Geometric Information Criterion for Model Selection
International Journal of Computer Vision
On the Fitting of Surfaces to Data with Covariances
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Statistical Optimization for Geometric Computation: Theory and Practice
Statistical Optimization for Geometric Computation: Theory and Practice
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Stabilizing Image Mosaicing by Model Selection
SMILE '00 Revised Papers from Second European Workshop on 3D Structure from Multiple Images of Large-Scale Environments
Self-Evaluation for Active Vision by the Geometric Information Criterion
CAIP '97 Proceedings of the 7th International Conference on Computer Analysis of Images and Patterns
Uncertainty Modeling and Model Selection for Geometric Inference
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance evaluation of iterative geometric fitting algorithms
Computational Statistics & Data Analysis
Optimal computation of 3-D similarity: Gauss-Newton vs. Gauss-Helmert
Computational Statistics & Data Analysis
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Given 3-D sensor data of points slightly moving in space, we consider the problem of discerning whether or not translation, rotation, and scale change take place and to what extent. For this purpose, we propose a new method for fitting various motion models to 3-D sensor data. Based on the observation that subgroups of 3-D affinity are defined by imposing various internal constraints on the parameters, our method fits 3-D affinity with internal constraints using the scheme of EFNS, which, unlike conventional methods, dispenses with any particular parameterizations for particular motion models. Then, we apply our method to simulated stereo vision data for motion interpretation, using various model selection criteria. We also apply our method to the GPS geodetic data of the land deformation in northeast Japan, where a massive earthquake took place on 11 March 2011. It is expected that our proposed technique will be widely used for 3-D analysis involving hierarchical motion models in various domains including computer vision, robotic navigation, and geodetic science.