Name
Abstract | ||
|---|---|---|
Illumination changes and their effects on scene appearance pose serious problems to many computer vision algorithms. In this paper, we present the benefits that a chromaticity-based approach can provide to illumination compensation. We consider three computationally inexpensive illumination models, and demonstrate that customizing these models for chromatically dissimilar regions reduces mean absolute difference (MAD) error by 70% to 80% over computing the models globally for the entire image. We demonstrate that models computed for a given color are somewhat effective for different colors with similar hues (increasing MAD error by a factor of 6), but are ineffective for colors with dissimilar hues (increasing MAD error by a factor of 15). Finally, we find that model choice is less important if the model is customized for chromatically dissimilar regions. Effects of webcamera drivers are considered. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-17289-2_21 | ISVC (1) |
Keywords | Field | DocType |
illumination change compensation technique,chromatic sensitivity,computer vision algorithm,illumination change,mad error,model choice,dissimilar hue,computationally inexpensive illumination model,different color,absolute difference,chromatically dissimilar region,chromaticity-based approach,mean absolute difference,computer vision | Mean difference,Computer vision,Chromatic scale,Pattern recognition,Computer science,Hue,Chromaticity,Computer vision algorithms,Artificial intelligence,Model choice | Conference |
Volume | ISSN | ISBN |
6453 | 0302-9743 | 3-642-17288-1 |
Citations | PageRank | References |
3 | 0.51 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Ryan Bales | 1 | 8 | 1.11 |
| Dana Forsthoefel | 2 | 11 | 2.18 |
| D. Scott Wills | 3 | 197 | 24.57 |
| Linda M Wills | 4 | 293 | 40.95 |