Maximum-Entropy Expectation-Maximization Algorithm for Image Reconstruction and Sensor Field Estimation
In this paper, we propose a maximum-entropy expectation-maximization (MEEM) algorithm. We use the proposed algorithm for density estimation. The maximum-entropy constraint is imposed for smoothness of the estimated density function. The derivation of the MEEM algorithm requires determination of the...
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| Veröffentlicht in: | IEEE transactions on image processing 2008-06, Vol.17 (6), p.897-907 |
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| description | In this paper, we propose a maximum-entropy expectation-maximization (MEEM) algorithm. We use the proposed algorithm for density estimation. The maximum-entropy constraint is imposed for smoothness of the estimated density function. The derivation of the MEEM algorithm requires determination of the covariance matrix in the framework of the maximum-entropy likelihood function, which is difficult to solve analytically. We, therefore, derive the MEEM algorithm by optimizing a lower-bound of the maximum-entropy likelihood function. We note that the classical expectation-maximization (EM) algorithm has been employed previously for 2-D density estimation. We propose to extend the use of the classical EM algorithm for image recovery from randomly sampled data and sensor field estimation from randomly scattered sensor networks. We further propose to use our approach in density estimation, image recovery and sensor field estimation. Computer simulation experiments are used to demonstrate the superior performance of the proposed MEEM algorithm in comparison to existing methods. |
| doi_str_mv | 10.1109/TIP.2008.921996 |
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We use the proposed algorithm for density estimation. The maximum-entropy constraint is imposed for smoothness of the estimated density function. The derivation of the MEEM algorithm requires determination of the covariance matrix in the framework of the maximum-entropy likelihood function, which is difficult to solve analytically. We, therefore, derive the MEEM algorithm by optimizing a lower-bound of the maximum-entropy likelihood function. We note that the classical expectation-maximization (EM) algorithm has been employed previously for 2-D density estimation. We propose to extend the use of the classical EM algorithm for image recovery from randomly sampled data and sensor field estimation from randomly scattered sensor networks. We further propose to use our approach in density estimation, image recovery and sensor field estimation. Computer simulation experiments are used to demonstrate the superior performance of the proposed MEEM algorithm in comparison to existing methods.</description><identifier>ISSN: 1057-7149</identifier><identifier>EISSN: 1941-0042</identifier><identifier>DOI: 10.1109/TIP.2008.921996</identifier><identifier>PMID: 18482885</identifier><identifier>CODEN: IIPRE4</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Algorithms ; Applied sciences ; Approximation methods ; Computational efficiency ; Computer Simulation ; Covariance matrix ; Data Interpretation, Statistical ; Density ; Density functional theory ; Entropy ; Exact sciences and technology ; Expectation-maximization (EM) ; Expectation-maximization algorithms ; Gaussian mixture model (GMM) ; Image Enhancement - methods ; Image Interpretation, Computer-Assisted - methods ; Image processing ; Image reconstruction ; image reconstrution ; Image sensors ; Information, signal and communications theory ; Kernel ; Kernel density estimation ; Likelihood Functions ; Mathematical analysis ; Mathematical models ; maximum entropy ; Models, Statistical ; Parzen density ; Recovery ; Reproducibility of Results ; Sampled data ; Sensitivity and Specificity ; sensor field estimation ; Sensors ; Signal processing ; Signal Processing, Computer-Assisted ; Studies ; Support vector machines ; Telecommunications and information theory</subject><ispartof>IEEE transactions on image processing, 2008-06, Vol.17 (6), p.897-907</ispartof><rights>2008 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2008</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c404t-aaa13132a937ec2583767c0993b0b9f11dd439961745215509fd3bb4399e5e733</citedby><cites>FETCH-LOGICAL-c404t-aaa13132a937ec2583767c0993b0b9f11dd439961745215509fd3bb4399e5e733</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4494455$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4494455$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=20325601$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/18482885$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Hunsop Hong</creatorcontrib><creatorcontrib>Schonfeld, D.