The formation of features for recognizing complex images based on stochastic geometry
The theory of obtaining the features of pattern recognition based on stochastic geometry and having a three-functional structure (triplet features) is presented. The effectiveness of the proposed method of forming features for recognizing complex-structured and semantically saturated images is prove...
Gespeichert in:
| Veröffentlicht in: | Measurement techniques 2008-02, Vol.51 (2), p.199-207 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 207 |
|---|---|
| container_issue | 2 |
| container_start_page | 199 |
| container_title | Measurement techniques |
| container_volume | 51 |
| creator | Fedotov, N. G. Kol’chugin, A. S. Smol’kin, O. A. Moiseev, A. V. Romanov, S. V. |
| description | The theory of obtaining the features of pattern recognition based on stochastic geometry and having a three-functional structure (triplet features) is presented. The effectiveness of the proposed method of forming features for recognizing complex-structured and semantically saturated images is proved. A practical example of the formation of features for images from the area of medical diagnostics is considered. |
| doi_str_mv | 10.1007/s11018-008-9002-8 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_36432169</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>36432169</sourcerecordid><originalsourceid>FETCH-LOGICAL-c228t-a794f511cd290d25f5ab673890005604b61aba13b94443bfc9836e73e793d5d23</originalsourceid><addsrcrecordid>eNp1kE9LxDAQxYMouK5-AG_Bg7fqJGmT5iiL_0DwsnsOaZp0u7TNmrTg-ulNWUEQPA3M_N7jzUPomsAdARD3kRAgZQZQZhKAZuUJWpBCsKyUwE_RAoqcZUQKeo4uYtwBABNcLtBmvbXY-dDrsfUD9g47q8cp2DhvcbDGN0P71Q4NNr7fd_YTt71u0rnS0dY4aeLozVbHsTW4sb63YzhcojOnu2ivfuYSbZ4e16uX7O39-XX18JYZSssx00LmriDE1FRCTQtX6IoLliIDFBzyihNdacIqmec5q5yRJeNWMCskq4uasiW6Pfrug_-YbBxV30Zju04P1k9RMZ4zSrhM4M0fcOenMKRsioJMdoyXCSJHyAQfY7BO7UN6NhwUATW3rI4tq9SymltWs4YeNTGxQ2PDr_H_om8P938R</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>209793368</pqid></control><display><type>article</type><title>The formation of features for recognizing complex images based on stochastic geometry</title><source>SpringerLink Journals - AutoHoldings</source><creator>Fedotov, N. G. ; Kol’chugin, A. S. ; Smol’kin, O. A. ; Moiseev, A. V. ; Romanov, S. V.</creator><creatorcontrib>Fedotov, N. G. ; Kol’chugin, A. S. ; Smol’kin, O. A. ; Moiseev, A. V. ; Romanov, S. V.</creatorcontrib><description>The theory of obtaining the features of pattern recognition based on stochastic geometry and having a three-functional structure (triplet features) is presented. The effectiveness of the proposed method of forming features for recognizing complex-structured and semantically saturated images is proved. A practical example of the formation of features for images from the area of medical diagnostics is considered.</description><identifier>ISSN: 0543-1972</identifier><identifier>EISSN: 1573-8906</identifier><identifier>DOI: 10.1007/s11018-008-9002-8</identifier><language>eng</language><publisher>Boston: Springer US</publisher><subject>Analytical Chemistry ; Characterization and Evaluation of Materials ; Diagnostics ; Geometry ; Investigations ; Measurement Science and Instrumentation ; Measurement techniques ; Medical and Biological Measurements ; Pattern recognition ; Physical Chemistry ; Physics ; Physics and Astronomy ; Stochastic models ; Studies</subject><ispartof>Measurement techniques, 2008-02, Vol.51 (2), p.199-207</ispartof><rights>Springer Science+Business Media, Inc. 2008</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c228t-a794f511cd290d25f5ab673890005604b61aba13b94443bfc9836e73e793d5d23</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11018-008-9002-8$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11018-008-9002-8$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Fedotov, N. G.</creatorcontrib><creatorcontrib>Kol’chugin, A. S.</creatorcontrib><creatorcontrib>Smol’kin, O. A.</creatorcontrib><creatorcontrib>Moiseev, A. V.</creatorcontrib><creatorcontrib>Romanov, S. V.</creatorcontrib><title>The formation of features for recognizing complex images based on stochastic geometry</title><title>Measurement techniques</title><addtitle>Meas Tech</addtitle><description>The theory of obtaining the features of pattern recognition based on stochastic geometry and having a three-functional structure (triplet features) is presented. The effectiveness of the proposed method of forming features for recognizing complex-structured and semantically saturated images is proved. A practical example of the formation of features for images from the area of medical diagnostics is considered.</description><subject>Analytical Chemistry</subject><subject>Characterization and Evaluation of Materials</subject><subject>Diagnostics</subject><subject>Geometry</subject><subject>Investigations</subject><subject>Measurement Science and Instrumentation</subject><subject>Measurement techniques</subject><subject>Medical and Biological Measurements</subject><subject>Pattern recognition</subject><subject>Physical Chemistry</subject><subject>Physics</subject><subject>Physics and Astronomy</subject><subject>Stochastic models</subject><subject>Studies</subject><issn>0543-1972</issn><issn>1573-8906</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kE9LxDAQxYMouK5-AG_Bg7fqJGmT5iiL_0DwsnsOaZp0u7TNmrTg-ulNWUEQPA3M_N7jzUPomsAdARD3kRAgZQZQZhKAZuUJWpBCsKyUwE_RAoqcZUQKeo4uYtwBABNcLtBmvbXY-dDrsfUD9g47q8cp2DhvcbDGN0P71Q4NNr7fd_YTt71u0rnS0dY4aeLozVbHsTW4sb63YzhcojOnu2ivfuYSbZ4e16uX7O39-XX18JYZSssx00LmriDE1FRCTQtX6IoLliIDFBzyihNdacIqmec5q5yRJeNWMCskq4uasiW6Pfrug_-YbBxV30Zju04P1k9RMZ4zSrhM4M0fcOenMKRsioJMdoyXCSJHyAQfY7BO7UN6NhwUATW3rI4tq9SymltWs4YeNTGxQ2PDr_H_om8P938R</recordid><startdate>20080201</startdate><enddate>20080201</enddate><creator>Fedotov, N. G.