Name
Abstract | ||
|---|---|---|
This paper focuses on a training-based method to reconstruct a scene's spectral reflectance from a single RGB image captured by a camera with known spectral response. In particular, we explore a new strategy to use training images to model the mapping between camera-specific RGB values and scene reflectance spectra. Our method is based on a radial basis function network that leverages RGB white-balancing to normalize the scene illumination to recover the scene reflectance. We show that our method provides the best result against three state-of-art methods, especially when the tested illumination is not included in the training stage. In addition, we also show an effective approach to recover the spectral illumination from the reconstructed spectral reflectance and RGB image. As a part of this work, we present a newly captured, publicly available, data set of hyperspectral images that are useful for addressing problems pertaining to spectral imaging, analysis and processing. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-10584-0_13 | COMPUTER VISION - ECCV 2014, PT VII |
Keywords | Field | DocType |
Spectral Image,Training Image,Hyperspectral Image,Radial Basis Function Network,Color Constancy | Computer vision,Color constancy,Radial basis function network,Spectral imaging,Normalization (statistics),Computer science,RGB color space,Spectral line,Hyperspectral imaging,Artificial intelligence,RGB color model | Conference |
Volume | ISSN | Citations |
8695 | 0302-9743 | 20 |
PageRank | References | Authors |
0.86 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| R. M. H. Nguyen | 1 | 54 | 3.61 |
| Dilip K. Prasad | 2 | 162 | 21.84 |
| Michael S. Brown | 3 | 2122 | 129.13 |