Fast adaptive fuzzy enhancement and correlation features analysis of flame image of sintering section
The state of the sintering end point can be indirectly reflected by the flame image characteristics of the material layer section at the end of the sintering machine. However, the image of tail section collected by industrial camera is easy to be interfered by smoke, dust and thermal radiation. As a...
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| Veröffentlicht in: | Signal, image and video processing image and video processing, 2021-04, Vol.15 (3), p.539-546 |
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| creator | Wang, Fubin Liu, Hefei He, Jianghong |
| description | The state of the sintering end point can be indirectly reflected by the flame image characteristics of the material layer section at the end of the sintering machine. However, the image of tail section collected by industrial camera is easy to be interfered by smoke, dust and thermal radiation. As a result, the edge between the flame area and the material layer area becomes fuzzy, accompanied with halo and noise, which leads to the degradation of flame image. In order to solve the problem of image quality degradation, a new method based on weighted guided image filtering and fast adaptive fuzzy enhancement of flame image of sintering cross section is proposed in this paper; furthermore, the correlation analysis of the flame image characteristics of sintering section is carried out. The main contents of this paper include three parts: cross-sectional flame image enhancement, image brightness characteristics and geometric feature extraction, and image feature correlation analysis. The results show that the proposed method effectively eliminates the interference of noise and halo in the cross-sectional flame image. The brightness characteristics of the flame image are related to the length and height of the flame and the area of the red fire region, while there is no correlation between the brightness characteristics of the flame image and the centroid variance. Therefore, the brightness characteristics and the centroid variance can be used as the input feature for the discrimination of sintering state. |
| doi_str_mv | 10.1007/s11760-020-01774-5 |
| format | Article |
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However, the image of tail section collected by industrial camera is easy to be interfered by smoke, dust and thermal radiation. As a result, the edge between the flame area and the material layer area becomes fuzzy, accompanied with halo and noise, which leads to the degradation of flame image. In order to solve the problem of image quality degradation, a new method based on weighted guided image filtering and fast adaptive fuzzy enhancement of flame image of sintering cross section is proposed in this paper; furthermore, the correlation analysis of the flame image characteristics of sintering section is carried out. The main contents of this paper include three parts: cross-sectional flame image enhancement, image brightness characteristics and geometric feature extraction, and image feature correlation analysis. The results show that the proposed method effectively eliminates the interference of noise and halo in the cross-sectional flame image. The brightness characteristics of the flame image are related to the length and height of the flame and the area of the red fire region, while there is no correlation between the brightness characteristics of the flame image and the centroid variance. Therefore, the brightness characteristics and the centroid variance can be used as the input feature for the discrimination of sintering state.</description><identifier>ISSN: 1863-1703</identifier><identifier>EISSN: 1863-1711</identifier><identifier>DOI: 10.1007/s11760-020-01774-5</identifier><language>eng</language><publisher>London: Springer London</publisher><subject>Adaptive filters ; Area ; Brightness ; Centroids ; Computer Imaging ; Computer Science ; Correlation analysis ; Cross-sections ; Feature extraction ; Image degradation ; Image enhancement ; Image filters ; Image Processing and Computer Vision ; Image quality ; Multimedia Information Systems ; Original Paper ; Pattern Recognition and Graphics ; Signal,Image and Speech Processing ; Sintering ; Thermal radiation ; Vision</subject><ispartof>Signal, image and video processing, 2021-04, Vol.15 (3), p.539-546</ispartof><rights>Springer-Verlag London Ltd., part of Springer Nature 2020</rights><rights>Springer-Verlag London Ltd., part of Springer Nature 2020.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c356t-23c660aa6b0731543f7046c2ceb4776340eca529634a9823a3451ecdec1609c13</citedby><cites>FETCH-LOGICAL-c356t-23c660aa6b0731543f7046c2ceb4776340eca529634a9823a3451ecdec1609c13</cites><orcidid>0000-0002-6853-3853</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11760-020-01774-5$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11760-020-01774-5$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Wang, Fubin</creatorcontrib><creatorcontrib>Liu, Hefei</creatorcontrib><creatorcontrib>He, Jianghong</creatorcontrib><title>Fast adaptive fuzzy enhancement and correlation features analysis of flame image of sintering section</title><title>Signal, image and video processing</title><addtitle>SIViP</addtitle><description>The state of the sintering end point can be indirectly reflected by the flame image characteristics of the material layer section at the end of the sintering machine. However, the image of tail section collected by industrial camera is easy to be interfered by smoke, dust and thermal radiation. As a result, the edge between the flame area and the material layer area becomes fuzzy, accompanied with halo and noise, which leads to the degradation of flame image. In order to solve the problem of image quality degradation, a new method based on weighted guided image filtering and fast adaptive fuzzy enhancement of flame image of sintering cross section is proposed in this paper; furthermore, the correlation analysis of the flame image characteristics of sintering section is carried out. The main contents of this paper include three parts: cross-sectional flame image enhancement, image brightness characteristics and geometric feature extraction, and image feature correlation analysis. The results show that the proposed method effectively eliminates the interference of noise and halo in the cross-sectional flame image. The brightness characteristics of the flame image are related to the length and height of the flame and the area of the red fire region, while there is no correlation between the brightness characteristics of the flame image and the centroid variance. Therefore, the brightness characteristics and the centroid variance can be used as the input feature for the discrimination of sintering state.