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dc.contributor.authorKomogortsev, Oleg ( )en_US
dc.contributor.authorJayarathna, Sampath ( Orcid Icon 0000-0002-4879-7309 )en_US
dc.contributor.authorKoh, Do Hyong ( )en_US
dc.contributor.authorGowda, Sandeep Munikrishne ( )en_US
dc.date.accessioned2009-11-23T10:03:26Z
dc.date.available2012-02-24T10:03:26Z
dc.date.issued2009-09-16en_US
dc.identifier.citationKomogortsev, O. V., Jayarathna, S., Koh, D. H., & Gowda, S. M. (2009). Qualitative and quantitative scoring and evaluation of the eye movement classification algorithm (Report No. TXSTATE-CS-TR-2009-16). Texas State University-San Marcos, Department of Computer Science.
dc.identifier.urihttps://digital.library.txstate.edu/handle/10877/2577
dc.description.abstractThis paper presents a set of qualitative and quantitative scores designed to assess performance of the various eye movement classification algorithms. The scores are designed to provide a foundation for the eye tracking researchers to communicate about the performance validity of various eye movement classification algorithms. The paper concentrates on the five algorithms in particular: Velocity Threshold Identification (I-VT), Dispersion Threshold Identification (I-DT), Minimum Spanning Tree Identification (MST), Hidden Markov Model Identification (I-HMM) and Kalman Filter Identification (I-KF). The paper presents an evaluation of the classification performance of each algorithm in the case when values of the input parameters are varied. Advantages provided by the new scores are discussed. Discussion on what is the \"best\".en_US
dc.formatText
dc.format.extent10 pages
dc.format.medium1 file (.pdf)
dc.language.isoen
dc.subjectEye movementsen_US
dc.subjectClassificationen_US
dc.subjectAlgorithmen_US
dc.subjectAnalysisen_US
dc.subjectScoringen_US
dc.subjectMetricsen_US
dc.subject.classificationEarth Sciencesen_US
dc.titleQualitative and Quantitative Scoring and Evaluation of the Eye Movement Classification Algorithmsen_US
dc.typepublishedVersion
txstate.documenttypeTechnical Report
dc.description.departmentComputer Science


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