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http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/3870| Title: | Learning object boundary detection from motion data |
| Keywords: | machine learning self-supervised algorithm motion segmentation object boundary detection |
| Issue Date: | 9-Oct-2013 |
| Description: | This paper describes the initial results of a project to create a self-supervised algorithm for learning object segmentation from video data. Developmental psychology and computational experience have demonstrated that the motion segmentation of objects is a simpler, more primitive process than the detection of object boundaries by static image cues. Therefore, motion information provides a plausible supervision signal for learning the static boundary detection task and for evaluating performance on a test set. A video camera and previously developed background subtraction algorithms can automatically produce a large database of motion-segmented images for minimal cost. The purpose of this work is to use the information in such a database to learn how to detect the object boundaries in novel images using static information, such as color, texture, and shape. Singapore-MIT Alliance (SMA) |
| URI: | http://koha.mediu.edu.my:8181/xmlui/handle/1721 |
| Other Identifiers: | http://hdl.handle.net/1721.1/3870 |
| Appears in Collections: | MIT Items |
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