Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Active shape models—their training and application
Computer Vision and Image Understanding
Feature tracking and motion classification using a switchable model Kalman filter
BMVC 94 Proceedings of the conference on British machine vision (vol. 2)
Statistical Optimization for Geometric Computation: Theory and Practice
Statistical Optimization for Geometric Computation: Theory and Practice
Metric-Based Methods for Adaptive Model Selection and Regularization
Machine Learning
Journal of Cognitive Neuroscience
A new metric-based approach to model selection
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Selection of local optical flow models by means of residual analysis
Proceedings of the 29th DAGM conference on Pattern recognition
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Many problems in computer vision involve a choice of the most suitable model for a set of data. Typically one wishes to choose a model which best represents the data in a way that generalises to unseen data without overfitting. We propose an algorithm in which the quality of a model match can be determined by calculating how well the distribution of model residuals matches a distribution estimated from the noise on the data. The distribution of residuals has two components - the measurement noise, and the noise caused by the uncertainty in the model parameters. If the model is too complex to be supported by the data, then there will be large uncertainty in the parameters. We demonstrate that the algorithm can be used to select appropriate model complexity in a variety of problems, including polynomial fitting, and selecting the number of modes to match a shape model to noisy data.