Least-Squares Fitting of Two 3-D Point Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Principles and techniques for sensor data fusion
Signal Processing - Intelligent systems for signal and image understanding
Mathematical Techniques in Multisensor Data Fusion
Mathematical Techniques in Multisensor Data Fusion
The Handbook of Brain Theory and Neural Networks
The Handbook of Brain Theory and Neural Networks
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Decentralized Multiple Target Tracking Using Netted Collaborative Autonomous Trackers
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Closed Form Solution to Direct Motion Segmentation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Optical Flow Estimation and Segmentation of Multiple Moving Dynamic Textures
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Two-View Multibody Structure from Motion
International Journal of Computer Vision
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Approaches to Multisensor Data Fusion in Target Tracking: A Survey
IEEE Transactions on Knowledge and Data Engineering
Non-rigid structure from motion using ranklet-based tracking and non-linear optimization
Image and Vision Computing
Walk-Sums and Belief Propagation in Gaussian Graphical Models
The Journal of Machine Learning Research
Spatio-temporal graphical-model-based multiple facial feature tracking
EURASIP Journal on Applied Signal Processing
Combining object and feature dynamics in probabilistic tracking
Computer Vision and Image Understanding
Estimation and decision fusion: A survey
Neurocomputing
A Probabilistic Approach to Integrating Multiple Cues in Visual Tracking
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Approximate inference in gaussian graphical models
Approximate inference in gaussian graphical models
Product of Gaussians for speech recognition
Computer Speech and Language
Tracking by parts: a Bayesian approach with component collaboration
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Tracking complex objects using graphical object models
IWCM'04 Proceedings of the 1st international conference on Complex motion
A new framework for machine learning
WCCI'08 Proceedings of the 2008 IEEE world conference on Computational intelligence: research frontiers
Nonparametric belief propagation
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Nonrigid shape and motion from multiple perspective views
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Robust Face Tracking via Collaboration of Generic and Specific Models
IEEE Transactions on Image Processing
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This report establishes a novel concept for tracking complex and articulated objects in the presence of high observation uncertainties utilising Markov random fields Markov chains (MRFMCs) and a novel paradigm of modelling visual perception. The approach is rooted in ideas from information fusion and cognitive sciences. The problem is to track non-rigid and articulated objects in the 3D space. The aim is to precisely estimate landmarks with high certainty for fitting accurate object models and secondary states like the orientation under partial occlusions. The targeted system is characterised by a high degree of generality. Previous solutions are relatively limited in robustness and accuracy. The new concept is motivated by the fact that all previous tracking approaches rely on semantic information, that is classified signal signatures, while neglecting all further non-classifiable and thus semantically unrelated information present in the scene herein abstracted as structure. By observing salient cues in structure and by learning and incorporating topological relations between salient cues and semantic features it is intended to tackle the major problem of visual tracking, namely accurate and robust inference in the presence of high observation uncertainties. The notion of the dichotomy of semantic and structure is not covered in previous literature. The new concept constitutes a novel direction in the design and implementation of visual perception and tracking networks. While the ideas of dynamic world modelling and intelligent forgetting stem from principles of information fusion, the principle of fusing semantical with structural information from intelligent exploring is an entirely original contribution and is inspired by ideas from cognitive sciences and linguistics. It is deduced from the inherent yet unrevealed principle of appearance modelling, which is based on incorporating object-related appearance information without classification. In this report the presented system is applied to high-level facial pose tracking and compared to a state-of-the-art reference method.