An Automated High-Level Saliency Predictor for Smart Game Balancing.
- Submitting institution
-
University of Durham
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 115986
- Type
- D - Journal article
- DOI
-
10.1145/2637479
- Title of journal
- ACM Transactions on Applied Perception
- Article number
- 17
- First page
- -
- Volume
- 11
- Issue
- 4
- ISSN
- 15443558
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2014
- URL
-
https://doi.org/10.1145/2637479
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
A - Innovative Computing
- Citation count
- 6
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The proposed model pioneered the use of high-level scene context features such as object topology and task-related object function that influence fixations in virtual environments and as a result was praised by both a Eurographics State of the Art report on perception-driven accelerated rendering (10.1111/cgf.13150) and a 30-year retrospective survey on gaze-based interaction (10.1016/j.cag.2018.04.002). A successful application of the proposed gaze-prediction model in mobile graphics rendering by the same authors won 3rd place at the prestigious Microsoft-sponsored Graduate Student Research Competition at ACM SIGGRAPH 2014 among more than 100 contestants (src.acm.org/winners/2015).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -