Seeded region growing: an extensive and comparative study
Pattern Recognition Letters
Spherical parameter detection based on hierarchical Hough transform
Pattern Recognition Letters
Active estimation of distance in a robotic system that replicates human eye movement
Robotics and Autonomous Systems
A variational method for the recovery of dense 3D structure from motion
Robotics and Autonomous Systems
Acquisition of translational motion by the parallel trinocular
Information Sciences: an International Journal
Optic flow from unstable sequences through local velocity constancy maximization
Image and Vision Computing
Derivation of qualitative information in motion analysis
Image and Vision Computing
Effective pose estimation from point pairs
Image and Vision Computing
Recursive estimation of time-varying motion and structure parameters
Pattern Recognition
A review and evaluation of methods estimating ego-motion
Computer Vision and Image Understanding
Motion-information-based video retrieval system using rough pre-classification
Transactions on Rough Sets V
Image deformations are better than optical flow
Mathematical and Computer Modelling: An International Journal
A self-calibration model for hand-eye systems with motion estimation
Mathematical and Computer Modelling: An International Journal
Group-Valued regularization for analysis of articulated motion
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
3D Video Based Segmentation and Motion Estimation with Active Surface Evolution
Journal of Signal Processing Systems
Adjustable linear models for optic flow based obstacle avoidance
Computer Vision and Image Understanding
Local scene flow by tracking in intensity and depth
Journal of Visual Communication and Image Representation
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A new approach for the interpretation of optical flow fields is presented. The flow field, which can be produced by a sensor moving through an environment with several independently moving, rigid objects, is allowed to be sparse, noisy, and partially incorrect. The approach is based on two main stages. In the first stage, the flow field is partitioned into connected segments of flow vectors, where each segment is consistent with a rigid motion of a roughly planar surface. In the second stage, segments are grouped under the hypothesis that they are induced by a single, rigidly moving object. Each hypothesis is tested by searching for three-dimensional (3-D) motion parameters which are compatible with all the segments in the corresponding group. Once the motion parameters are recovered, the relative environmental depth can be estimated as well. Experiments based on real and simulated data are presented.