Multiple constraints for optical flow
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Alignment by Maximization of Mutual Information
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Region Tracking With Parametric Models of Geometry and Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robustly estimating changes in image appearance
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
A unifying framework for mutual information methods for use in non-linear optimisation
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Optimization of mutual information for multiresolution image registration
IEEE Transactions on Image Processing
Hi-index | 0.00 |
This paper proposes a new way to achieve feature point tracking using the entropy of the image. Sum of Squared Differences (SSD) is widely considered in differential trackers such as the KLT. Here, we consider another metric called Mutual Information (MI), which is far less sensitive to changes in the lighting condition and to a wide class of non-linear image transformation. Since mutual-information is used as an energy function to be maximized to track each points, a new feature selection, which is optimal for this metric, is proposed. Results under various complex conditions are presented. Comparison with the classical KLT tracker are proposed.