Name
Abstract | ||
|---|---|---|
In many image analysis applications there is a need to extract curves in noisy images. To achieve a more robust extraction, one can exploit correlations of oriented features over a spatial context in the image. Tensor voting is an existing technique to extract features in this way. In this paper, we present a new computational scheme for tensor voting on a dense field of rank-2 tensors. Using steerable filter theory, it is possible to rewrite the tensor voting operation as a linear combination of complex-valued convolutions. This approach has computational advantages since convolutions can be implemented efficiently. We provide speed measurements to indicate the gain in speed, and illustrate the use of steerable tensor voting on medical applications. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11744085_18 | ECCV (4) |
Keywords | Field | DocType |
speed measurement,image analysis application,computational advantage,tensor voting operation,tensor voting,complex-valued convolution,new computational scheme,efficient method,steerable tensor voting,steerable filter theory,noisy image,image analysis,spatial context | Linear combination,Computer vision,Tensor,Computer science,Convolution,Tensor voting,Algorithm,Image processing,Exploit,Artificial intelligence,Spatial contextual awareness,Steerable filter | Conference |
Volume | ISSN | ISBN |
3954 | 0302-9743 | 3-540-33838-1 |
Citations | PageRank | References |
20 | 1.59 | 7 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Erik Franken | 1 | 119 | 7.73 |
| Markus van Almsick | 2 | 56 | 4.80 |
| Peter M. J. Rongen | 3 | 52 | 5.48 |
| L. M. J. Florack | 4 | 1212 | 210.47 |
| Bart ter Haar Romeny | 5 | 224 | 16.62 |