Integrating visual modules: a Bayesian approach

Kok-Guan Lim; Richard W. Prager

doi:10.1117/12.155077

3 September 1993 Integrating visual modules: a Bayesian approach

Kok-Guan Lim, Richard W. Prager

Proceedings Volume 1956, Sensor Fusion and Aerospace Applications; (1993) https://doi.org/10.1117/12.155077
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

Abstract

We present a new method using the Bayesian approach and a Markov random field (MRF) model of integrating several low level visual modules. Using this approach, we have shown how results from an edge based stereo module can be integrated with an intensity based stereo algorithm. In another example, results from a shape from shading module are combined with intensity based stereo. We first derive the intensity based stereo algorithm using the MRF model. The integration is then performed by coupling the results from other modules to the energy functional of the MRF associated with the intensity based stereo. The maximum a prosteriori (MAP) estimate of the resulting MRF is obtained using the mean field annealing algorithm. Results from real and artificial images show a consistent improvement in the accuracy after integration.

Citation Download Citation

Kok-Guan Lim and Richard W. Prager "Integrating visual modules: a Bayesian approach", Proc. SPIE 1956, Sensor Fusion and Aerospace Applications, (3 September 1993); https://doi.org/10.1117/12.155077

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