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On the analysis of local and global features for hyperemia grading.

, , , , , and . ICMV, volume 10341 of SPIE Proceedings, page 103411T. SPIE, (2016)

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On the analysis of local and global features for hyperemia grading., , , , , and . ICMV, volume 10341 of SPIE Proceedings, page 103411T. SPIE, (2016)Precise segmentation of the bulbar conjunctiva for hyperaemia images., , , , and . Pattern Anal. Appl., 21 (2): 563-577 (2018)Comparing Machine Learning Techniques in a Hyperemia Grading Framework., , , , and . ICAART (2), page 423-429. SciTePress, (2016)iDEAS: A web-based system for dry eye assessment., , , , , , and . Computer Methods and Programs in Biomedicine, (2016)Statistical Comparison of Classifiers Applied to the Interferential Tear Film Lipid Layer Automatic Classification., , , , , and . Comp. Math. Methods in Medicine, (2012)Analysis of parameters for the automatic computation of the tear film break-up time test based on CCLRU standards., , , , , and . Computer Methods and Programs in Biomedicine, 113 (3): 715-724 (2014)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)Texture and Color Analysis for the Automatic Classification of the Eye Lipid Layer., , , , , and . IWANN (2), volume 6692 of Lecture Notes in Computer Science, page 66-73. Springer, (2011)On the analysis of feature selection techniques in a conjunctival hyperemia grading framework., , , , , and . ESANN, (2016)Colour Texture Segmentation of Tear Film Lipid Layer Images., , , , and . EUROCAST (2), volume 8112 of Lecture Notes in Computer Science, page 140-147. Springer, (2013)