On curve matching
Two algorithms to find the longest common subcurve of two 2D curves are presented. These algorithms are based on conversion of the curves into shape signature strings and application of string matching techniques to find long matching substrings, followed by direct curve matching of the correspondin...
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| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence 1990-05, Vol.12 (5), p.483-489 |
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| container_title | IEEE transactions on pattern analysis and machine intelligence |
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| creator | Wolfson, H.J. |
| description | Two algorithms to find the longest common subcurve of two 2D curves are presented. These algorithms are based on conversion of the curves into shape signature strings and application of string matching techniques to find long matching substrings, followed by direct curve matching of the corresponding candidate subcurves to find the longest matching subcurve. The first algorithm is of complexity O(n), where n is the number of sample points on the curves. The second one, while being theoretically somewhat less efficient, proved to be robust and efficient in practical applications. Both algorithms solve the problem of general curves without being dependent on some set of special points on the curves. The algorithms have industrial applications to problems of object assembly and object recognition. Experimental results are included. The algorithms can be easily extended to the 3D case.< > |
| doi_str_mv | 10.1109/34.55108 |
| format | Article |
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These algorithms are based on conversion of the curves into shape signature strings and application of string matching techniques to find long matching substrings, followed by direct curve matching of the corresponding candidate subcurves to find the longest matching subcurve. The first algorithm is of complexity O(n), where n is the number of sample points on the curves. The second one, while being theoretically somewhat less efficient, proved to be robust and efficient in practical applications. Both algorithms solve the problem of general curves without being dependent on some set of special points on the curves. The algorithms have industrial applications to problems of object assembly and object recognition. Experimental results are included. 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These algorithms are based on conversion of the curves into shape signature strings and application of string matching techniques to find long matching substrings, followed by direct curve matching of the corresponding candidate subcurves to find the longest matching subcurve. The first algorithm is of complexity O(n), where n is the number of sample points on the curves. The second one, while being theoretically somewhat less efficient, proved to be robust and efficient in practical applications. Both algorithms solve the problem of general curves without being dependent on some set of special points on the curves. The algorithms have industrial applications to problems of object assembly and object recognition. Experimental results are included. The algorithms can be easily extended to the 3D case.< ></description><subject>Application software</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Exact sciences and technology</subject><subject>Intelligent robots</subject><subject>Layout</subject><subject>Least squares methods</subject><subject>Object recognition</subject><subject>Pattern matching</subject><subject>Pattern recognition</subject><subject>Pattern recognition. Digital image processing. 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Computational geometry</topic><topic>Robotic assembly</topic><topic>Robustness</topic><topic>Shape</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wolfson, H.J.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on pattern analysis and machine intelligence</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wolfson, H.J.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>On curve matching</atitle><jtitle>IEEE transactions on pattern analysis and machine intelligence</jtitle><stitle>TPAMI</stitle><date>1990-05-01</date><risdate>1990</risdate><volume>12</volume><issue>5</issue><spage>483</spage><epage>489</epage><pages>483-489</pages><issn>0162-8828</issn><eissn>1939-3539</eissn><coden>ITPIDJ</coden><abstract>Two algorithms to find the longest common subcurve of two 2D curves are presented. These algorithms are based on conversion of the curves into shape signature strings and application of string matching techniques to find long matching substrings, followed by direct curve matching of the corresponding candidate subcurves to find the longest matching subcurve. The first algorithm is of complexity O(n), where n is the number of sample points on the curves. The second one, while being theoretically somewhat less efficient, proved to be robust and efficient in practical applications. Both algorithms solve the problem of general curves without being dependent on some set of special points on the curves. The algorithms have industrial applications to problems of object assembly and object recognition. Experimental results are included. The algorithms can be easily extended to the 3D case.< ></abstract><cop>Los Alamitos, CA</cop><pub>IEEE</pub><doi>10.1109/34.55108</doi><tpages>7</tpages></addata></record> |
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| identifier | ISSN: 0162-8828 |
| ispartof | IEEE transactions on pattern analysis and machine intelligence, 1990-05, Vol.12 (5), p.483-489 |
| issn | 0162-8828 1939-3539 |
| language | eng |
| recordid | cdi_ieee_primary_55108 |
| source | IEEE Electronic Library (IEL) |
| subjects | Application software Applied sciences Artificial intelligence Computer science control theory systems Exact sciences and technology Intelligent robots Layout Least squares methods Object recognition Pattern matching Pattern recognition Pattern recognition. Digital image processing. Computational geometry Robotic assembly Robustness Shape |
| title | On curve matching |
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