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Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/DB6996CE-C724-4B6F-94FC-645EF0768387 | ||
| Year | 2014 | ||
| Type of Item | Conference Full Paper | ||
| License |
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| Bibliographic Citation | G.A. Koulieris, G. Drettakis, D. Cunningham, K. Mania ,High level saliency prediction for smart game balancing,"in 2014 ACM SIGGRAPH ,pp.73-73.doi:10.1145/2614106.2614157 https://doi.org/10.1145/2614106.2614157 | ||
| Appears in Collections |
Predicting visual attention can significantly improve scene design, interactivity and rendering. For example, image synthesis can be accelerated by reducing computation on non-attended scene regions; attention can also be used to improve LOD. Most previous attention models are based on low-level image features, as it is computationally and conceptually challenging to take into account highlevel factors such as scene context, topology or task.
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