</creator><creator>Kol’chugin, A. S.</creator><creator>Smol’kin, O. A.</creator><creator>Moiseev, A. V.</creator><creator>Romanov, S. V.</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7U5</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>88I</scope><scope>8AL</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8FL</scope><scope>ABJCF</scope><scope>ABUWG</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>FRNLG</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K60</scope><scope>K6~</scope><scope>K7-</scope><scope>L.-</scope><scope>L6V</scope><scope>L7M</scope><scope>M0C</scope><scope>M0N</scope><scope>M2P</scope><scope>M7S</scope><scope>P62</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYYUZ</scope><scope>Q9U</scope></search><sort><creationdate>20080201</creationdate><title>The formation of features for recognizing complex images based on stochastic geometry</title><author>Fedotov, N. G. ; Kol’chugin, A. S. ; Smol’kin, O. A. ; Moiseev, A. V. ; Romanov, S. V.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c228t-a794f511cd290d25f5ab673890005604b61aba13b94443bfc9836e73e793d5d23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Analytical Chemistry</topic><topic>Characterization and Evaluation of Materials</topic><topic>Diagnostics</topic><topic>Geometry</topic><topic>Investigations</topic><topic>Measurement Science and Instrumentation</topic><topic>Measurement techniques</topic><topic>Medical and Biological Measurements</topic><topic>Pattern recognition</topic><topic>Physical Chemistry</topic><topic>Physics</topic><topic>Physics and Astronomy</topic><topic>Stochastic models</topic><topic>Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fedotov, N. G.</creatorcontrib><creatorcontrib>Kol’chugin, A. S.</creatorcontrib><creatorcontrib>Smol’kin, O. A.</creatorcontrib><creatorcontrib>Moiseev, A. V.</creatorcontrib><creatorcontrib>Romanov, S. V.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Access via ABI/INFORM (ProQuest)</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</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>Business Premium Collection (Alumni)</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 (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Computer Science Database</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>ABI/INFORM Global</collection><collection>Computing Database</collection><collection>Science Database</collection><collection>Engineering Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>ABI/INFORM Collection China</collection><collection>ProQuest Central Basic</collection><jtitle>Measurement techniques</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fedotov, N. G.</au><au>Kol’chugin, A. S.</au><au>Smol’kin, O. A.</au><au>Moiseev, A. V.</au><au>Romanov, S. V.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The formation of features for recognizing complex images based on stochastic geometry</atitle><jtitle>Measurement techniques</jtitle><stitle>Meas Tech</stitle><date>2008-02-01</date><risdate>2008</risdate><volume>51</volume><issue>2</issue><spage>199</spage><epage>207</epage><pages>199-207</pages><issn>0543-1972</issn><eissn>1573-8906</eissn><abstract>The theory of obtaining the features of pattern recognition based on stochastic geometry and having a three-functional structure (triplet features) is presented. The effectiveness of the proposed method of forming features for recognizing complex-structured and semantically saturated images is proved. A practical example of the formation of features for images from the area of medical diagnostics is considered.</abstract><cop>Boston</cop><pub>Springer US</pub><doi>10.1007/s11018-008-9002-8</doi><tpages>9</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0543-1972 |
| ispartof | Measurement techniques, 2008-02, Vol.51 (2), p.199-207 |
| issn | 0543-1972 1573-8906 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_36432169 |
| source | SpringerLink Journals - AutoHoldings |
| subjects | Analytical Chemistry Characterization and Evaluation of Materials Diagnostics Geometry Investigations Measurement Science and Instrumentation Measurement techniques Medical and Biological Measurements Pattern recognition Physical Chemistry Physics Physics and Astronomy Stochastic models Studies |
| title | The formation of features for recognizing complex images based on stochastic geometry |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-29T12%3A27%3A40IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=The%20formation%20of%20features%20for%20recognizing%20complex%20images%20based%20on%20stochastic%20geometry&rft.jtitle=Measurement%20techniques&rft.au=Fedotov,%20N.%20G.&rft.date=2008-02-01&rft.volume=51&rft.issue=2&rft.spage=199&rft.epage=207&rft.pages=199-207&rft.issn=0543-1972&rft.eissn=1573-8906&rft_id=info:doi/10.1007/s11018-008-9002-8&rft_dat=%3Cproquest_cross%3E36432169%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=209793368&rft_id=info:pmid/&rfr_iscdi=true |