Finding line segments by stick growing
A method is described for extracting lineal features from an image using extended local information to provide robustness and sensitivity. The method utilizes both gradient magnitude and direction information, and incorporates explicit lineal and end-stop terms. These terms are combined nonlinearly...
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| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence 1994-05, Vol.16 (5), p.519-523 |
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| container_end_page | 523 |
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| container_issue | 5 |
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| container_title | IEEE transactions on pattern analysis and machine intelligence |
| container_volume | 16 |
| creator | Nelson, R.C. |
| description | A method is described for extracting lineal features from an image using extended local information to provide robustness and sensitivity. The method utilizes both gradient magnitude and direction information, and incorporates explicit lineal and end-stop terms. These terms are combined nonlinearly to produce an energy landscape in which local minima correspond to lineal features called sticks that can be represented as line segments. A hill climbing (stick-growing) process is used to find these minima. The method is compared to two others, and found to have improved gap-crossing characteristics.< > |
| doi_str_mv | 10.1109/34.291445 |
| format | Article |
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The method utilizes both gradient magnitude and direction information, and incorporates explicit lineal and end-stop terms. These terms are combined nonlinearly to produce an energy landscape in which local minima correspond to lineal features called sticks that can be represented as line segments. A hill climbing (stick-growing) process is used to find these minima. The method is compared to two others, and found to have improved gap-crossing characteristics.< ></description><identifier>ISSN: 0162-8828</identifier><identifier>EISSN: 1939-3539</identifier><identifier>DOI: 10.1109/34.291445</identifier><identifier>CODEN: ITPIDJ</identifier><language>eng</language><publisher>Los Alamitos, CA: IEEE</publisher><subject>Applied sciences ; Artificial intelligence ; Computer science; control theory; systems ; Data mining ; Exact sciences and technology ; Feature extraction ; Geometry ; Image edge detection ; Image recognition ; Pattern recognition ; Pattern recognition. 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The method utilizes both gradient magnitude and direction information, and incorporates explicit lineal and end-stop terms. These terms are combined nonlinearly to produce an energy landscape in which local minima correspond to lineal features called sticks that can be represented as line segments. A hill climbing (stick-growing) process is used to find these minima. The method is compared to two others, and found to have improved gap-crossing characteristics.< ></description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Data mining</subject><subject>Exact sciences and technology</subject><subject>Feature extraction</subject><subject>Geometry</subject><subject>Image edge detection</subject><subject>Image recognition</subject><subject>Pattern recognition</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><subject>Robot sensing systems</subject><subject>Robotics and automation</subject><subject>Robustness</subject><subject>Shape</subject><issn>0162-8828</issn><issn>1939-3539</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1994</creationdate><recordtype>article</recordtype><recordid>eNqF0D1PwzAQBmALgUQpDKxMGRASQ8rZZyf2iCq-pEosMEe2c6kMaVLsVKj_nkCrrkw3vM-9Oh1jlxxmnIO5QzkThkupjtiEGzQ5KjTHbAK8ELnWQp-ys5Q-ALhUgBN28xi6OnTLrA0dZYmWK-qGlLltlobgP7Nl7L_H-JydNLZNdLGfU_b--PA2f84Xr08v8_tF7hHLIdcGbM0L1LV3JcjCa1F41yAqglLWUnHuoKESAVwhbN2QsFwLcuBgdByn40F_vevYf20oDdUqJE9tazvqN6kSWhiNAv6HhQSFUo3wdgd97FOK1FTrGFY2bisO1e_LKpTV7mWjvd6X2uRt20Tb-ZAOC5KX0kgc2dWOBSI6pPuOH2VfcRk</recordid><startdate>19940501</startdate><enddate>19940501</enddate><creator>Nelson, R.C.</creator><general>IEEE</general><general>IEEE Computer Society</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>19940501</creationdate><title>Finding line segments by stick growing</title><author>Nelson, R.C.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c337t-890ad1638dcb7046c826cbf335e074d4511b0fe7300b62adfe2a182eb0b0cbf13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1994</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Data mining</topic><topic>Exact sciences and technology</topic><topic>Feature extraction</topic><topic>Geometry</topic><topic>Image edge detection</topic><topic>Image recognition</topic><topic>Pattern recognition</topic><topic>Pattern recognition. Digital image processing. Computational geometry</topic><topic>Robot sensing systems</topic><topic>Robotics and automation</topic><topic>Robustness</topic><topic>Shape</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Nelson, R.C.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications 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>IEEE transactions on pattern analysis and machine intelligence</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Nelson, R.C.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Finding line segments by stick growing</atitle><jtitle>IEEE transactions on pattern analysis and machine intelligence</jtitle><stitle>TPAMI</stitle><date>1994-05-01</date><risdate>1994</risdate><volume>16</volume><issue>5</issue><spage>519</spage><epage>523</epage><pages>519-523</pages><issn>0162-8828</issn><eissn>1939-3539</eissn><coden>ITPIDJ</coden><abstract>A method is described for extracting lineal features from an image using extended local information to provide robustness and sensitivity. The method utilizes both gradient magnitude and direction information, and incorporates explicit lineal and end-stop terms. These terms are combined nonlinearly to produce an energy landscape in which local minima correspond to lineal features called sticks that can be represented as line segments. A hill climbing (stick-growing) process is used to find these minima. The method is compared to two others, and found to have improved gap-crossing characteristics.< ></abstract><cop>Los Alamitos, CA</cop><pub>IEEE</pub><doi>10.1109/34.291445</doi><tpages>5</tpages></addata></record> |
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| identifier | ISSN: 0162-8828 |
| ispartof | IEEE transactions on pattern analysis and machine intelligence, 1994-05, Vol.16 (5), p.519-523 |
| issn | 0162-8828 1939-3539 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_26405345 |
| source | IEEE Electronic Library (IEL) |
| subjects | Applied sciences Artificial intelligence Computer science control theory systems Data mining Exact sciences and technology Feature extraction Geometry Image edge detection Image recognition Pattern recognition Pattern recognition. Digital image processing. Computational geometry Robot sensing systems Robotics and automation Robustness Shape |
| title | Finding line segments by stick growing |
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