Automated 3D registration of truncated MR and CT images of the head
BMVC '95 Proceedings of the 1995 British conference on Machine vision (Vol. 1)
Alignment by Maximization of Mutual Information
International Journal of Computer Vision
Alignment by maximization of mutual information
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
On Registration of Regions of Interest (ROI) in Video Sequences
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
Image-based robot navigation from an image memory
Robotics and Autonomous Systems
Monocular Vision for Mobile Robot Localization and Autonomous Navigation
International Journal of Computer Vision
Mutual Information-Based 3D Object Tracking
International Journal of Computer Vision
A mapping and localization framework for scalable appearance-based navigation
Computer Vision and Image Understanding
Robust Appearance Based Visual Route Following for Navigation in Large-scale Outdoor Environments
International Journal of Robotics Research
Qualitative vision-based path following
IEEE Transactions on Robotics - Special issue on rehabilitation robotics
Autonomous navigation of vehicles from a visual memory using a generic camera model
IEEE Transactions on Intelligent Transportation Systems
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
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
IEEE Transactions on Robotics
Mutual Information-Based Visual Servoing
IEEE Transactions on Robotics
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In this paper we propose a new way to achieve a navigation task (visual path following) for a non-holonomic vehicle. We consider an image-based navigation process. We show that it is possible to navigate along a visual path without relying on the extraction, matching and tracking of geometric visual features such as keypoint. The new proposed approach relies directly on the information (entropy) contained in the image signal. We show that it is possible to build a control law directly from the maximization of the shared information between the current image and the next key image in the visual path. The shared information between those two images is obtained using mutual information that is known to be robust to illumination variations and occlusions. Moreover the generally complex task of features extraction and matching is avoided. Both simulations and experiments on a real vehicle are presented and show the possibilities and advantages offered by the proposed method.