goal is the computation of the visible surface structure in a 3D scene by analyzing a sequence of digital images acquired moving a camera in the environment. This paper describes a method of depth reconstruction based on stochastic modeling of the motion, the image acquisition processes, and the 3D-2D projection. The stochastic model is based on the well-known extended Kalman filter to derive an optimized depth estimation: it integrates successive views by using a pair of optical flow equations that we have adapted to a general pin-hole camera model (linear transformation from 3D to 2D coordinates). In comparison with similar methods we developed a reconstruction system to improve the speed of the estimation process and its stability by means of a multi-scale approach and used a massive parallel MIMD machine to speed up globally the estimation process.

Paper Details

Date Published: 1 November 1992
PDF: 10 pages
Proc. SPIE 1826, Intelligent Robots and Computer Vision XI: Biological, Neural Net, and 3D Methods, (1 November 1992); doi: 10.1117/12.131624
Show Author Affiliations
Arcangelo Distante, Istituto Elaborazione Segnali ed Immagini/CNR (Italy)
Francesco P. Lovergine, Istituto Elaborazione Segnali ed Immagini/CNR (Italy)
Giovanni Attolico, Istituto Elaborazione Segnali ed Immagini/CNR (Italy)
Maria Teresa Chiaradia, Univ. di Bari (Italy)
Laura Caponetti, Univ. di Bari (Italy)


Published in SPIE Proceedings Vol. 1826:
Intelligent Robots and Computer Vision XI: Biological, Neural Net, and 3D Methods
David P. Casasent, Editor(s)

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