</description><subject>Adaptive filters</subject><subject>Area</subject><subject>Brightness</subject><subject>Centroids</subject><subject>Computer Imaging</subject><subject>Computer Science</subject><subject>Correlation analysis</subject><subject>Cross-sections</subject><subject>Feature extraction</subject><subject>Image degradation</subject><subject>Image enhancement</subject><subject>Image filters</subject><subject>Image Processing and Computer Vision</subject><subject>Image quality</subject><subject>Multimedia Information Systems</subject><subject>Original Paper</subject><subject>Pattern Recognition and Graphics</subject><subject>Signal,Image and Speech Processing</subject><subject>Sintering</subject><subject>Thermal radiation</subject><subject>Vision</subject><issn>1863-1703</issn><issn>1863-1711</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9UMFOwzAMjRBITGM_wCkS50KctEl7RBMDpElc4Bx5mTs6dW1JWqTt60kpghuWLNt6fk_2Y-waxC0IYe4CgNEiETImGJMm2RmbQa5VAgbg_LcX6pItQtiLGEqaXOczRisMPcctdn31SbwcTqcjp-YdG0cHaiLUbLlrvaca-6pteEnYD55CBLA-hirwtuRljQfi1QF3NI6hanryVbPjgdzIumIXJdaBFj91zt5WD6_Lp2T98vi8vF8nTmW6T6RyWgtEvRFGQZaq0ohUO-lokxqjVSrIYSaL2GGRS4UqzYDclhxoUThQc3Yz6Xa-_Rgo9HbfDj4eGqzMBEiTysLELTltOd-G4Km0nY-3-6MFYUdH7eSojY7ab0dtFklqIoVu_Iz8n_Q_rC-OP3kW</recordid><startdate>20210401</startdate><enddate>20210401</enddate><creator>Wang, Fubin</creator><creator>Liu, Hefei</creator><creator>He, Jianghong</creator><general>Springer London</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0002-6853-3853</orcidid></search><sort><creationdate>20210401</creationdate><title>Fast adaptive fuzzy enhancement and correlation features analysis of flame image of sintering section</title><author>Wang, Fubin ; Liu, Hefei ; He, Jianghong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c356t-23c660aa6b0731543f7046c2ceb4776340eca529634a9823a3451ecdec1609c13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Adaptive filters</topic><topic>Area</topic><topic>Brightness</topic><topic>Centroids</topic><topic>Computer Imaging</topic><topic>Computer Science</topic><topic>Correlation analysis</topic><topic>Cross-sections</topic><topic>Feature extraction</topic><topic>Image degradation</topic><topic>Image enhancement</topic><topic>Image filters</topic><topic>Image Processing and Computer Vision</topic><topic>Image quality</topic><topic>Multimedia Information Systems</topic><topic>Original Paper</topic><topic>Pattern Recognition and Graphics</topic><topic>Signal,Image and Speech Processing</topic><topic>Sintering</topic><topic>Thermal radiation</topic><topic>Vision</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Fubin</creatorcontrib><creatorcontrib>Liu, Hefei</creatorcontrib><creatorcontrib>He, Jianghong</creatorcontrib><collection>CrossRef</collection><jtitle>Signal, image and video processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Fubin</au><au>Liu, Hefei</au><au>He, Jianghong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fast adaptive fuzzy enhancement and correlation features analysis of flame image of sintering section</atitle><jtitle>Signal, image and video processing</jtitle><stitle>SIViP</stitle><date>2021-04-01</date><risdate>2021</risdate><volume>15</volume><issue>3</issue><spage>539</spage><epage>546</epage><pages>539-546</pages><issn>1863-1703</issn><eissn>1863-1711</eissn><abstract>The state of the sintering end point can be indirectly reflected by the flame image characteristics of the material layer section at the end of the sintering machine. However, the image of tail section collected by industrial camera is easy to be interfered by smoke, dust and thermal radiation. As a result, the edge between the flame area and the material layer area becomes fuzzy, accompanied with halo and noise, which leads to the degradation of flame image. In order to solve the problem of image quality degradation, a new method based on weighted guided image filtering and fast adaptive fuzzy enhancement of flame image of sintering cross section is proposed in this paper; furthermore, the correlation analysis of the flame image characteristics of sintering section is carried out. The main contents of this paper include three parts: cross-sectional flame image enhancement, image brightness characteristics and geometric feature extraction, and image feature correlation analysis. The results show that the proposed method effectively eliminates the interference of noise and halo in the cross-sectional flame image. The brightness characteristics of the flame image are related to the length and height of the flame and the area of the red fire region, while there is no correlation between the brightness characteristics of the flame image and the centroid variance. Therefore, the brightness characteristics and the centroid variance can be used as the input feature for the discrimination of sintering state.</abstract><cop>London</cop><pub>Springer London</pub><doi>10.1007/s11760-020-01774-5</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0002-6853-3853</orcidid></addata></record> |
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| subjects | Adaptive filters Area Brightness Centroids Computer Imaging Computer Science Correlation analysis Cross-sections Feature extraction Image degradation Image enhancement Image filters Image Processing and Computer Vision Image quality Multimedia Information Systems Original Paper Pattern Recognition and Graphics Signal,Image and Speech Processing Sintering Thermal radiation Vision |
| title | Fast adaptive fuzzy enhancement and correlation features analysis of flame image of sintering section |
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