Name
Abstract | ||
|---|---|---|
In this paper, we propose two complete sets of similarity invariant descriptors under the Fourier-Mellin Transform and the Analytical Fourier-Mellin Transform (AFMT) frameworks respectively. Furthermore, their numerical properties are presented and be revealed through image reconstruction. Experimental results indicate that our proposed invariant descriptors can fully reconstruct the original image eliminating any existing similarity transformation (such as rotation, translation and scale) from the original image. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11595755_82 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
similarity invariants,novel complete set,proposed invariant descriptors,numerical property,complete set,analytical fourier-mellin transform,similarity invariant descriptors,image reconstruction,existing similarity transformation,fourier-mellin transform,original image,mellin transform,similarity transformation | Iterative reconstruction,Similitude,Matrix similarity,Pattern recognition,Computer science,Fourier transform,Invariant (mathematics),Artificial intelligence | Conference |
Volume | ISSN | ISBN |
3804 | 0302-9743 | 3-540-30750-8 |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongchuan Yu | 1 | 116 | 12.72 |
| Mohammed Bennamoun | 2 | 37 | 4.14 |