Fitting Parameterized Three-Dimensional Models to Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-based object pose in 25 lines of code
International Journal of Computer Vision - Special issue: image understanding research at the University of Maryland
Alignment by Maximization of Mutual Information
International Journal of Computer Vision
Real-Time Visual Tracking of Complex Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
Fusing Points and Lines for High Performance Tracking
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Real-Time Markerless Tracking for Augmented Reality: The Virtual Visual Servoing Framework
IEEE Transactions on Visualization and Computer Graphics
Monocular model-based 3D tracking of rigid objects
Foundations and Trends® in Computer Graphics and Vision
Real-time Hybrid Tracking using Edge and Texture Information
International Journal of Robotics Research
Monocular Vision for Mobile Robot Localization and Autonomous Navigation
International Journal of Computer Vision
Mutual Information for Lucas-Kanade Tracking (MILK): An Inverse Compositional Formulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic scene structure and camera motion using a catadioptric system
Computer Vision and Image Understanding
Mutual Information-Based 3D Object Tracking
International Journal of Computer Vision
Real-time Quadrifocal Visual Odometry
International Journal of Robotics Research
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
IEEE Transactions on Robotics
Mutual Information-Based Visual Servoing
IEEE Transactions on Robotics
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This paper deals with model-based pose estimation (or camera localization). We propose a direct approach that takes into account the image as a whole. For this, we consider a similarity measure, the mutual information. Mutual information is a measure of the quantity of information shared by two signals (or two images in our case). Exploiting this measure allows our method to deal with different image modalities (real and synthetic). Furthermore, it handles occlusions and illumination changes. Results with synthetic (benchmark) and real image sequences, with static or mobile camera, demonstrate the robustness of the method and its ability to produce stable and precise pose estimations.