Name
Abstract | ||
|---|---|---|
Detecting geometric primitives in images is one of the basic tasks of computer vision. We introduce a new Hough transform aimed at improving curve detection accuracy and robustness as well as computational efficiency. Robustness and accuracy improvement is achieved by analytically propagating the errors with image pixels to the estimated curve parameters. The errors with the curve parameters are then used to determine the contribution of pixels to the accumulator array. The computational efficiency is achieved by choosing best-distinguished pixels and by performing progressive detection. The detection approaches are given for line and circle. The concept can be applied to other curves, such as circle and ellipse. The experiments on line detection show improved performance with our technique |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1109/ICIP.2001.958983 | ICIP (1) |
Keywords | Field | DocType |
robustness,error propagation,curve detection accuracy,parameter estimation,computational efficiency,line detection,computational geometry,progressive detection,edge detection,computer vision,geometric primitives,hough transform,image pixels,curve parameter estimation,accumulator array,performance,hough transforms,circle detection,computer science,pixel,image analysis | Computer vision,Propagation of uncertainty,Computer science,Edge detection,Computational geometry,Hough transform,Geometric primitive,Robustness (computer science),Artificial intelligence,Pixel,Ellipse | Conference |
Volume | ISSN | ISBN |
1 | 1522-4880 | 0-7803-6725-1 |
Citations | PageRank | References |
1 | 0.38 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yonghong Xie | 1 | 122 | 14.43 |
| Qiang Ji | 2 | 66 | 4.19 |