{"@context":"http://iiif.io/api/presentation/2/context.json","@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/manifest.json","@type":"sc:Manifest","label":"Gaze Behavior Detection System Based on the Object Image","metadata":[{"label":"dc.description.sponsorship","value":"This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree."},{"label":"dc.format","value":"Monograph"},{"label":"dc.format.medium","value":"Electronic Resource"},{"label":"dc.identifier.uri","value":"http://hdl.handle.net/11401/77468"},{"label":"dc.language.iso","value":"en_US"},{"label":"dc.publisher","value":"The Graduate School, Stony Brook University: Stony Brook, NY."},{"label":"dcterms.abstract","value":"This thesis presents a design methodology of a low-cost noninvasive gaze tracking system to detect gaze behavior when user is browsing internet or reading material on computer. The user's face image is captured and processed in real-time. By means of C++ and OpenCV library, the system detects face, eye region with Haar feature-based cascade classifier. Eye center is detected by contouring dark area in eye region and finding the center of largest area among contoured dark areas. The detected eye center is mapped to gaze point on computer screen after four point calibration. The average angular error is 1.96 degree, which is comparable to other proposed techniques. During the experiment, the gaze point is displayed real-time with eye movement, and its coordinate as well as the gazed object are recorded in file. The system represents image information in unit area, object, scene, and frame hierarchy structure. With the gaze point data and image information, it is able to analyze gaze duration among objects and understand user's gaze behavior."},{"label":"dcterms.available","value":"2017-09-20T16:52:45Z"},{"label":"dcterms.contributor","value":"Hong, Sangjin"},{"label":"dcterms.creator","value":"Huang, Yunkai"},{"label":"dcterms.dateAccepted","value":"2017-09-20T16:52:45Z"},{"label":"dcterms.dateSubmitted","value":"2017-09-20T16:52:45Z"},{"label":"dcterms.description","value":"Department of Electrical Engineering."},{"label":"dcterms.extent","value":"44 pg."},{"label":"dcterms.format","value":"Monograph"},{"label":"dcterms.identifier","value":"http://hdl.handle.net/11401/77468"},{"label":"dcterms.issued","value":"2013-12-01"},{"label":"dcterms.language","value":"en_US"},{"label":"dcterms.provenance","value":"Made available in DSpace on 2017-09-20T16:52:45Z (GMT). No. of bitstreams: 1\nHuang_grad.sunysb_0771M_11342.pdf: 2847145 bytes, checksum: 19730b783613220e63c350305c87eeec (MD5)\n  Previous issue date:    1"},{"label":"dcterms.publisher","value":"The Graduate School, Stony Brook University: Stony Brook, NY."},{"label":"dcterms.subject","value":"camera, gaze behavior detection, gaze tracking, opencv"},{"label":"dcterms.title","value":"Gaze Behavior Detection System Based on the Object Image"},{"label":"dcterms.type","value":"Thesis"},{"label":"dc.type","value":"Thesis"}],"description":"This manifest was generated dynamically","viewingDirection":"left-to-right","sequences":[{"@type":"sc:Sequence","canvases":[{"@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/canvas/page-1.json","@type":"sc:Canvas","label":"Page 1","height":1650,"width":1275,"images":[{"@type":"oa:Annotation","motivation":"sc:painting","resource":{"@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/95%2F84%2F88%2F95848808672524020000081448952097998257/full/full/0/default.jpg","@type":"dctypes:Image","format":"image/jpeg","height":1650,"width":1275,"service":{"@context":"http://iiif.io/api/image/2/context.json","@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/95%2F84%2F88%2F95848808672524020000081448952097998257","profile":"http://iiif.io/api/image/2/level2.json"}},"on":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/canvas/page-1.json"}]}]}]}