Name
Abstract | ||
|---|---|---|
Recent progress in computational photography has shown that we can acquire physical information beyond visible (RGB) image representations. In particular, we can acquire near-infrared (NIR) cues with only slight modification to any standard digital camera. In this paper, we study whether this extra channel can improve semantic image segmentation. Based on a state-of-the-art segmentation framework and a novel manually segmented image database that contains 4-channel images (RGB+NIR), we study how to best incorporate the specific characteristics of the NIR response. We show that it leads to improved performances for 7 classes out of 10 in the proposed dataset and discuss the results with respect to the physical properties of the NIR response. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-33868-7_46 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
state-of-the-art segmentation framework,near-infrared channel,4-channel image,physical property,physical information,semantic image segmentation,nir response,segmented image database,image representation,extra channel,computational photography | Conditional random field,Computer vision,Scale-space segmentation,Pattern recognition,Computer science,Segmentation,Physical information,Computational photography,Segmentation-based object categorization,Digital camera,RGB color model,Artificial intelligence | Conference |
Volume | ISSN | Citations |
7584 | 0302-9743 | 8 |
PageRank | References | Authors |
0.54 | 18 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Neda Salamati | 1 | 41 | 3.22 |
| Diane Larlus | 2 | 864 | 53.74 |
| Gabriela Csurka | 3 | 972 | 85.08 |
| Sabine Süsstrunk | 4 | 4984 | 207.02 |