Name
Abstract | ||
|---|---|---|
We have introduced the Statistical Hough transform [2] that extends the standard Hough transform by using a kernel mixture as a robust alternative to the 2 dimensional accumulator histogram. This work develops further this framework by proposing a Bayesian classification scheme to associate the spatial coordinates (x, y) to one particular class defined in the Hough space (theta, rho). In a first step, we segment the Hough space into meaningful classes. Then using the inverse Radon transform, we backproject the different classes into the image space. We illustrate our approach on a synthetic image and on real images. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICPR.2008.4761109 | 19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6 |
Keywords | Field | DocType |
bayesian classification,hough transform,radon transform,statistical analysis,image segmentation,kernel,pixel,image classification,2 dimensional,bayesian methods | Kernel (linear algebra),Computer vision,Histogram,Scale-invariant feature transform,Pattern recognition,Computer science,Hough transform,Image segmentation,Artificial intelligence,Real image,Contextual image classification,Radon transform | Conference |
ISSN | Citations | PageRank |
1051-4651 | 1 | 0.38 |
References | Authors | |
3 | 1 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rozenn Dahyot | 1 | 340 | 32.62 |