Title:
Grammatical Methods in Computer Vision: An Overview
Grammatical Methods in Computer Vision: An Overview
| dc.contributor.author | Chanda, Gaurav | |
| dc.contributor.author | Dellaert, Frank | |
| dc.date.accessioned | 2004-12-09T17:51:28Z | |
| dc.date.available | 2004-12-09T17:51:28Z | |
| dc.date.issued | 2004 | |
| dc.description.abstract | We review various methods and applications that have used grammars for solving inference problems in computer vision and pattern recognition. Grammars have been useful because they are intuitively simple to understand, and have very elegant representations. Their ability to model semantic interpretations of patterns, both spatial and temporal, have made them extremely popular in the research community. In this paper, we attempt to give an overview of what syntactic methods exist in the literature, and how they have been used as tools for pattern modeling and recognition. We also describe several practical applications, which have used them with great success. | en |
| dc.format.extent | 648459 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/1853/3738 | |
| dc.language.iso | en_US | |
| dc.publisher | Georgia Institute of Technology | en |
| dc.relation.ispartofseries | GVU Technical Report;GIT-GVU-04-29 | |
| dc.subject | Context free grammars | en |
| dc.subject | Attribute grammars | en |
| dc.subject | Parsing | en |
| dc.subject | Graph grammars | en |
| dc.subject | Array grammars | en |
| dc.subject | Picture language | en |
| dc.subject | Stochastic grammars | en |
| dc.subject | Shape grammars | en |
| dc.title | Grammatical Methods in Computer Vision: An Overview | en |
| dc.type | Text | |
| dc.type.genre | Technical Report | |
| dspace.entity.type | Publication | |
| local.contributor.author | Dellaert, Frank | |
| local.contributor.corporatename | Institute for Robotics and Intelligent Machines (IRIM) | |
| local.contributor.corporatename | GVU Center | |
| local.relation.ispartofseries | GVU Technical Report Series | |
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