Motion and Structure From Two Perspective Views: Algorithms, Error Analysis, and Error Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Moving Objects Using the Rigidity Constraint
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learned Models for Estimation of Rigid and ArticulatedHuman Motion from Stationary or Moving Camera
International Journal of Computer Vision
Robust recovery of multiple light source based on local light source constant constraint
Pattern Recognition Letters
Coarse Registration of Surface Patches with Local Symmetries
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Parametric model-based motion segmentation using surface selection criterion
Computer Vision and Image Understanding
Computer Vision and Image Understanding
Parametric model-based motion segmentation using surface selection criterion
Computer Vision and Image Understanding
Theoretical quantification of shape distortion in fuzzy Hough transform
Fuzzy Sets and Systems
International Journal of Computer Vision
A review and evaluation of methods estimating ego-motion
Computer Vision and Image Understanding
| Hi-index | 0.14 |
We present a method to determine 3D motion and structure of multiple objects from two perspective views, using adaptive Hough transform. In our method, segmentation is determined based on a 3D rigidity constraint. Instead of searching candidate solutions over the entire five-dimensional translation and rotation parameter space, we only examine the two-dimensional translation space. We divide the input image into overlapping patches, and, for each sample of the translation space, we compute the rotation parameters of patches using least-squares fit. Every patch votes for a sample in the five-dimensional parameter space. For a patch containing multiple motions, we use a redescending M-estimator to compute rotation parameters of a dominant motion within the patch. To reduce computational and storage burdens of standard multidimensional Hough transform, we use adaptive Hough transform to iteratively refine the relevant parameter space in a "coarse-to-fine" fashion. Our method can robustly recover 3D motion parameters, reject outliers of the flow estimates, and deal with multiple moving objects present in the scene. Applications of the proposed method to both synthetic and real image sequences are demonstrated with promising results.