New measures for comparing matrices and their application to image processing
•We present the concept of matrix resemblance functions, i.e., functions that measure the similarity between two matrices.•We give two construction methods and study the main properties of matrix resemblance functions.•We design an algorithm for image comparison based on matrix resemblance functions...
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| Veröffentlicht in: | Applied Mathematical Modelling 2018-09, Vol.61, p.498-520 |
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| container_title | Applied Mathematical Modelling |
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| creator | Sesma-Sara, Mikel De Miguel, Laura Pagola, Miguel Burusco, Ana Mesiar, Radko Bustince, Humberto |
| description | •We present the concept of matrix resemblance functions, i.e., functions that measure the similarity between two matrices.•We give two construction methods and study the main properties of matrix resemblance functions.•We design an algorithm for image comparison based on matrix resemblance functions.•We propose an application of this algorithm for defect detection in industrial manufacturing processes.•We propose an application of this algorithm for video motion detection and object tracking.
In this work we present the class of matrix resemblance functions, i.e., functions that measure the difference between two matrices. We present two construction methods and study the properties that matrix resemblance functions satisfy, which suggest that this class of functions is an appropriate tool for comparing images. Hence, we present a comparison method for grayscale images whose result is a new image, which enables to locate the areas where both images are equally similar or dissimilar. Additionally, we propose some applications in which this comparison method can be used, such as defect detection in industrial manufacturing processes and video motion detection and object tracking. |
| doi_str_mv | 10.1016/j.apm.2018.05.006 |
| format | Article |
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In this work we present the class of matrix resemblance functions, i.e., functions that measure the difference between two matrices. We present two construction methods and study the properties that matrix resemblance functions satisfy, which suggest that this class of functions is an appropriate tool for comparing images. Hence, we present a comparison method for grayscale images whose result is a new image, which enables to locate the areas where both images are equally similar or dissimilar. Additionally, we propose some applications in which this comparison method can be used, such as defect detection in industrial manufacturing processes and video motion detection and object tracking.</description><identifier>ISSN: 0307-904X</identifier><identifier>ISSN: 1088-8691</identifier><identifier>EISSN: 0307-904X</identifier><identifier>DOI: 10.1016/j.apm.2018.05.006</identifier><language>eng</language><publisher>New York: Elsevier Inc</publisher><subject>Defect detection ; Defects ; Fuzzy mathematical morphology ; Image processing ; Image processing systems ; Inclusion grades ; Manufacturing ; Matrix ; Matrix resemblance functions ; Motion detection ; Motion perception ; Restricted equivalence functions</subject><ispartof>Applied Mathematical Modelling, 2018-09, Vol.61, p.498-520</ispartof><rights>2018 Elsevier Inc.</rights><rights>Copyright Elsevier BV Sep 2018</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c368t-821cc5ceb83fdd95dc7f4c854efd51ec9c3001f2d5588c1f1e12f6aebff26d03</citedby><cites>FETCH-LOGICAL-c368t-821cc5ceb83fdd95dc7f4c854efd51ec9c3001f2d5588c1f1e12f6aebff26d03</cites><orcidid>0000-0002-1279-6195</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0307904X18302166$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids></links><search><creatorcontrib>Sesma-Sara, Mikel</creatorcontrib><creatorcontrib>De Miguel, Laura</creatorcontrib><creatorcontrib>Pagola, Miguel</creatorcontrib><creatorcontrib>Burusco, Ana</creatorcontrib><creatorcontrib>Mesiar, Radko</creatorcontrib><creatorcontrib>Bustince, Humberto</creatorcontrib><title>New measures for comparing matrices and their application to image processing</title><title>Applied Mathematical Modelling</title><description>•We present the concept of matrix resemblance functions, i.e., functions that measure the similarity between two matrices.•We give two construction methods and study the main properties of matrix resemblance functions.•We design an algorithm for image comparison based on matrix resemblance functions.•We propose an application of this algorithm for defect detection in industrial manufacturing processes.•We propose an application of this algorithm for video motion detection and object tracking.
