Automatic marker generation for watershed segmentation of natural images
Automatic marker selection for watershed segmentation is a difficult problem. Most of the existing procedures are intended for specific application fields and usually require some prior knowledge about the problem at hand. A simple and general method for generating markers is proposed. The markers a...
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| Veröffentlicht in: | Electronics letters 2014-08, Vol.50 (18), p.1281-1283 |
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| container_end_page | 1283 |
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| container_title | Electronics letters |
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| creator | Sigut, J Fumero, F Nuñez, O Sigut, M |
| description | Automatic marker selection for watershed segmentation is a difficult problem. Most of the existing procedures are intended for specific application fields and usually require some prior knowledge about the problem at hand. A simple and general method for generating markers is proposed. The markers are obtained from the splitting of the three-dimensional (3D) colour histogram of the image and imposed as minima for watershed segmentation. The method has been tested on natural images from the Berkeley segmentation dataset (BSDS500), showing results comparable with the state-of-the-art segmentation algorithms. |
| doi_str_mv | 10.1049/el.2014.2705 |
| format | Article |
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Most of the existing procedures are intended for specific application fields and usually require some prior knowledge about the problem at hand. A simple and general method for generating markers is proposed. The markers are obtained from the splitting of the three-dimensional (3D) colour histogram of the image and imposed as minima for watershed segmentation. The method has been tested on natural images from the Berkeley segmentation dataset (BSDS500), showing results comparable with the state-of-the-art segmentation algorithms.</description><identifier>ISSN: 0013-5194</identifier><identifier>ISSN: 1350-911X</identifier><identifier>EISSN: 1350-911X</identifier><identifier>DOI: 10.1049/el.2014.2705</identifier><identifier>CODEN: ELLEAK</identifier><language>eng</language><publisher>Stevenage: The Institution of Engineering and Technology</publisher><subject>3D colour histogram ; Applied sciences ; Artificial intelligence ; automatic marker generation ; automatic marker selection ; Automation ; Berkeley segmentation dataset ; BSDS500 ; Color ; Colour ; Computer science; control theory; systems ; Exact sciences and technology ; Image and vision processing and display technology ; image colour analysis ; image segmentation ; Markers ; natural images ; Pattern recognition. Digital image processing. 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Most of the existing procedures are intended for specific application fields and usually require some prior knowledge about the problem at hand. A simple and general method for generating markers is proposed. The markers are obtained from the splitting of the three-dimensional (3D) colour histogram of the image and imposed as minima for watershed segmentation. The method has been tested on natural images from the Berkeley segmentation dataset (BSDS500), showing results comparable with the state-of-the-art segmentation algorithms.</description><subject>3D colour histogram</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>automatic marker generation</subject><subject>automatic marker selection</subject><subject>Automation</subject><subject>Berkeley segmentation dataset</subject><subject>BSDS500</subject><subject>Color</subject><subject>Colour</subject><subject>Computer science; control theory; systems</subject><subject>Exact sciences and technology</subject><subject>Image and vision processing and display technology</subject><subject>image colour analysis</subject><subject>image segmentation</subject><subject>Markers</subject><subject>natural images</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><subject>Segmentation</subject><subject>Three dimensional</subject><subject>three‐dimensional colour histogram</subject><subject>watershed segmentation</subject><subject>Watersheds</subject><issn>0013-5194</issn><issn>1350-911X</issn><issn>1350-911X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp9kE1r3DAQhkVooUuaW3-AoS30UG9Hsj6sYxo2TcHQSwK9iVl5vHHrj61kE_LvK-PQJKX0JBg98_DOy9gbDlsO0n6ibiuAy60woE7YhhcKcsv59xdsA8CLXHErX7GzGNt9wrjUIPmGXZ3P09jj1Pqsx_CTQnaggUIajEPWjCG7w4lCvKU6i3ToaZjWr7HJBpzmgF3W9nig-Jq9bLCLdPbwnrKby931xVVeffvy9eK8yr00QuS6VBJ1rYUlSWCKupElNkbvPVjwNdWajMUSTSG8qb0uLVoUdg9KFLQvRHHKPqzeYxh_zRQn17fRU9fhQOMcHU_qQpVW6IS-_Qv9Mc5hSOkSxYVSYDVP1MeV8mGMMVDjjiGdFO4dB7c066hzS7NuaTbh7x-kGD12TcDBt_HPjihLMCUsWrVyd21H9_91ul1Vic-XwI1c7nu37rX0JO-ueoIf6-axhmfYPxP_BsGPoCk</recordid><startdate>20140828</startdate><enddate>20140828</enddate><creator>Sigut, J</creator><creator>Fumero, F</creator><creator>Nuñez, O</creator><creator>Sigut, M</creator><general>The Institution of Engineering and Technology</general><general>Institution of Engineering and Technology</general><general>John Wiley & Sons, Inc</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>7SP</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>KR7</scope><scope>L7M</scope></search><sort><creationdate>20140828</creationdate><title>Automatic marker generation for watershed segmentation of natural images</title><author>Sigut, J ; Fumero, F ; Nuñez, O ; Sigut, M</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4722-6854a6d629e4e073df48af76bc090cded6e79a8a732c7dc689a9a29b0523eb323</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>3D colour histogram</topic><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>automatic marker generation</topic><topic>automatic marker selection</topic><topic>Automation</topic><topic>Berkeley segmentation dataset</topic><topic>BSDS500</topic><topic>Color</topic><topic>Colour</topic><topic>Computer science; control theory; systems</topic><topic>Exact sciences and technology</topic><topic>Image and vision processing and display technology</topic><topic>image colour analysis</topic><topic>image segmentation</topic><topic>Markers</topic><topic>natural images</topic><topic>Pattern recognition. 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| subjects | 3D colour histogram Applied sciences Artificial intelligence automatic marker generation automatic marker selection Automation Berkeley segmentation dataset BSDS500 Color Colour Computer science control theory systems Exact sciences and technology Image and vision processing and display technology image colour analysis image segmentation Markers natural images Pattern recognition. Digital image processing. Computational geometry Segmentation Three dimensional three‐dimensional colour histogram watershed segmentation Watersheds |
| title | Automatic marker generation for watershed segmentation of natural images |
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