{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/33102"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/33102","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Flycatcher : fusion of gaze with hierarchical image segmentation for robust object detection","abstract":"We present Flycatcher, a prototype system illustrating the idea of gaze- based image processing in the context of object segmentation for wearable photography. The prototype includes a wearable eye tracking device that captures real-time eyetraces of a user, and a wearable video camera that captures first-person perspective images of the user's visual environment. The system combines the deliberate eyetraces of the user with hierarchical image segmentation applied to scene images to achieve reliable object segmentation. In evaluations with certain classes of real-world images, fusion of gaze and image segmentation information led to higher object detection accuracy than either signal alone. Flycatcher may be integrated with assistive communication devices, enabling individuals with severe motor impairments to use eye control to communicate about objects in their environment. The system also represents a promising step toward an eye-driven interface for \"copy and paste\" visual memory augmentation in wearable computing applications.","abstract_html":"We present Flycatcher, a prototype system illustrating the idea of gaze- based image processing in the context of object segmentation for wearable photography. The prototype includes a wearable eye tracking device that captures real-time eyetraces of a user, and a wearable video camera that captures first-person perspective images of the user's visual environment. The system combines the deliberate eyetraces of the user with hierarchical image segmentation applied to scene images to achieve reliable object segmentation. In evaluations with certain classes of real-world images, fusion of gaze and image segmentation information led to higher object detection accuracy than either signal alone. Flycatcher may be integrated with assistive communication devices, enabling individuals with severe motor impairments to use eye control to communicate about objects in their environment. The system also represents a promising step toward an eye-driven interface for "copy and paste" visual memory augmentation in wearable computing applications.","abstract_has_math":false,"creators":["Bartelma, Jeffrey M"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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