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Glints Detection in Noisy Images Using Graph Matching
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AI
DOI：10.1109/THMS.2022.3213656.png)
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Abstract
En 中文
Glints detection is important in gaze estimation with virtual reality equipment. In some complex scenarios, the glints detection may fail due to multiple reasons, such as the ambiguous correspondence between the multiple light sources or their reflected glints in the captured image, as well as the distinction between the good glints and the noisy glints caused by the lights reflect on the glasses that the users may wear. In this article, the glints detection is formulated as a graph matching problem between the two graphs constructed from the candidate glints and the rough predicted ones from the information given by the last frame. In addition, the optimal parameters for the matching are estimated using an Expectation Maximum algorithm-like algorithm. Finally, with the optimal parameters, the Kuhn-Munkras algorithm is used to get the optimal matching, and the successfully matched true glints are considered the correctly detected glints. Experiments are performed to validate the effectiveness of the proposed method.
Keywords:
Estimation
Noise measurement
Pupils
Predictive models
Prediction algorithms
Optimization
Light sources
Gaze estimation
glints detection
graph matching
Kuhn-Munkras (KM) algorithm
Journal
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4.4
Papers:
1.1K
Citations:
3.5K
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