Detection and separation of generic-shaped objects by fuzzy clustering
Purpose - Existing shape-based fuzzy clustering algorithms are all designed to explicitly segment regular geometrically shaped objects in an image, with the consequence that this restricts their capability to separate arbitrarily shaped objects. The purpose of this paper is to introduce a new detect...
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| Veröffentlicht in: | International journal of intelligent computing and cybernetics 2010-08, Vol.3 (3), p.365-390 |
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| container_title | International journal of intelligent computing and cybernetics |
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| creator | Ameer Ali, M. Karmakar, Gour C. Dooley, Laurence S. |
| description | Purpose - Existing shape-based fuzzy clustering algorithms are all designed to explicitly segment regular geometrically shaped objects in an image, with the consequence that this restricts their capability to separate arbitrarily shaped objects. The purpose of this paper is to introduce a new detection and separation of generic-shaped object algorithm.Design methodology approach - With the aim of separating arbitrary-shaped objects in an image, this paper presents a new detection and separation of generic-shaped objects (FKG) algorithm that analytically integrates arbitrary shape information into a fuzzy clustering framework, by introducing a shape constraint that preserves the original object shape during iterative scaling.Findings - Both qualitative and numerical empirical results analysis corroborate the improved object segmentation performance achieved by the FKG strategy upon different image types and disparately shaped objects.Originality value - The proposed FKG algorithm can be highly used in applications where object segmentation is necessary. Likewise, this algorithm can be applied in Moving Picture Experts Group-4 for real object segmentation that is already applied in synthetic object segmentation. |
| doi_str_mv | 10.1108/17563781011066684 |
| format | Article |
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The purpose of this paper is to introduce a new detection and separation of generic-shaped object algorithm.Design methodology approach - With the aim of separating arbitrary-shaped objects in an image, this paper presents a new detection and separation of generic-shaped objects (FKG) algorithm that analytically integrates arbitrary shape information into a fuzzy clustering framework, by introducing a shape constraint that preserves the original object shape during iterative scaling.Findings - Both qualitative and numerical empirical results analysis corroborate the improved object segmentation performance achieved by the FKG strategy upon different image types and disparately shaped objects.Originality value - The proposed FKG algorithm can be highly used in applications where object segmentation is necessary. 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Likewise, this algorithm can be applied in Moving Picture Experts Group-4 for real object segmentation that is already applied in synthetic object segmentation.</description><subject>Algorithms</subject><subject>Cluster analysis</subject><subject>Clustering</subject><subject>Control theory</subject><subject>Fuzzy</subject><subject>Fuzzy logic</subject><subject>Fuzzy set theory</subject><subject>Image processing systems</subject><subject>Optimization techniques</subject><subject>Preserves</subject><subject>Segmentation</subject><subject>Separation</subject><subject>Studies</subject><issn>1756-378X</issn><issn>1756-3798</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kEtLAzEUhYMoWB8_wN3gyoWjySSTx1KqVaHgRsFdyONObZnOjMnMov31plYUKl3dB9-553IQuiD4hhAsb4koORWS4DRxziU7QKPNLqdCycPfXr4fo5MYFxhzWUo6QpN76MH187bJTOOzCJ0J5ntsq2wGDYS5y-OH6cBnrV0kNGZ2lVXDer3KXD3EPhHN7AwdVaaOcP5TT9Hb5OF1_JRPXx6fx3fT3CXvPgcCtvBlybGw3DPOGFTCeI-dl8pyZUtKlGUFOOtoVVCryoKUhSoJVL4QlJ6iq-3dLrSfA8ReL-fRQV2bBtohasIFoZsIWEIvd9BFO4QmfacFE4oThlWCyBZyoY0xQKW7MF-asNIE602w-l-wSXO91cASgqn9n2QX1Z2vEo734HsdvgBxXIYk</recordid><startdate>20100824</startdate><enddate>20100824</enddate><creator>Ameer Ali, M.</creator><creator>Karmakar, Gour C.</creator><creator>Dooley, Laurence S.</creator><general>Emerald Group Publishing Limited</general><scope>AAYXX</scope><scope>CITATION</scope><scope>0U~</scope><scope>1-H</scope><scope>7SC</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K6~</scope><scope>K7-</scope><scope>L.-</scope><scope>L.0</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0C</scope><scope>M0N</scope><scope>M2P</scope><scope>P5Z</scope><scope>P62</scope><scope>PQBIZ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PYYUZ</scope><scope>Q9U</scope></search><sort><creationdate>20100824</creationdate><title>Detection and separation of generic-shaped objects by fuzzy clustering</title><author>Ameer Ali, M. ; Karmakar, Gour C. ; Dooley, Laurence S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c378t-e1eb2d55607b6d4644ef7add0cd89b69b5319b42ecbc3f23b952152951efd2733</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Algorithms</topic><topic>Cluster analysis</topic><topic>Clustering</topic><topic>Control theory</topic><topic>Fuzzy</topic><topic>Fuzzy logic</topic><topic>Fuzzy set theory</topic><topic>Image processing systems</topic><topic>Optimization techniques</topic><topic>Preserves</topic><topic>Segmentation</topic><topic>Separation</topic><topic>Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ameer Ali, M.</creatorcontrib><creatorcontrib>Karmakar, Gour C.</creatorcontrib><creatorcontrib>Dooley, Laurence S.</creatorcontrib><collection>CrossRef</collection><collection>Global News & ABI/Inform Professional</collection><collection>Trade PRO</collection><collection>Computer and Information Systems Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Business Collection</collection><collection>Computer Science Database</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Professional Standard</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ABI/INFORM Global</collection><collection>Computing Database</collection><collection>Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Business</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ABI/INFORM Collection China</collection><collection>ProQuest Central Basic</collection><jtitle>International journal of intelligent computing and cybernetics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ameer Ali, M.</au><au>Karmakar, Gour C.</au><au>Dooley, Laurence S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Detection and separation of generic-shaped objects by fuzzy clustering</atitle><jtitle>International journal of intelligent computing and cybernetics</jtitle><date>2010-08-24</date><risdate>2010</risdate><volume>3</volume><issue>3</issue><spage>365</spage><epage>390</epage><pages>365-390</pages><issn>1756-378X</issn><eissn>1756-3798</eissn><abstract>Purpose - Existing shape-based fuzzy clustering algorithms are all designed to explicitly segment regular geometrically shaped objects in an image, with the consequence that this restricts their capability to separate arbitrarily shaped objects. The purpose of this paper is to introduce a new detection and separation of generic-shaped object algorithm.Design methodology approach - With the aim of separating arbitrary-shaped objects in an image, this paper presents a new detection and separation of generic-shaped objects (FKG) algorithm that analytically integrates arbitrary shape information into a fuzzy clustering framework, by introducing a shape constraint that preserves the original object shape during iterative scaling.Findings - Both qualitative and numerical empirical results analysis corroborate the improved object segmentation performance achieved by the FKG strategy upon different image types and disparately shaped objects.Originality value - The proposed FKG algorithm can be highly used in applications where object segmentation is necessary. 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| ispartof | International journal of intelligent computing and cybernetics, 2010-08, Vol.3 (3), p.365-390 |
| issn | 1756-378X 1756-3798 |
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| source | Emerald Journals; Standard: Emerald eJournal Premier Collection |
| subjects | Algorithms Cluster analysis Clustering Control theory Fuzzy Fuzzy logic Fuzzy set theory Image processing systems Optimization techniques Preserves Segmentation Separation Studies |
| title | Detection and separation of generic-shaped objects by fuzzy clustering |
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