A survey of image registration techniques
ACM Computing Surveys (CSUR)
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
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
A Reflectance Model for Computer Graphics
ACM Transactions on Graphics (TOG)
Multiple view geometry in computer vision
Multiple view geometry in computer vision
Computing Optical Flow with Physical Models of Brightness Variation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Geometry of Multiple Images: The Laws That Govern The Formation of Images of A Scene and Some of Their Applications
Models of light reflection for computer synthesized pictures
SIGGRAPH '77 Proceedings of the 4th annual conference on Computer graphics and interactive techniques
Correction to Construction of Panoramic Image Mosaics with Global and Local Alignment
International Journal of Computer Vision
Generalized Image Matching: Statistical Learning of Physically-Based Deformations
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
Separating Reflection Components of Textured Surfaces Using a Single Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Homography-based 2D Visual Tracking and Servoing
International Journal of Robotics Research
Groupwise Geometric and Photometric Direct Image Registration
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Bi-directional Framework for Unifying Parametric Image Alignment Approaches
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
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
An Efficient Direct Approach to Visual SLAM
IEEE Transactions on Robotics
Visual object tracking by an evolutionary self-organizing neural network
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Evolutionary neural networks for practical applications
Evolving a self-organizing feature map for visual object tracking
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
Simultaneous reconstruction and tracking of non-planar templates
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
Robust decentralized multi-model adaptive template tracking
Pattern Recognition
Improving NCC-based direct visual tracking
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Robust gaussian-based template tracking
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part I
Photogeometric Direct Visual Tracking for Central Omnidirectional Cameras
Journal of Mathematical Imaging and Vision
Efficient and robust multi-template tracking using multi-start interactive hybrid search
Computer Vision and Image Understanding
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The fundamental task of visual tracking is considered in this work as an incremental direct image registration problem. Direct methods refer to those that exploit the pixel intensities without resorting to image features. We propose new transformation models and optimization methods for directly and robustly registering images (including color ones) of rigid and deformable objects, all in a unified manner. We also show that widely adopted models are in fact particular cases of the proposed ones. Indeed, the proposed general models combine various classes of image warps and ensure robustness to generic lighting changes. Finally, the proposed optimization method together with the exploitation of all possible image information allow the algorithm to achieve high levels of accuracy. Extensive experiments are reported to demonstrate that visual tracking can indeed be highly accurate and robust despite deforming objects and severe illumination changes.