Large-scale simulation studies in image pattern recognition
Many obstacles to progress in image pattern recognition result from the fact that per-class distributions are often too irregular to be well-approximated by simple analytical functions. Simulation studies offer one way to circumvent these obstacles. We present three closely related studies of machin...
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| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence 1997-10, Vol.19 (10), p.1067-1079 |
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| container_end_page | 1079 |
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| container_issue | 10 |
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| container_title | IEEE transactions on pattern analysis and machine intelligence |
| container_volume | 19 |
| creator | Tin Kam Ho Baird, H.S. |
| description | Many obstacles to progress in image pattern recognition result from the fact that per-class distributions are often too irregular to be well-approximated by simple analytical functions. Simulation studies offer one way to circumvent these obstacles. We present three closely related studies of machine-printed character recognition that rely on synthetic data generated pseudo-randomly in accordance with an explicit stochastic model of document image degradations. The unusually large scale of experiments - involving several million samples that makes this methodology possible have allowed us to compute sharp estimates of the intrinsic difficulty (Bayes risk) of concrete image recognition problems, as well as the asymptotic accuracy and domain of competency of classifiers. |
| doi_str_mv | 10.1109/34.625107 |
| format | Article |
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The unusually large scale of experiments - involving several million samples that makes this methodology possible have allowed us to compute sharp estimates of the intrinsic difficulty (Bayes risk) of concrete image recognition problems, as well as the asymptotic accuracy and domain of competency of classifiers.</description><subject>Character generation</subject><subject>Character recognition</subject><subject>Concrete</subject><subject>Degradation</subject><subject>Image analysis</subject><subject>Image recognition</subject><subject>Large-scale systems</subject><subject>Pattern analysis</subject><subject>Pattern recognition</subject><subject>Stochastic processes</subject><issn>0162-8828</issn><issn>1939-3539</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1997</creationdate><recordtype>article</recordtype><recordid>eNo90DtLxEAUBeBBFIyrha1VKsEi6513gpUs6wMCNloPk8lNGMljnUkK_71Zslid4nxcLoeQWwpbSqF45GKrmKSgz0hCC15kXPLinCRAFcvynOWX5CrGbwAqJPCEPJU2tJhFZztMo-_nzk5-HNI4zbXHmPoh9b1tMT3YacIwpAHd2A7-iK7JRWO7iDen3JCvl_3n7i0rP17fd89l5pjWU6atdpWqAUUjnajqpqoYrzhDqRWizSl1SjLlUAktoBLa2lo2BUKNDgrB-Ybcr3cPYfyZMU6m99Fh19kBxzkalnMOlOkFPqzQhTHGgI05hOX78GsomOM8hguzzrPYu9V6RPx3p_IPhvdgNQ</recordid><startdate>19971001</startdate><enddate>19971001</enddate><creator>Tin Kam Ho</creator><creator>Baird, H.S.</creator><general>IEEE</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>19971001</creationdate><title>Large-scale simulation studies in image pattern recognition</title><author>Tin Kam Ho ; Baird, H.S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c277t-7a7cb6d0e4f5c4bdfbb23b32e576eea811c6526ce64740b47aad5f9e0dec09433</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1997</creationdate><topic>Character generation</topic><topic>Character recognition</topic><topic>Concrete</topic><topic>Degradation</topic><topic>Image analysis</topic><topic>Image recognition</topic><topic>Large-scale systems</topic><topic>Pattern analysis</topic><topic>Pattern recognition</topic><topic>Stochastic processes</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tin Kam Ho</creatorcontrib><creatorcontrib>Baird, H.S.</creatorcontrib><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>Tin Kam Ho</au><au>Baird, H.S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Large-scale simulation studies in image pattern recognition</atitle><jtitle>IEEE transactions on pattern analysis and machine intelligence</jtitle><stitle>TPAMI</stitle><date>1997-10-01</date><risdate>1997</risdate><volume>19</volume><issue>10</issue><spage>1067</spage><epage>1079</epage><pages>1067-1079</pages><issn>0162-8828</issn><eissn>1939-3539</eissn><coden>ITPIDJ</coden><abstract>Many obstacles to progress in image pattern recognition result from the fact that per-class distributions are often too irregular to be well-approximated by simple analytical functions. 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| ispartof | IEEE transactions on pattern analysis and machine intelligence, 1997-10, Vol.19 (10), p.1067-1079 |
| issn | 0162-8828 1939-3539 |
| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Character generation Character recognition Concrete Degradation Image analysis Image recognition Large-scale systems Pattern analysis Pattern recognition Stochastic processes |
| title | Large-scale simulation studies in image pattern recognition |
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