Journal article
A maximum-likelihood approach to segmenting range data
IEEE journal of robotics and automation, v 4(3)
Jun 1988
Abstract
The problem of segmenting a range image into homogeneous regions in each of which the range data correspond to a different surface is considered. The segmentation sought is a maximum-likelihood (ML) segmentation. Only planes, cylinders, and spheres are considered as presented in the image. The basic approach to segmentation is to divide the range image into windows, classify each window as a particular surface primitive, and group like windows into surface regions. Mixed windows are detected by testing the hypothesis that a window is homogeneous. Homogeneous windows are classified according to a generalized likelihood ratio test which is computationally simple and incorporates information from adjacent windows. Grouping windows of the same surface types is cast as a weighted ML clustering problem. Finally, mixed windows are segmented using an ML hierarchical segmentation algorithm. A similar approach is taken for segmenting visible-light images of Lambertian objects illuminated by a point source at infinity.< >
Metrics
Details
- Title
- A maximum-likelihood approach to segmenting range data
- Creators
- R.D Rimey - University of Rhode IslandF.S Cohen - Drexel University
- Publication Details
- IEEE journal of robotics and automation, v 4(3)
- Publisher
- IEEE
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:A1988N213100004
- Scopus ID
- 2-s2.0-0024032793
- Other Identifier
- 991019173797704721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Automation & Control Systems
- Robotics