Interpretation of visual motion: a computational study
Interpretation of visual motion: a computational study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Approximations for the MarginalLikelihood of Bayesian Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
International Journal of Computer Vision
Model selection techniques and merging rules for range data segmentation algorithms
Computer Vision and Image Understanding
Modelling Objects having Quadric Surfaces Incorporating Geometric cCnstraints
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Evaluation and Selection of Models for Motion Segmentation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
An Assessment of Information Criteria for Motion Model Selection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Model Selection and Surface Merging in Reconstruction Algorithms
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Motion Analysis: Model Selection and Motion Segmentation
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Uncertainty Modeling and Model Selection for Geometric Inference
IEEE Transactions on Pattern Analysis and Machine Intelligence
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
Universal coding, information, prediction, and estimation
IEEE Transactions on Information Theory
Range image segmentation using surface selection criterion
IEEE Transactions on Image Processing
International Journal of Computer Vision
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During last three decades many model selection techniques have been developed, many of those have also been employed in computer vision applications. Interestingly, most of those criteria are based upon assumptions that are rarely realised in practical applications. As a result, the question of which model selection criterion works best for a particular application is of interest to many computer vision researcher and practitioners alike. This paper is an attempt to provide a satisfactory answer to this question for some well-known computer vision applications. Here, we present a comparative study of a large number of the existing model selection criteria for three computer vision tasks including: range modelling, motion modelling and merging of 3D surfaces in range data. Compared with other criteria, the results show that the surface selection criterion (SSC) appears to perform generally better for the above applications.