Name
Abstract | ||
|---|---|---|
Given a time sequence of digital images of a high-noise environment, the authors address the problem of detecting pixel-sized, barely discernible moving objects whose position and trajectories are unknown. The sequences may be temporally sparse and contain significant frame-to-frame drifting background clutter as caused by relative motion between the sensor array and natural terrain, ocean, or clouds. A general, two-step approach is presented. First, time correlation and space-varying background structure are removed. Second, a large, dense set of pixel-sized space-time trajectories are hypothesized and tested in the innovations sequence. The search space is organized into a tree structure. A sequential statistical technique, multistage hypothesis testing, optimized for the innovations model, is used to test the multiple hypotheses and prune the tree-structured list of candidate trajectories |
Year | DOI | Venue |
|---|---|---|
1988 | 10.1109/CVPR.1988.196309 | CVPR |
Keywords | Field | DocType |
computerised pattern recognition,computerised picture processing,statistical analysis,trees (mathematics),clutter,digital images,image sequences,relative motion,sequential statistical technique,space-time trajectories,space-varying background structure,target detection,time correlation,tree search algorithm,search space,sequential analysis,digital image,space time,tree structure,testing,search algorithm,hypothesis test,sensor array,pixel | Object detection,Computer vision,Tree traversal,Pattern recognition,Clutter,Computer science,Sensor array,Digital image,Tree structure,Artificial intelligence,Pixel,Statistical hypothesis testing | Conference |
Volume | Issue | ISSN |
1988 | 1 | 1063-6919 |
Citations | PageRank | References |
6 | 1.11 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Steven D. Blostein | 1 | 329 | 61.46 |
| Huang, T.S. | 2 | 6 | 1.11 |