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Adaptive parameter computation for the automatic measure of the Tear Break-Up Time.

, , , , , , and . KES, volume 243 of Frontiers in Artificial Intelligence and Applications, page 1370-1379. IOS Press, (2012)

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Comparing Machine Learning Techniques in a Hyperemia Grading Framework., , , , and . ICAART (2), page 423-429. SciTePress, (2016)Break-Up Analysis of the Tear Film Based on Time, Location, Size and Shape of the Rupture Area., , , , and . ICIAR, volume 7950 of Lecture Notes in Computer Science, page 695-702. Springer, (2013)Precise segmentation of the bulbar conjunctiva for hyperaemia images., , , , and . Pattern Anal. Appl., 21 (2): 563-577 (2018)On the analysis of local and global features for hyperemia grading., , , , , and . ICMV, volume 10341 of SPIE Proceedings, page 103411T. SPIE, (2016)Adaptive parameter computation for the automatic measure of the Tear Break-Up Time., , , , , , and . KES, volume 243 of Frontiers in Artificial Intelligence and Applications, page 1370-1379. IOS Press, (2012)A Novel Framework for Hyperemia Grading Based on Artificial Neural Networks., , , and . IWANN (1), volume 9094 of Lecture Notes in Computer Science, page 263-275. Springer, (2015)Adaptive parameter computation for the automatic measure of the Tear Break-Up Time., , , , , , and . KES, volume 243 of Frontiers in Artificial Intelligence and Applications, page 1370-1379. IOS Press, (2012)Comparing Machine Learning Techniques in a Hyperemia Grading Framework., , , , and . ICAART (2), page 423-429. SciTePress, (2016)Defining the Optimal Region of Interest for Hyperemia Grading in the Bulbar Conjunctiva., , , , and . Comp. Math. Methods in Medicine, (2016)On the Automation of the Tear Film Non-invasive Break-up Test., , , , , and . CBMS, page 185-188. IEEE Computer Society, (2014)