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Texel: a methodology and an integrated environment for developing automated systems for texture classification
The techniques used when building a texture classification system are determined, in most cases, ad hoc. For developing such a system, the most commonly used sequence of steps is: acquisition, preprocessing, feature extraction and classification. For each of them, there exist many options, and the c...
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creator | Sanchez, R.M. Espinosa, F.C. |
description | The techniques used when building a texture classification system are determined, in most cases, ad hoc. For developing such a system, the most commonly used sequence of steps is: acquisition, preprocessing, feature extraction and classification. For each of them, there exist many options, and the choice of one or more of them influences the rest. The expert has to choose among several kinds of preprocessing tasks, a number of texture analysis methods, and several classifiers. The process of getting a system that provides a good performance is a costly procedure due to the great variety of combinations of processing tasks. In this paper we introduce a methodology for the construction of texture classification systems, primarily focused in natural, random or pseudorandom textures, and a visual tool with a powerful interface that allows us to put it into practice. |
doi_str_mv | 10.1109/IJSIS.1998.685480 |
format | conference_proceeding |
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For developing such a system, the most commonly used sequence of steps is: acquisition, preprocessing, feature extraction and classification. For each of them, there exist many options, and the choice of one or more of them influences the rest. The expert has to choose among several kinds of preprocessing tasks, a number of texture analysis methods, and several classifiers. The process of getting a system that provides a good performance is a costly procedure due to the great variety of combinations of processing tasks. 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No.98EX174)</title><addtitle>IJSIS</addtitle><description>The techniques used when building a texture classification system are determined, in most cases, ad hoc. For developing such a system, the most commonly used sequence of steps is: acquisition, preprocessing, feature extraction and classification. For each of them, there exist many options, and the choice of one or more of them influences the rest. The expert has to choose among several kinds of preprocessing tasks, a number of texture analysis methods, and several classifiers. The process of getting a system that provides a good performance is a costly procedure due to the great variety of combinations of processing tasks. In this paper we introduce a methodology for the construction of texture classification systems, primarily focused in natural, random or pseudorandom textures, and a visual tool with a powerful interface that allows us to put it into practice.</description><subject>Electronic mail</subject><subject>Gabor filters</subject><subject>Histograms</subject><subject>Image analysis</subject><subject>Image edge detection</subject><subject>Image texture analysis</subject><subject>Machine vision</subject><subject>Photography</subject><subject>Quality control</subject><subject>Reactive power</subject><isbn>9780818685484</isbn><isbn>0818685484</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1998</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotkM1qAyEcxIVSaEn3AdqTL5BUV121txL6kRLoIek5uPo3texqWE1I3r5hk4FhGPgxh0HokZIZpUQ_L75Wi9WMaq1mjRJckRtUaamIomrs_A5VOf-Rs5gWTMt7FNdwhO4FG9xD-U0udWl7wia6s3GIBbaDKeAwxEMYUuwhFuzTgB0coEu7ELfY7EvqRyifcoE-j0CBY9kPgG1ncg4-WFNCig_o1psuQ3XNCfp5f1vPP6fL74_F_HU5DVTyMnWWEduYthHEt7WyDSNOcS3BKdpqAzU1VniQmtW85qCVt62QrfBcUMGoZRP0dNkNALDZDaE3w2lzOYX9A1zBWvI</recordid><startdate>1998</startdate><enddate>1998</enddate><creator>Sanchez, R.M.</creator><creator>Espinosa, F.C.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1998</creationdate><title>Texel: a methodology and an integrated environment for developing automated systems for texture classification</title><author>Sanchez, R.M. ; Espinosa, F.C.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i174t-dc30c6ab650fb28c630d8497ed81b9ae21ac5fe7932424e98fcb57b5f451531c3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1998</creationdate><topic>Electronic mail</topic><topic>Gabor filters</topic><topic>Histograms</topic><topic>Image analysis</topic><topic>Image edge detection</topic><topic>Image texture analysis</topic><topic>Machine vision</topic><topic>Photography</topic><topic>Quality control</topic><topic>Reactive power</topic><toplevel>online_resources</toplevel><creatorcontrib>Sanchez, R.M.</creatorcontrib><creatorcontrib>Espinosa, F.C.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Sanchez, R.M.</au><au>Espinosa, F.C.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Texel: a methodology and an integrated environment for developing automated systems for texture classification</atitle><btitle>Proceedings. IEEE International Joint Symposia on Intelligence and Systems (Cat. No.98EX174)</btitle><stitle>IJSIS</stitle><date>1998</date><risdate>1998</risdate><spage>382</spage><epage>388</epage><pages>382-388</pages><isbn>9780818685484</isbn><isbn>0818685484</isbn><abstract>The techniques used when building a texture classification system are determined, in most cases, ad hoc. For developing such a system, the most commonly used sequence of steps is: acquisition, preprocessing, feature extraction and classification. For each of them, there exist many options, and the choice of one or more of them influences the rest. The expert has to choose among several kinds of preprocessing tasks, a number of texture analysis methods, and several classifiers. The process of getting a system that provides a good performance is a costly procedure due to the great variety of combinations of processing tasks. In this paper we introduce a methodology for the construction of texture classification systems, primarily focused in natural, random or pseudorandom textures, and a visual tool with a powerful interface that allows us to put it into practice.</abstract><pub>IEEE</pub><doi>10.1109/IJSIS.1998.685480</doi><tpages>7</tpages></addata></record> |
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ispartof | Proceedings. IEEE International Joint Symposia on Intelligence and Systems (Cat. No.98EX174), 1998, p.382-388 |
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source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | Electronic mail Gabor filters Histograms Image analysis Image edge detection Image texture analysis Machine vision Photography Quality control Reactive power |
title | Texel: a methodology and an integrated environment for developing automated systems for texture classification |
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