Name
Abstract | ||
|---|---|---|
Compositing is one of the most commonly performed operations in computer graphics. A realistic composite requires adjusting the appearance of the foreground and background so that they appear compatible; unfortunately, this task is challenging and poorly understood. We use statistical and visual perception experiments to study the realism of image composites. First, we evaluate a number of standard 2D image statistical measures, and identify those that are most significant in determining the realism of a composite. Then, we perform a human subjects experiment to determine how the changes in these key statistics influence human judgements of composite realism. Finally, we describe a data-driven algorithm that automatically adjusts these statistical measures in a foreground to make it more compatible with its background in a composite. We show a number of compositing results, and evaluate the performance of both our algorithm and previous work with a human subjects study. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2185520.2185580 | ACM Trans. Graph. |
Keywords | DocType | Volume |
image statistical measure,realistic composite,composite realism,human subjects study,compositing result,statistical measure,image composite,human subjects experiment,data-driven algorithm,human judgement | Journal | 31 |
Issue | ISSN | Citations |
4 | 0730-0301 | 26 |
PageRank | References | Authors |
0.93 | 18 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Su Xue | 1 | 49 | 3.82 |
| Aseem Agarwala | 2 | 3125 | 178.39 |
| Julie Dorsey | 3 | 2535 | 182.80 |
| Holly Rushmeier | 4 | 2294 | 334.25 |