Stochastic structure estimation by motion

Arcangelo Distante; Francesco P. Lovergine; Giovanni Attolico; Maria Teresa Chiaradia; Laura Caponetti

doi:10.1117/12.131624

1 November 1992 Stochastic structure estimation by motion

Arcangelo Distante, Francesco P. Lovergine, Giovanni Attolico, Maria Teresa Chiaradia, Laura Caponetti

Proceedings Volume 1826, Intelligent Robots and Computer Vision XI: Biological, Neural Net, and 3D Methods; (1992) https://doi.org/10.1117/12.131624
Event: Applications in Optical Science and Engineering, 1992, Boston, MA, United States

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.

Citation Download Citation

Arcangelo Distante, Francesco P. Lovergine, Giovanni Attolico, Maria Teresa Chiaradia, and Laura Caponetti "Stochastic structure estimation by motion", Proc. SPIE 1826, Intelligent Robots and Computer Vision XI: Biological, Neural Net, and 3D Methods, (1 November 1992); https://doi.org/10.1117/12.131624

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