Unsupervised image change detection means based on 2-D entropy
In this paper, a novel image change detection means based on 2-D histogram formed by pixel gray levels and the local average gray levels is proposed. First, the 2-D histogram is segmented by a line passing through the diagonal of 2-D histogram in a certain direction. Second, the improved 2-D entropy...
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| creator | Wenbang Sun Hexin Chen Haiyan Tang Yu Liu |
| description | In this paper, a novel image change detection means based on 2-D histogram formed by pixel gray levels and the local average gray levels is proposed. First, the 2-D histogram is segmented by a line passing through the diagonal of 2-D histogram in a certain direction. Second, the improved 2-D entropy is defined to acquire the optimal segmentation direction and the optimal threshold. And the 2-D histogram is segmented into unchanged region and change region. Then the change area is detected based on the change region of 2-D histogram. Finally, the change detection means is compared to traditional means Theoretical analysis and experiment result show that this algorithm is more accurate on detection precision and faster on detection speed. |
| doi_str_mv | 10.1109/ICISE.2010.5691145 |
| format | Conference Proceeding |
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Finally, the change detection means is compared to traditional means Theoretical analysis and experiment result show that this algorithm is more accurate on detection precision and faster on detection speed.</description><subject>2-D entropy</subject><subject>2-D histogram</subject><subject>Change detection</subject><subject>Entropy</subject><subject>Equations</subject><subject>Histograms</subject><subject>Image edge detection</subject><subject>Image segmentation</subject><subject>Noise</subject><subject>Pixel</subject><issn>2160-1283</issn><isbn>142447616X</isbn><isbn>9781424476169</isbn><isbn>1424476186</isbn><isbn>9781424476176</isbn><isbn>1424476178</isbn><isbn>9781424476183</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFj81Kw0AUhUdUsK19Ad3MC6TeO3-Z2QgSqwYKXVjBXcnM3GjEpiEThb69EQuuDh8cDudj7AphgQjupizK5-VCwMjaOESlT9gUlVAqN2jN6T-Y1zM2EWggQ2HlBZun9AEAEnMLKp-w25c2fXXUfzeJIm921Rvx8F61Y0QaKAzNvuU7qtrEffVbGVFk95zaod93h0t2XlefiebHnLHNw3JTPGWr9WNZ3K2yxsGQGQNeOwxOyRhixOCFjM4pqAMJlBIkeUsarNEWNHgrwefkRpVaRFRKztj132xDRNuuH3_2h-1RXf4ALLNJtg</recordid><startdate>201012</startdate><enddate>201012</enddate><creator>Wenbang Sun</creator><creator>Hexin Chen</creator><creator>Haiyan Tang</creator><creator>Yu Liu</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201012</creationdate><title>Unsupervised image change detection means based on 2-D entropy</title><author>Wenbang Sun ; Hexin Chen ; Haiyan Tang ; Yu Liu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-660b591c943dcdd1cb23d9940fce213303eb8e508658050b830b7e9618f2d1443</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>2-D entropy</topic><topic>2-D histogram</topic><topic>Change detection</topic><topic>Entropy</topic><topic>Equations</topic><topic>Histograms</topic><topic>Image edge detection</topic><topic>Image segmentation</topic><topic>Noise</topic><topic>Pixel</topic><toplevel>online_resources</toplevel><creatorcontrib>Wenbang Sun</creatorcontrib><creatorcontrib>Hexin Chen</creatorcontrib><creatorcontrib>Haiyan Tang</creatorcontrib><creatorcontrib>Yu Liu</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>Wenbang Sun</au><au>Hexin Chen</au><au>Haiyan Tang</au><au>Yu Liu</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Unsupervised image change detection means based on 2-D entropy</atitle><btitle>The 2nd International Conference on Information Science and Engineering</btitle><stitle>ICISE</stitle><date>2010-12</date><risdate>2010</risdate><spage>4199</spage><epage>4202</epage><pages>4199-4202</pages><issn>2160-1283</issn><isbn>142447616X</isbn><isbn>9781424476169</isbn><eisbn>1424476186</eisbn><eisbn>9781424476176</eisbn><eisbn>1424476178</eisbn><eisbn>9781424476183</eisbn><abstract>In this paper, a novel image change detection means based on 2-D histogram formed by pixel gray levels and the local average gray levels is proposed. First, the 2-D histogram is segmented by a line passing through the diagonal of 2-D histogram in a certain direction. Second, the improved 2-D entropy is defined to acquire the optimal segmentation direction and the optimal threshold. And the 2-D histogram is segmented into unchanged region and change region. Then the change area is detected based on the change region of 2-D histogram. Finally, the change detection means is compared to traditional means Theoretical analysis and experiment result show that this algorithm is more accurate on detection precision and faster on detection speed.</abstract><pub>IEEE</pub><doi>10.1109/ICISE.2010.5691145</doi><tpages>4</tpages></addata></record> |
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| issn | 2160-1283 |
| language | eng |
| recordid | cdi_ieee_primary_5691145 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | 2-D entropy 2-D histogram Change detection Entropy Equations Histograms Image edge detection Image segmentation Noise Pixel |
| title | Unsupervised image change detection means based on 2-D entropy |
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