Peak detection in Hough transform via self-organizing learning

In this paper, we suggest a novel concept of applying the self-organizing map (SOM) in the Hough domain for a significant reduction of the Hough space. By using the SOM as the output space of the generalized Hough transform, the conventional 4-D Hough domain is replaced by a 10/spl times/10 map, org...

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Hauptverfasser:	Choy, C.S.-T., Ser, P.-K., Siu, W.-C.
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Analog circuits Costs Councils Feature extraction Genetic expression Neural networks Neurons Pixel Testing Voting
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creator	Choy, C.S.-T. Ser, P.-K. Siu, W.-C.
description	In this paper, we suggest a novel concept of applying the self-organizing map (SOM) in the Hough domain for a significant reduction of the Hough space. By using the SOM as the output space of the generalized Hough transform, the conventional 4-D Hough domain is replaced by a 10/spl times/10 map, organized in a rectangular grid. Experimental results indicate high accuracy in voting is attainable despite its small memory requirement.
doi_str_mv	10.1109/ISCAS.1995.521470
format	Conference Proceeding
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Experimental results indicate high accuracy in voting is attainable despite its small memory requirement.</description><subject>Analog circuits</subject><subject>Costs</subject><subject>Councils</subject><subject>Feature extraction</subject><subject>Genetic expression</subject><subject>Neural networks</subject><subject>Neurons</subject><subject>Pixel</subject><subject>Testing</subject><subject>Voting</subject><isbn>0780325702</isbn><isbn>9780780325708</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1995</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotT81KAzEYDIig1j6AnvICu-Z3k1yEsqgtFBTankuy_bJGt1lJVkGfvoE6l_lhGBiE7iipKSXmYbVpF5uaGiNryahQ5ALdEKUJZ1IRdoXmOX-QAiElb8w1enwD-4kPMEE3hTHiEPFy_O7f8ZRszH5MR_wTLM4w-GpMvY3hL8QeD2BTLOIWXXo7ZJj_8wztnp-27bJav76s2sW6CpSIqRJAVKPFQYJwhmmgnDvXdNw6XzLnFafFgzHgJVfac97pBpRhTChKS32G7s-7AQD2Xykcbfrdnx_yEx7mRuY</recordid><startdate>1995</startdate><enddate>1995</enddate><creator>Choy, C.S.-T.</creator><creator>Ser, P.-K.</creator><creator>Siu, W.-C.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1995</creationdate><title>Peak detection in Hough transform via self-organizing learning</title><author>Choy, C.S.-T. ; Ser, P.-K. ; Siu, W.-C.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i104t-4e07684d5e4b928e133bb6c3abfd5ebf731bb6e99ef5378f33c86e79224711e13</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1995</creationdate><topic>Analog circuits</topic><topic>Costs</topic><topic>Councils</topic><topic>Feature extraction</topic><topic>Genetic expression</topic><topic>Neural networks</topic><topic>Neurons</topic><topic>Pixel</topic><topic>Testing</topic><topic>Voting</topic><toplevel>online_resources</toplevel><creatorcontrib>Choy, C.S.-T.</creatorcontrib><creatorcontrib>Ser, P.-K.</creatorcontrib><creatorcontrib>Siu, W.-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>Choy, C.S.-T.</au><au>Ser, P.-K.</au><au>Siu, W.-C.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Peak detection in Hough transform via self-organizing learning</atitle><btitle>1995 IEEE International Symposium on Circuits and Systems (ISCAS)</btitle><stitle>ISCAS</stitle><date>1995</date><risdate>1995</risdate><volume>1</volume><spage>139</spage><epage>142 vol.1</epage><pages>139-142 vol.1</pages><isbn>0780325702</isbn><isbn>9780780325708</isbn><abstract>In this paper, we suggest a novel concept of applying the self-organizing map (SOM) in the Hough domain for a significant reduction of the Hough space. By using the SOM as the output space of the generalized Hough transform, the conventional 4-D Hough domain is replaced by a 10/spl times/10 map, organized in a rectangular grid. Experimental results indicate high accuracy in voting is attainable despite its small memory requirement.</abstract><pub>IEEE</pub><doi>10.1109/ISCAS.1995.521470</doi></addata></record>
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subjects	Analog circuits Costs Councils Feature extraction Genetic expression Neural networks Neurons Pixel Testing Voting
title	Peak detection in Hough transform via self-organizing learning
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