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dc.contributor.advisorRandall Davis.en_US
dc.contributor.authorChien, Jonathanen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2015-12-16T15:54:17Z
dc.date.available2015-12-16T15:54:17Z
dc.date.copyright2015en_US
dc.date.issued2015en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/100299
dc.descriptionThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 85-86).en_US
dc.description.abstractIn this thesis, I designed and implemented improvements to an automatic classifier for the digitized clock drawing test, a diagnostic tool for assessing cognitive impairment, which asks the patient to draw an analog clock face using a digital pen. The classifier handles both the grouping of strokes into clock components and the subsequent labeling of those groups. Despite the domain-specificity, classification is a challenging problem because the subject often has cognitive or motor impairments. It is thus important for the classifier to be able to handle a wide range of input with distorted, overwritten, or missing components. I improve the robustness of the classifier, particularly for messy clinical data, by incorporating intrinsic stroke properties, developing additional symbol recognizers, and creating a global context evaluator. I describe in this thesis properties for isolated symbol recognition, features for symbol recognition and match scoring, as well as common sense rules based on a symbol's local and global context in a drawing. I combine these elements into a new system that locally maximizes a global label assignment based on match quality and context. I demonstrate that this system accurately recognizes a wide variety of clinical input, improving overall classification performance.en_US
dc.description.statementofresponsibilityby Jonathan Chien.en_US
dc.format.extent86 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleMarking Time : increased scope and accuracy for sketch classification of the clock drawing testen_US
dc.title.alternativeIncreased scope and accuracy for sketch classification of the clock drawing testen_US
dc.typeThesisen_US
dc.description.degreeM. Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc930617504en_US


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