Name
Abstract | ||
|---|---|---|
In this paper, we propose a statistical scheme to judge the activity level measurement (ALM) that is based on wavelet-domain hidden Markov model (WD-HMM) and maximum likelihood (MLK). The source images are firstly decomposed by the wavelets and only the coefficients in the high frequency (HH) are utilized. Considering the shift-variance of wavelets, the merged image is obtained from the source images directly. The regions of each source image are obtained by the Hough transform (HT) and their ALM are decided by the ALM of their coefficients in HH according to MLK. Finally, two multi focus images are merged by our new framework. The fusion results show the high ability of our scheme in preserving edge information and avoiding shift-variant. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11739685_115 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Keywords | Field | DocType |
statistical scheme,fusion result,edge information,maximum likelihood,activity level measurement,statistical image fusion scheme,merged image,high ability,multi focus image,source image,high frequency,multi focus application,hidden markov model,hough transform,image fusion | Image fusion,Pattern recognition,Markov model,Computer science,Image processing,Hough transform,Sensor fusion,Multi focus,Artificial intelligence,Hidden Markov model,Wavelet | Conference |
Volume | Issue | ISSN |
3930 LNAI | null | 16113349 |
ISBN | Citations | PageRank |
3-540-33584-6 | 0 | 0.34 |
References | Authors | |
9 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Z. W. Liao | 1 | 0 | 0.34 |
| S. X. Hu | 2 | 1 | 1.72 |
| W. F. Chen | 3 | 0 | 0.34 |
| Y. Y. Tang | 4 | 416 | 165.12 |
| T. Z. Huang | 5 | 115 | 18.95 |