Robust regression and outlier detection
Robust regression and outlier detection
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
The Development and Comparison of Robust Methodsfor Estimating the Fundamental Matrix
International Journal of Computer Vision
Recognizing Facial Expressions in Image Sequences Using Local Parameterized Models of Image Motion
International Journal of Computer Vision
An anthropometric face model using variational techniques
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
Synthesizing realistic facial expressions from photographs
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust computer vision: an interdisciplinary challenge
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
MLESAC: a new robust estimator with application to estimating image geometry
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Optical Flow Constraints on Deformable Models with Applications to Face Tracking
International Journal of Computer Vision
International Journal of Computer Vision
Modeling and Animating Realistic Faces from Images
International Journal of Computer Vision
IMPSAC: Synthesis of Importance Sampling and Random Sample Consensus
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Computer Vision through Kernel Density Estimation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Statistical Cue Integration in DAG Deformable Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical cue estimation for model-based shape and motion tracking
Statistical cue estimation for model-based shape and motion tracking
Efficient, Robust and Accurate Fitting of a 3D Morphable Model
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Robust Regression with Projection Based M-estimators
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Capturing Subtle Facial Motions in 3D Face Tracking
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Accurate face models from uncalibrated and Ill-Lit video sequences
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Sparse deformable models with application to cardiac motion analysis
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
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Deformable model tracking is a powerful methodology that allows us to track the evolution of high-dimensional parameter vectors from uncalibrated monocular video sequences. The core of the approach consists of using low-level vision algorithms, such as edge trackers or optical flow, to collect a large number of 2D displacements, or motion measurements, at selected model points and mapping them into 3D space with the model Jacobians. However, the low-level algorithms are prone to errors and outliers, which can skew the entire tracking procedure if left unchecked. There are several known techniques in the literature, such as RANSAC, that can find and reject outliers. Unfortunately, these approaches are not easily mapped into the deformable model tracking framework, where there is no closed-form algebraic mapping from samples to the underlying parameter space. In this paper, we present three simple, yet effective ways to find the outliers. We validate and compare these approaches in an 11-parameter deformable face tracking application against ground truth data.