Name
Abstract | ||
|---|---|---|
In the paper we present the Progressive Probabilistic Hough Transform (PPHT). Unlike the Probabilistic Hough Transform (4) where Standard Hough Transform is performed on a pre-selected fraction of input points, PPHT min- imises the amount of computation needed to detect lines by exploiting the difference in the fraction of votes needed to reliably detect lines with differ- ent numbers of supporting points . The fraction of points used for voting need not be specified ad hoc or using a priori knowledge, as in the Probabilistic Hough Transform; it is a function of the inherent complexity of data. The algorithm is ideally suited for real-time applications with a fixed amount of available processing time, since voting and line detection is in- terleaved. The most salient features are likely to be detected first. Experi- ments show PPHT has, in many circumstances, advantages over the Standard Hough Transform. |
Year | Venue | Keywords |
|---|---|---|
1998 | BMVC | hough transform,a priori knowledge |
Field | DocType | Citations |
Scale-invariant feature transform,Computer vision,Pattern recognition,Voting,Computer science,A priori and a posteriori,Hough transform,Artificial intelligence,Probabilistic logic,Computation,Salient | Conference | 26 |
PageRank | References | Authors |
2.12 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiri Matas | 1 | 335 | 35.85 |
| Charles Galambos | 2 | 81 | 5.47 |
| J. Kittler | 3 | 14346 | 1465.03 |