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Proceedings Paper

Regularization approach to multisensor reconstruction
Author(s): Jagath C. Rajapakse; Raj S. Acharya
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Paper Abstract

Due to the presence of noise and uncertainty, a single set of visual data is not always sufficient to successfully perform the intended vision task. In this paper, we consider the visual reconstruction problem with multi-sensor data and propose a computational method for fusion of sensor information. Correlation among different sensor information appears in some hidden representation of data. In our representation of visual data, the fusion of information occurs at the places where visual discontinuities appear. The appearance of discontinuities in one set of data is constrained by the discontinuity contours in other sets of data. This is achieved by introducing energies of external discontinuity cliques of multiple sensor data to the minimizing energy functional. The fusion improves the reconstruction error in individual sensors, and the detection of discontinuities, and reduces the false detection of discontinuities. The computational method presented in this paper can deal with fusion of surfaces with higher order discontinuities and with data sets belonging to different visual cues. Previously proposed Markov Random Field (MRF) techniques can deal with surfaces having only first order discontinuities. An implementation of the proposed fusion method on simulated images, and a comparison of the results with the other methods are presented.

Paper Details

Date Published: 20 August 1993
PDF: 12 pages
Proc. SPIE 2059, Sensor Fusion VI, (20 August 1993); doi: 10.1117/12.150261
Show Author Affiliations
Jagath C. Rajapakse, SUNY/Buffalo (United States)
Raj S. Acharya, SUNY/Buffalo (United States)

Published in SPIE Proceedings Vol. 2059:
Sensor Fusion VI
Paul S. Schenker, Editor(s)

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