</creatorcontrib><title>Maximum-Entropy Expectation-Maximization Algorithm for Image Reconstruction and Sensor Field Estimation</title><title>IEEE transactions on image processing</title><addtitle>TIP</addtitle><addtitle>IEEE Trans Image Process</addtitle><description>In this paper, we propose a maximum-entropy expectation-maximization (MEEM) algorithm. We use the proposed algorithm for density estimation. The maximum-entropy constraint is imposed for smoothness of the estimated density function. The derivation of the MEEM algorithm requires determination of the covariance matrix in the framework of the maximum-entropy likelihood function, which is difficult to solve analytically. We, therefore, derive the MEEM algorithm by optimizing a lower-bound of the maximum-entropy likelihood function. We note that the classical expectation-maximization (EM) algorithm has been employed previously for 2-D density estimation. We propose to extend the use of the classical EM algorithm for image recovery from randomly sampled data and sensor field estimation from randomly scattered sensor networks. We further propose to use our approach in density estimation, image recovery and sensor field estimation. Computer simulation experiments are used to demonstrate the superior performance of the proposed MEEM algorithm in comparison to existing methods.</description><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Approximation methods</subject><subject>Computational efficiency</subject><subject>Computer Simulation</subject><subject>Covariance matrix</subject><subject>Data Interpretation, Statistical</subject><subject>Density</subject><subject>Density functional theory</subject><subject>Entropy</subject><subject>Exact sciences and technology</subject><subject>Expectation-maximization (EM)</subject><subject>Expectation-maximization algorithms</subject><subject>Gaussian mixture model (GMM)</subject><subject>Image Enhancement - methods</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Image processing</subject><subject>Image reconstruction</subject><subject>image reconstrution</subject><subject>Image sensors</subject><subject>Information, signal and communications theory</subject><subject>Kernel</subject><subject>Kernel density estimation</subject><subject>Likelihood Functions</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>maximum entropy</subject><subject>Models, Statistical</subject><subject>Parzen density</subject><subject>Recovery</subject><subject>Reproducibility of Results</subject><subject>Sampled data</subject><subject>Sensitivity and Specificity</subject><subject>sensor field estimation</subject><subject>Sensors</subject><subject>Signal processing</subject><subject>Signal Processing, Computer-Assisted</subject><subject>Studies</subject><subject>Support vector machines</subject><subject>Telecommunications and information theory</subject><issn>1057-7149</issn><issn>1941-0042</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNp9kc1rFTEUxYNYbK2uXQgyCOpqXu_NxyRZlvKqD1oqWtdDJpN5psxMXpMZaP3rzfuggouu8nF-91wOh5B3CAtE0Ge3q-8LCqAWmqLW1QtygppjCcDpy3wHIUuJXB-T1yndASAXWL0ix6i4okqJE7K-Ng9-mIdyOU4xbB6L5cPG2clMPozlTvN_do_ivF-H6KffQ9GFWKwGs3bFD2fDmKY42x1ixrb46caU9Uvv-rZYpskPu_E35KgzfXJvD-cp-XW5vL34Vl7dfF1dnF-VlgOfSmMMMmTUaCadpUIxWUkLWrMGGt0hti1nOShKLigKAbprWdNs_5xwkrFT8mXvu4nhfnZpqgefrOt7M7owp1opqHilVZXJz8-SEiQHwXUGP_4H3oU5jjlFnW0Y15Ju957tIRtDStF19Sbm6PGxRqi3VdW5qnpbVb2vKk98ONjOzeDaf_yhmwx8OgAmWdN30YzWpyeOAqOiAszc-z3nnXNPMueacyHYX-oco84</recordid><startdate>20080601</startdate><enddate>20080601</enddate><creator>Hunsop Hong</creator><creator>Schonfeld, D.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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We use the proposed algorithm for density estimation. The maximum-entropy constraint is imposed for smoothness of the estimated density function. The derivation of the MEEM algorithm requires determination of the covariance matrix in the framework of the maximum-entropy likelihood function, which is difficult to solve analytically. We, therefore, derive the MEEM algorithm by optimizing a lower-bound of the maximum-entropy likelihood function. We note that the classical expectation-maximization (EM) algorithm has been employed previously for 2-D density estimation. We propose to extend the use of the classical EM algorithm for image recovery from randomly sampled data and sensor field estimation from randomly scattered sensor networks. We further propose to use our approach in density estimation, image recovery and sensor field estimation. Computer simulation experiments are used to demonstrate the superior performance of the proposed MEEM algorithm in comparison to existing methods.</abstract><cop>New York, NY</cop><pub>IEEE</pub><pmid>18482885</pmid><doi>10.1109/TIP.2008.921996</doi><tpages>11</tpages></addata></record> |
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| subjects | Algorithms Applied sciences Approximation methods Computational efficiency Computer Simulation Covariance matrix Data Interpretation, Statistical Density Density functional theory Entropy Exact sciences and technology Expectation-maximization (EM) Expectation-maximization algorithms Gaussian mixture model (GMM) Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image processing Image reconstruction image reconstrution Image sensors Information, signal and communications theory Kernel Kernel density estimation Likelihood Functions Mathematical analysis Mathematical models maximum entropy Models, Statistical Parzen density Recovery Reproducibility of Results Sampled data Sensitivity and Specificity sensor field estimation Sensors Signal processing Signal Processing, Computer-Assisted Studies Support vector machines Telecommunications and information theory |
| title | Maximum-Entropy Expectation-Maximization Algorithm for Image Reconstruction and Sensor Field Estimation |
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