Fast segmentation of range images into planar regions by scan line grouping
Machine Vision and Applications
The Development and Comparison of Robust Methodsfor Estimating the Fundamental Matrix
International Journal of Computer Vision
MLESAC: a new robust estimator with application to estimating image geometry
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Range Image Segmentation by an Effective Jump-Diffusion Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
| Hi-index | 0.00 |
In many applications it is necessary to describe some experimental data with one or more geometric models. A naive approach to find multiple models consists on the sequential application of a robust regression estimator, such as RANSAC [2], and removing inliers each time that a model instance was detected. The quality of the final result in the sequential approach depends strongly on the order on which the models were. The MuSAC method proposed in this paper discovers several models at the same time, based on the consensus of each model. To reduce bad correspondences between data points and geometric models, this paper also introduces a novel distance for laser range sensors. We use the MuSAC algorithm to find models from 2D range images on cluttered environments with promising results.