Symbol Recognition with Kernel Density Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Pose Estimation and Recognition Using Non-Gaussian Modeling of Appearance Subspaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image registration based on kernel-predictability
Computer Vision and Image Understanding
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
Vehicle tracking based on image alignment in aerial videos
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Kernel covariance image region description for object tracking
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A Probabilistic Contour Observer for Online Visual Tracking
SIAM Journal on Imaging Sciences
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Challenges to accurate registration come from three factors -presence of background clutter, occlusion of the pattern being registered and changes in feature values across images. To address these concerns, we propose a robust probabilistic estimation approach predicated on representations of the object model and the target image using a kernel density estimate. These representations are then matched in the space of density functions using a correlation measure, termed the Kernel Density Correlation (KDC) measure. A popular metric which has been widely used by previous image registration approaches is the Mutual Information (MI) metric. We compare the proposed KDC metric with the MI metric to highlight its better robustness to occlusions and random background clutter-this is a consequence of the fact that the KDC measure forms a re-descending M-estimator. Another advantage of the proposed metric is that the registration problem can be efficiently solved using a variational optimization algorithm. We show that this algorithm is an iteratively reweighted least squares (IRLS) algorithm and prove its convergence properties. The efficacy of the proposed algorithm is demonstrated by its application on standard stereo registration data-sets and real tracking sequences.