Automatic Histogram Threshold Using Fuzzy Measures
In this paper, an automatic histogram threshold approach based on a fuzziness measure is presented. This work is an improvement of an existing method. Using fuzzy logic concepts, the problems involved in finding the minimum of a criterion function are avoided. Similarity between gray levels is the k...
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| Veröffentlicht in: | IEEE transactions on image processing 2010-01, Vol.19 (1), p.199-204 |
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| container_title | IEEE transactions on image processing |
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| creator | Lopes, N.V. Mogadouro do Couto, P.A. Bustince, H. Melo-Pinto, P. |
| description | In this paper, an automatic histogram threshold approach based on a fuzziness measure is presented. This work is an improvement of an existing method. Using fuzzy logic concepts, the problems involved in finding the minimum of a criterion function are avoided. Similarity between gray levels is the key to find an optimal threshold. Two initial regions of gray levels, located at the boundaries of the histogram, are defined. Then, using an index of fuzziness, a similarity process is started to find the threshold point. A significant contrast between objects and background is assumed. Previous histogram equalization is used in small contrast images. No prior knowledge of the image is required. |
| doi_str_mv | 10.1109/TIP.2009.2032349 |
| format | Article |
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This work is an improvement of an existing method. Using fuzzy logic concepts, the problems involved in finding the minimum of a criterion function are avoided. Similarity between gray levels is the key to find an optimal threshold. Two initial regions of gray levels, located at the boundaries of the histogram, are defined. Then, using an index of fuzziness, a similarity process is started to find the threshold point. A significant contrast between objects and background is assumed. Previous histogram equalization is used in small contrast images. 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This work is an improvement of an existing method. Using fuzzy logic concepts, the problems involved in finding the minimum of a criterion function are avoided. Similarity between gray levels is the key to find an optimal threshold. Two initial regions of gray levels, located at the boundaries of the histogram, are defined. Then, using an index of fuzziness, a similarity process is started to find the threshold point. A significant contrast between objects and background is assumed. Previous histogram equalization is used in small contrast images. No prior knowledge of the image is required.</description><subject>Application software</subject><subject>Applied sciences</subject><subject>Automatic histogram</subject><subject>Detection, estimation, filtering, equalization, prediction</subject><subject>Exact sciences and technology</subject><subject>Failure analysis</subject><subject>Fuzzy logic</subject><subject>fuzzy measures</subject><subject>Fuzzy set theory</subject><subject>Fuzzy sets</subject><subject>Histograms</subject><subject>Humans</subject><subject>Image processing</subject><subject>Image segmentation</subject><subject>index of fuzziness</subject><subject>Information, signal and communications theory</subject><subject>Pixel</subject><subject>Signal and communications theory</subject><subject>Signal processing</subject><subject>Signal, noise</subject><subject>Telecommunications and information theory</subject><subject>threshold</subject><issn>1057-7149</issn><issn>1941-0042</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpFkE1PAjEQhhujEUTvJiZmL8bTYqft9uNIiAgJRg9w3pTSwppdFtvdA_x6S9jgZWaS95nJ5EHoEfAQAKu3xex7SDBWsVBCmbpCfVAMUowZuY4zzkQqgKkeugvhB2NgGfBb1AMlMik57iMyapu60k1hkmkRmnrjdZUstt6GbV2uk2Uodptk0h6Ph-TT6tDG4B7dOF0G-9D1AVpO3hfjaTr_-piNR_PUUJE1qWOKra0WQIFzSjO2Ypw5yq0EDUo6EAQbsEZaJozATinDCTBOxMqtVUboAL2e7-59_dva0ORVEYwtS72zdRtyQRmoDAOOJD6TxtcheOvyvS8q7Q854PwkKo-i8pOovBMVV5674-2qsuv_hc5MBF46QAejS-f1zhThwhFCFZdcRu7pzBXW2ksc35eZAvoHDjh3Og</recordid><startdate>201001</startdate><enddate>201001</enddate><creator>Lopes, N.V.</creator><creator>Mogadouro do Couto, P.A.</creator><creator>Bustince, H.</creator><creator>Melo-Pinto, P.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>201001</creationdate><title>Automatic Histogram Threshold Using Fuzzy Measures</title><author>Lopes, N.V. ; Mogadouro do Couto, P.A. ; Bustince, H. ; Melo-Pinto, P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-f494dea7131663354b464f36e81a198f1720c1ec8e47c70f99c6214627bfd9523</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Application software</topic><topic>Applied sciences</topic><topic>Automatic histogram</topic><topic>Detection, estimation, filtering, equalization, prediction</topic><topic>Exact sciences and technology</topic><topic>Failure analysis</topic><topic>Fuzzy logic</topic><topic>fuzzy measures</topic><topic>Fuzzy set theory</topic><topic>Fuzzy sets</topic><topic>Histograms</topic><topic>Humans</topic><topic>Image processing</topic><topic>Image segmentation</topic><topic>index of fuzziness</topic><topic>Information, signal and communications theory</topic><topic>Pixel</topic><topic>Signal and communications theory</topic><topic>Signal processing</topic><topic>Signal, noise</topic><topic>Telecommunications and information theory</topic><topic>threshold</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lopes, N.V.</creatorcontrib><creatorcontrib>Mogadouro do Couto, P.A.</creatorcontrib><creatorcontrib>Bustince, H.</creatorcontrib><creatorcontrib>Melo-Pinto, P.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005–Present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE transactions on image processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Lopes, N.V.</au><au>Mogadouro do Couto, P.A.</au><au>Bustince, H.</au><au>Melo-Pinto, P.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Automatic Histogram Threshold Using Fuzzy Measures</atitle><jtitle>IEEE transactions on image processing</jtitle><stitle>TIP</stitle><addtitle>IEEE Trans Image Process</addtitle><date>2010-01</date><risdate>2010</risdate><volume>19</volume><issue>1</issue><spage>199</spage><epage>204</epage><pages>199-204</pages><issn>1057-7149</issn><eissn>1941-0042</eissn><coden>IIPRE4</coden><abstract>In this paper, an automatic histogram threshold approach based on a fuzziness measure is presented. This work is an improvement of an existing method. Using fuzzy logic concepts, the problems involved in finding the minimum of a criterion function are avoided. Similarity between gray levels is the key to find an optimal threshold. Two initial regions of gray levels, located at the boundaries of the histogram, are defined. Then, using an index of fuzziness, a similarity process is started to find the threshold point. A significant contrast between objects and background is assumed. Previous histogram equalization is used in small contrast images. No prior knowledge of the image is required.</abstract><cop>New York, NY</cop><pub>IEEE</pub><pmid>19758860</pmid><doi>10.1109/TIP.2009.2032349</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record> |
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| ispartof | IEEE transactions on image processing, 2010-01, Vol.19 (1), p.199-204 |
| issn | 1057-7149 1941-0042 |
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| subjects | Application software Applied sciences Automatic histogram Detection, estimation, filtering, equalization, prediction Exact sciences and technology Failure analysis Fuzzy logic fuzzy measures Fuzzy set theory Fuzzy sets Histograms Humans Image processing Image segmentation index of fuzziness Information, signal and communications theory Pixel Signal and communications theory Signal processing Signal, noise Telecommunications and information theory threshold |
| title | Automatic Histogram Threshold Using Fuzzy Measures |
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