What Makes a Good Feature?
| dc.date.accessioned | 2004-10-04T14:24:15Z | |
| dc.date.accessioned | 2018-11-24T10:11:18Z | |
| dc.date.available | 2004-10-04T14:24:15Z | |
| dc.date.available | 2018-11-24T10:11:18Z | |
| dc.date.issued | 1992-04-01 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1721.1/5963 | |
| dc.identifier.uri | http://repository.aust.edu.ng/xmlui/handle/1721.1/5963 | |
| dc.description.abstract | Using a Bayesian framework, we place bounds on just what features are worth computing if inferences about the world properties are to be made from image data. Previously others have proposed that useful features reflect "non-accidental'' or "suspicious'' configurations (such as parallel or colinear lines). We make these notions more precise and show them to be context sensitive. | en_US |
| dc.format.extent | 42 p. | en_US |
| dc.format.extent | 2433280 bytes | |
| dc.format.extent | 1910701 bytes | |
| dc.language.iso | en_US | |
| dc.subject | computational vision | en_US |
| dc.subject | vision features | en_US |
| dc.subject | Bayesian model | en_US |
| dc.subject | svision psychophysics | en_US |
| dc.subject | color | en_US |
| dc.subject | motion | en_US |
| dc.title | What Makes a Good Feature? | en_US |
Files in this item
| Files | Size | Format | View |
|---|---|---|---|
| AIM-1356.pdf | 1.910Mb | application/pdf | View/ |
| AIM-1356.ps | 2.433Mb | application/postscript | View/ |