In this work we present the class of matrix resemblance functions, i.e., functions that measure the difference between two matrices. We present two construction methods and study the properties that matrix resemblance functions satisfy, which suggest that this class of functions is an appropriate tool for comparing images. Hence, we present a comparison method for grayscale images whose result is a new image, which enables to locate the areas where both images are equally similar or dissimilar. Additionally, we propose some applications in which this comparison method can be used, such as defect detection in industrial manufacturing processes and video motion detection and object tracking.</description><subject>Defect detection</subject><subject>Defects</subject><subject>Fuzzy mathematical morphology</subject><subject>Image processing</subject><subject>Image processing systems</subject><subject>Inclusion grades</subject><subject>Manufacturing</subject><subject>Matrix</subject><subject>Matrix resemblance functions</subject><subject>Motion detection</subject><subject>Motion perception</subject><subject>Restricted equivalence functions</subject><issn>0307-904X</issn><issn>1088-8691</issn><issn>0307-904X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp9kLtOAzEQRS0EEiHwAXSWqHcZ78b7EBWKeEkBmhR0ljMeB0fZ9WJvQPw9jkJBRTUP3TN3dBm7FJALENX1JtdDlxcgmhxkDlAdsQmUUGctzN6O__Sn7CzGDQDINE3Y8wt98Y503AWK3PrA0XeDDq5f806PwWFa697w8Z1c4HoYtg716HzPR89dp9fEh-CTKibknJ1YvY108VunbHl_t5w_ZovXh6f57SLDsmrGrCkEokRaNaU1ppUGazvDRs7IGikIWywBhC2MlE2DwgoSha00rawtKgPllF0dzibnjx3FUW38LvTJURXQ1m1d1G2ZVOKgwuBjDGTVENLD4VsJUPvQ1Eal0NQ-NAVSpdASc3NgKH3_6SioiI56JOMC4aiMd__QP7uWdlQ</recordid><startdate>201809</startdate><enddate>201809</enddate><creator>Sesma-Sara, Mikel</creator><creator>De Miguel, Laura</creator><creator>Pagola, Miguel</creator><creator>Burusco, Ana</creator><creator>Mesiar, Radko</creator><creator>Bustince, Humberto</creator><general>Elsevier Inc</general><general>Elsevier BV</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><orcidid>https://orcid.org/0000-0002-1279-6195</orcidid></search><sort><creationdate>201809</creationdate><title>New measures for comparing matrices and their application to image processing</title><author>Sesma-Sara, Mikel ; De Miguel, Laura ; Pagola, Miguel ; Burusco, Ana ; Mesiar, Radko ; Bustince, Humberto</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c368t-821cc5ceb83fdd95dc7f4c854efd51ec9c3001f2d5588c1f1e12f6aebff26d03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Defect detection</topic><topic>Defects</topic><topic>Fuzzy mathematical morphology</topic><topic>Image processing</topic><topic>Image processing systems</topic><topic>Inclusion grades</topic><topic>Manufacturing</topic><topic>Matrix</topic><topic>Matrix resemblance functions</topic><topic>Motion detection</topic><topic>Motion perception</topic><topic>Restricted equivalence functions</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sesma-Sara, Mikel</creatorcontrib><creatorcontrib>De Miguel, Laura</creatorcontrib><creatorcontrib>Pagola, Miguel</creatorcontrib><creatorcontrib>Burusco, Ana</creatorcontrib><creatorcontrib>Mesiar, Radko</creatorcontrib><creatorcontrib>Bustince, Humberto</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>Applied Mathematical Modelling</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sesma-Sara, Mikel</au><au>De Miguel, Laura</au><au>Pagola, Miguel</au><au>Burusco, Ana</au><au>Mesiar, Radko</au><au>Bustince, Humberto</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>New measures for comparing matrices and their application to image processing</atitle><jtitle>Applied Mathematical Modelling</jtitle><date>2018-09</date><risdate>2018</risdate><volume>61</volume><spage>498</spage><epage>520</epage><pages>498-520</pages><issn>0307-904X</issn><issn>1088-8691</issn><eissn>0307-904X</eissn><abstract>•We present the concept of matrix resemblance functions, i.e., functions that measure the similarity between two matrices.•We give two construction methods and study the main properties of matrix resemblance functions.•We design an algorithm for image comparison based on matrix resemblance functions.•We propose an application of this algorithm for defect detection in industrial manufacturing processes.•We propose an application of this algorithm for video motion detection and object tracking.
In this work we present the class of matrix resemblance functions, i.e., functions that measure the difference between two matrices. We present two construction methods and study the properties that matrix resemblance functions satisfy, which suggest that this class of functions is an appropriate tool for comparing images. Hence, we present a comparison method for grayscale images whose result is a new image, which enables to locate the areas where both images are equally similar or dissimilar. Additionally, we propose some applications in which this comparison method can be used, such as defect detection in industrial manufacturing processes and video motion detection and object tracking.</abstract><cop>New York</cop><pub>Elsevier Inc</pub><doi>10.1016/j.apm.2018.05.006</doi><tpages>23</tpages><orcidid>https://orcid.org/0000-0002-1279-6195</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | Applied Mathematical Modelling, 2018-09, Vol.61, p.498-520 |
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| language | eng |
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| source | Education Source; Elsevier ScienceDirect Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Business Source Complete |
| subjects | Defect detection Defects Fuzzy mathematical morphology Image processing Image processing systems Inclusion grades Manufacturing Matrix Matrix resemblance functions Motion detection Motion perception Restricted equivalence functions |
| title | New measures for comparing matrices and their application to image processing |
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