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http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/3870Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.creator | Ross, Michael G. | - |
| dc.creator | Kaelbling, Leslie P. | - |
| dc.date | 2003-12-13T20:13:43Z | - |
| dc.date | 2003-12-13T20:13:43Z | - |
| dc.date | 2004-01 | - |
| dc.date.accessioned | 2013-10-09T02:33:10Z | - |
| dc.date.available | 2013-10-09T02:33:10Z | - |
| dc.date.issued | 2013-10-09 | - |
| dc.identifier | http://hdl.handle.net/1721.1/3870 | - |
| dc.identifier.uri | http://koha.mediu.edu.my:8181/xmlui/handle/1721 | - |
| dc.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. | - |
| dc.description | Singapore-MIT Alliance (SMA) | - |
| dc.format | 1234090 bytes | - |
| dc.format | application/pdf | - |
| dc.language | en_US | - |
| dc.relation | Computer Science (CS); | - |
| dc.subject | machine learning | - |
| dc.subject | self-supervised algorithm | - |
| dc.subject | motion segmentation | - |
| dc.subject | object boundary detection | - |
| dc.title | Learning object boundary detection from motion data | - |
| dc.type | Article | - |
| Appears in Collections: | MIT Items | |
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