Name
Abstract | ||
|---|---|---|
Recently, cross-spectral stereo-camera setups found their way from special applications to mass market, especially in smartphones, automotive systems, or drones. In the following, a novel concept is introduced to bring stereo cameras and cross-spectral disparity estimation together. So far, either mono-modal stereo algorithms exist that are not suitable for cross-spectral image registration, or structural template matching is applied that achieves a low quality. To overcome these limitations, a technique is proposed to synthesize arbitrary spectral components from widely available color stereo databases, and to retrain mono-modal deep learning methods. In this contribution, the estimation of spectral bands based on random processes is shown together with noise models, which also allow for a robust registration of narrowband components. The theoretical examination is completed by an extensive evaluation, including a self-manufactured cross-spectral camera setup. In comparison to state-of-the-art techniques, the end-point error is on average reduced by a factor of seven. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICIP40778.2020.9191353 | 2020 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) |
Keywords | DocType | ISSN |
Stereo matching, disparity estimation, cross-spectral imaging | Conference | 1522-4880 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nils Genser | 1 | 3 | 2.45 |
| Andreas Spruck | 2 | 0 | 1.01 |
| Jürgen Seiler | 3 | 145 | 28.28 |
| André Kaup | 4 | 861 | 127.24 |