Name
Abstract | ||
|---|---|---|
Capturing a picture that \"tells a story\" requires the ability to create the right composition. The two most important parameters controlling composition are the camera position and the focal length of the lens. The traditional paradigm is for a photographer to mentally visualize the desired picture, select the capture parameters to produce it, and finally take the photograph, thus committing to a particular composition. We propose to change this paradigm. To do this, we introduce computational zoom, a framework that allows a photographer to manipulate several aspects of composition in post-processing from a stack of pictures captured at different distances from the scene. We further define a multi-perspective camera model that can generate compositions that are not physically attainable, thus extending the photographer's control over factors such as the relative size of objects at different depths and the sense of depth of the picture. We show several applications and results of the proposed computational zoom framework. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3072959.3073687 | ACM Trans. Graph. |
Field | DocType | Volume |
Computer vision,Computer graphics (images),Computer science,Zoom,Focal length,Artificial intelligence,Appearance modeling | Journal | 36 |
Issue | ISSN | Citations |
4 | 0730-0301 | 1 |
PageRank | References | Authors |
0.38 | 24 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abhishek Badki | 1 | 5 | 1.12 |
| Orazio Gallo | 2 | 248 | 12.94 |
| Jan Kautz | 3 | 3615 | 198.77 |
| Pradeep Sen | 4 | 882 | 53.01 |