Name
Title | ||
|---|---|---|
Detection of Curvilinear Objects in Noisy Image using Feature-Adapted Beamlet Transform |
Abstract | ||
|---|---|---|
This paper addresses the problem of detecting features running along lines or piecewise constant curves. Our method is adapted either for common image features like edges or ridges as well as any kind of features that can be designed by a priori knowledge. The main contribution of this paper is to unify the well-known Beamlet transform, introduced by Donoho et al. [1], with linear filtering technique in order to define what we call the Feature-adapted Beamlet transform. If the desired feature is chosen to belong to the class of steerable filters, our method can be achieved in linear time and can be easily implemented on a parallel machine. We present some experimental results both on edge- and ridge-like features that demonstrate the substantial improvement over classical feature detectors. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/ICASSP.2007.366135 | ICASSP |
Keywords | Field | DocType |
filtering theory,object detection,parallel machines,piecewise constant techniques,transforms,curvilinear object detection,feature-adapted Beamlet transform,image noise,linear filtering technique,parallel machine,piecewise constant curves,Beamlet transform,biology,curvilinear objects,features detection,steerable filters | Object detection,Mathematical optimization,Linear filter,Pattern recognition,Feature (computer vision),Computer science,A priori and a posteriori,Filter (signal processing),Image segmentation,Artificial intelligence,Time complexity,Piecewise | Conference |
Volume | ISSN | Citations |
1 | 1520-6149 | 1 |
PageRank | References | Authors |
0.36 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sylvain Berlemont | 1 | 9 | 0.95 |
| Aaron Bensimon | 2 | 1 | 0.36 |
| Jean-Christophe Olivo-Marin | 3 | 747 | 77.94 |