Stereo Registration using Kernel Density Correlation

M. Singh, A. Jagmohan, and N. Ahuja (USA)


stereo, MRF, Parzen windows, density estimation, varia tional optimization, dense disparity field.


A common approach to solving the stereo registration prob lem is to model the disparity function as a discrete-valued Markov Random Field. The key problems with this ap proach are its combinatorial computational complexity, and the discretization of the obtained disparity estimates. In this paper, we propose a framework that addresses the re quirements of a robust continuous domain formulation for stereo registration. The proposed formulation is based on a new measure, derived from the correlation of empirical probability density distributions estimated using kernel es timators. We term this the kernel density correlation (KDC) measure. The proposed framework takes the form of an en ergy minimization formulation which is efficiently solved using the technique of variational optimization. We prove the convergence properties of the resultant iterative algo rithm, and compare the performance of the proposed for mulation to that of a state-of-the-art stereo registration ap proach.

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