Computing internally constrained motion of 3-D sensor data for motion interpretation
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.
3-d affinity internal constraints stereo vision 3-d similarity 3-d rigid motion geometric model selection geodetic sensing