Show simple item record

dc.contributor.supervisor Bruce, Neil (Computer Science) en_US
dc.contributor.author Fahimi, Ramin
dc.date.accessioned 2018-11-28T19:23:05Z
dc.date.available 2018-11-28T19:23:05Z
dc.date.issued 2018-11-24 en_US
dc.date.submitted 2018-11-25T04:42:55Z en
dc.identifier.uri http://hdl.handle.net/1993/33571
dc.description.abstract Visual saliency and eye movements have been well studied, mostly in the capacity of predicting topographical spatial saliency maps. In this thesis, we examine the problem of sequential selection and sampling of image content in detail. Careful scrutiny is applied to existing metrics for measuring success of sequential selection strategies, and a new family of metrics is proposed with an intuitive interpretation and that provides more discriminative power in revealing differences between viewing patterns or computational models. This is accompanied by experimentation based on classic strategies for simulating sequential selection from traditional representations of saliency, and deep neural networks that produce sequences by construction. Experiments provide strong support for the necessity of sequential analysis of attention and a roadmap for moving forward. en_US
dc.subject eye-tracking, Saliency, visual-attention, scanpath en_US
dc.title Sequential selection, saliency and scanpaths en_US
dc.degree.discipline Computer Science en_US
dc.contributor.examiningcommittee Wang, Yang (Computer science) Morrison, Jason (Biosystems Engineering) en_US
dc.degree.level Master of Science (M.Sc.) en_US
dc.description.note February 2019 en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

View Statistics