Performance comparison of ten variations on the iterpretation-tree matching algorithm:
Abstract: "The best known algorithm for symbolic model matching in computer vision is the Interpretation Tree search algorithm. This algorithm has a high computational complexity when applied to matching problems with large numbers of features. This paper examines ten variations of this algorit...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Edinburgh
1994
|
| Schriftenreihe: | University <Edinburgh> / Department of Artificial Intelligence: DAI research paper
684 |
| Schlagworte: | |
| Zusammenfassung: | Abstract: "The best known algorithm for symbolic model matching in computer vision is the Interpretation Tree search algorithm. This algorithm has a high computational complexity when applied to matching problems with large numbers of features. This paper examines ten variations of this algorithm in a search for improved performance, and concludes that the non-wildcard and hierarchical algorithms have reduced theoretical complexity and run faster than the standard algorithm." |
| Beschreibung: | [6] S. |
Internformat
MARC
| LEADER | 00000nam a2200000 cb4500 | ||
|---|---|---|---|
| 001 | BV010467305 | ||
| 003 | DE-604 | ||
| 005 | 00000000000000.0 | ||
| 007 | t | ||
| 008 | 951109s1994 |||| 00||| engod | ||
| 035 | |a (OCoLC)32307088 | ||
| 035 | |a (DE-599)BVBBV010467305 | ||
| 040 | |a DE-604 |b ger |e rakddb | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-91G | ||
| 100 | 1 | |a Fisher, Robert B. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Performance comparison of ten variations on the iterpretation-tree matching algorithm |
| 264 | 1 | |a Edinburgh |c 1994 | |
| 300 | |a [6] S. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a University <Edinburgh> / Department of Artificial Intelligence: DAI research paper |v 684 | |
| 520 | 3 | |a Abstract: "The best known algorithm for symbolic model matching in computer vision is the Interpretation Tree search algorithm. This algorithm has a high computational complexity when applied to matching problems with large numbers of features. This paper examines ten variations of this algorithm in a search for improved performance, and concludes that the non-wildcard and hierarchical algorithms have reduced theoretical complexity and run faster than the standard algorithm." | |
| 650 | 7 | |a Pattern recognition, image processing and remote sensing |2 sigle | |
| 650 | 4 | |a Computer vision | |
| 810 | 2 | |a Department of Artificial Intelligence: DAI research paper |t University <Edinburgh> |v 684 |w (DE-604)BV010450646 |9 684 | |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-006975116 | ||
Datensatz im Suchindex
| _version_ | 1804124900476059648 |
|---|---|
| any_adam_object | |
| author | Fisher, Robert B. |
| author_facet | Fisher, Robert B. |
| author_role | aut |
| author_sort | Fisher, Robert B. |
| author_variant | r b f rb rbf |
| building | Verbundindex |
| bvnumber | BV010467305 |
| ctrlnum | (OCoLC)32307088 (DE-599)BVBBV010467305 |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01508nam a2200301 cb4500</leader><controlfield tag="001">BV010467305</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">00000000000000.0</controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">951109s1994 |||| 00||| engod</controlfield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)32307088</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV010467305</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rakddb</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-91G</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Fisher, Robert B.</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Performance comparison of ten variations on the iterpretation-tree matching algorithm</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Edinburgh</subfield><subfield code="c">1994</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">[6] S.</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="1" ind2=" "><subfield code="a">University <Edinburgh> / Department of Artificial Intelligence: DAI research paper</subfield><subfield code="v">684</subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="a">Abstract: "The best known algorithm for symbolic model matching in computer vision is the Interpretation Tree search algorithm. This algorithm has a high computational complexity when applied to matching problems with large numbers of features. This paper examines ten variations of this algorithm in a search for improved performance, and concludes that the non-wildcard and hierarchical algorithms have reduced theoretical complexity and run faster than the standard algorithm."</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Pattern recognition, image processing and remote sensing</subfield><subfield code="2">sigle</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer vision</subfield></datafield><datafield tag="810" ind1="2" ind2=" "><subfield code="a">Department of Artificial Intelligence: DAI research paper</subfield><subfield code="t">University <Edinburgh></subfield><subfield code="v">684</subfield><subfield code="w">(DE-604)BV010450646</subfield><subfield code="9">684</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-006975116</subfield></datafield></record></collection> |
| id | DE-604.BV010467305 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-09T17:53:01Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-006975116 |
| oclc_num | 32307088 |
| open_access_boolean | |
| owner | DE-91G DE-BY-TUM |
| owner_facet | DE-91G DE-BY-TUM |
| physical | [6] S. |
| publishDate | 1994 |
| publishDateSearch | 1994 |
| publishDateSort | 1994 |
| record_format | marc |
| series2 | University <Edinburgh> / Department of Artificial Intelligence: DAI research paper |
| spelling | Fisher, Robert B. Verfasser aut Performance comparison of ten variations on the iterpretation-tree matching algorithm Edinburgh 1994 [6] S. txt rdacontent n rdamedia nc rdacarrier University <Edinburgh> / Department of Artificial Intelligence: DAI research paper 684 Abstract: "The best known algorithm for symbolic model matching in computer vision is the Interpretation Tree search algorithm. This algorithm has a high computational complexity when applied to matching problems with large numbers of features. This paper examines ten variations of this algorithm in a search for improved performance, and concludes that the non-wildcard and hierarchical algorithms have reduced theoretical complexity and run faster than the standard algorithm." Pattern recognition, image processing and remote sensing sigle Computer vision Department of Artificial Intelligence: DAI research paper University <Edinburgh> 684 (DE-604)BV010450646 684 |
| spellingShingle | Fisher, Robert B. Performance comparison of ten variations on the iterpretation-tree matching algorithm Pattern recognition, image processing and remote sensing sigle Computer vision |
| title | Performance comparison of ten variations on the iterpretation-tree matching algorithm |
| title_auth | Performance comparison of ten variations on the iterpretation-tree matching algorithm |
| title_exact_search | Performance comparison of ten variations on the iterpretation-tree matching algorithm |
| title_full | Performance comparison of ten variations on the iterpretation-tree matching algorithm |
| title_fullStr | Performance comparison of ten variations on the iterpretation-tree matching algorithm |
| title_full_unstemmed | Performance comparison of ten variations on the iterpretation-tree matching algorithm |
| title_short | Performance comparison of ten variations on the iterpretation-tree matching algorithm |
| title_sort | performance comparison of ten variations on the iterpretation tree matching algorithm |
| topic | Pattern recognition, image processing and remote sensing sigle Computer vision |
| topic_facet | Pattern recognition, image processing and remote sensing Computer vision |
| volume_link | (DE-604)BV010450646 |
| work_keys_str_mv | AT fisherrobertb performancecomparisonoftenvariationsontheiterpretationtreematchingalgorithm |