Name
Abstract | ||
|---|---|---|
A canonical model is proposed for object classes in aerial images. This model is motivated by the observation that geographic regions of interest are characterized by collec- tions of texture motifs corresponding to geographic pro- cesses. Furthermore, the spatial arrangement of the motifs is an important discriminating characteristic. In our approach, the states of a Hidden Markov Model (HMM) correspond to the geographic processes and the state transitions corre- spond to the spatial arrangement of the processes. A one- dimensional approach reduces the computational complex- ity. The model is shown to be effective in characterizing objects of interest in spatial datasets in terms of their under- lying texture motifs. The potential of the model for identi- fying the classes of unlabeled objects is demonstrated. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/ICIP.2002.1038161 | Image Processing. 2002. Proceedings. 2002 International Conference |
Keywords | Field | DocType |
computational complexity,geography,hidden Markov models,image texture,HMM,aerial images,computational complexity reduction,geographic processes,geographic regions of interest,hidden Markov models,motifs spatial arrangement,object classes modelling,one-dimensional approach,spatial datasets,state transitions,texture motifs,unlabeled object identification | Computer vision,Pattern recognition,Image texture,Computer science,Canonical model,Artificial intelligence,Hidden Markov model,Computational complexity theory | Conference |
Volume | ISSN | Citations |
1 | 1522-4880 | 1 |
PageRank | References | Authors |
0.39 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shawn Newsam | 1 | 800 | 51.85 |
| Sitaram Bhagavathy | 2 | 114 | 9.82 |
| B. S. Manjunath | 3 | 7561 | 783.37 |