{"id":161965,"date":"2011-10-01T00:00:00","date_gmt":"2011-10-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/region-of-interest-determination-using-human-computation\/"},"modified":"2018-10-16T20:04:43","modified_gmt":"2018-10-17T03:04:43","slug":"region-of-interest-determination-using-human-computation","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/region-of-interest-determination-using-human-computation\/","title":{"rendered":"Region of Interest Determination Using Human Computation"},"content":{"rendered":"<div class=\"asset-content\">\n<p>The ability to identify and track visually interesting regions has many practical applications \u2013 for example, in image and video compression, visual marketing and foveal machine vision. Due to challenges in modeling the peculiarities of human physiological and psychological responses, automatic detection of fixation points is an open problem. Indeed, no objective methods are currently capable of fully modeling the human perception of regions of interest (ROIs). Thus, research often relies on user studies with eye tracking systems. In this paper we propose a cost effective and convenient alternative, obtained by having internet workers annotate videos with ROI coordinates. The workers use an interactive video player with a simulated mouse-driven fovea, which models the fall-off in resolution of the human visual system. Since this approach is not supervised, we implement methods for identifying inaccurate or malicious results. Using this proposal, one can collect ROI data in an automated fashion, and at a much lower cost than laboratory studies.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The ability to identify and track visually interesting regions has many practical applications \u2013 for example, in image and video compression, visual marketing and foveal machine vision. Due to challenges in modeling the peculiarities of human physiological and psychological responses, automatic detection of fixation points is an open problem. Indeed, no objective methods are currently [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13551],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-161965","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-graphics-and-multimedia","msr-locale-en_us"],"msr_publishername":"IEEE 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