Name
Abstract | ||
|---|---|---|
This paper investigated a probabilistic method using likelihood and granularity measures for scale selection in the case of anisotropic diffusion filtering. A multi-scale stack of diffused images is created, from which the optimal scale for subsequent analysis is extracted. For each pixel at each scale, a likelihood measure based upon the minimal description length principles is attached. By maximizing the likelihood that each pixel is optimal represented at a certain scale, a local scale map can be defined. The latter is subsequently augmented by a MRF-related granularity measure (Ising potential). This ensures the retainment of certain localized details. The global scale is defined as the scale at which the difference between the corresponding diffused image and the local scale image is minimal. The proposed approach is applied as a preprocessing step for the classification of high resolution air-borne multi-spectral images. Initial results show that the probabilistic scale selection identifies a suitable ideal scale. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1109/IGARSS.2003.1294258 | Geoscience and Remote Sensing Symposium, 2003. IGARSS '03. Proceedings. 2003 IEEE International |
Keywords | DocType | Volume |
anisotropic magnetoresistance,probabilistic method,image analysis,image resolution,information processing,filtering,image segmentation,multispectral imaging,iris,high resolution,anisotropic diffusion | Conference | 3 |
ISSN | ISBN | Citations |
2153-6996 | 0-7803-7929-2 | 0 |
PageRank | References | Authors |
0.34 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chan, J.C.-W. | 1 | 0 | 0.34 |
| Iris Vanhamel | 2 | 100 | 9.96 |
| Suliga, M. | 3 | 0 | 0.34 |