A robust stereo matching algorithm using kernel representation of the probability density functions (pdf’s) of the sources that generate the stereoscopic images. Matching is done using either a Maximum Likelihood framework or using correlation in the pdf domain and an MRF prior to model the disparity function.
- A. Jagmohan, M. Singh and N. Ahuja, Dense Two View Stereo Matching Using Kernel Maximum Likelihood Estimation, IEEE International Conference on Pattern Recognition, Cambridge, UK, August, 2004, 28-31.
- M. Singh, H. Arora and N. Ahuja, Robust Registration and Tracking Using Kernel Density Correlation, 2nd IEEE Conference on Image and Video Registration, Washington, DC. June 2004, p. 174.