Estimation of Displacement Vector Field from Noisy Data using Maximum Likelihood Estimator
The present study proposes an approach for robust motion estimation between two successive image frames, from a degraded sequence. The method is based on generalized cross-correlation (GCC) methods, where the phase of the Fourier components is used for motion parameter estimation. This method uses &...
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| creator | El Mehdi, Ismaili Aalaoui El Haj Elhassane, Ibn |
| description | The present study proposes an approach for robust motion estimation between two successive image frames, from a degraded sequence. The method is based on generalized cross-correlation (GCC) methods, where the phase of the Fourier components is used for motion parameter estimation. This method uses "whitening" FIR filters to sharpen the cross-correlation maximum, thereby improving the accuracy of identification of the peak. The estimators of interest are the phase transform (PHAT), and the maximum likelihood (ML) estimators. For robust motion estimation it has been found that the ML estimator is particularly suited to this purpose. The accuracy of the estimators is also discussed. Significant results have been obtained for sub-pixel translation of images of different nature and across different spectral bands. |
| doi_str_mv | 10.1109/ICECS.2007.4511256 |
| format | Conference Proceeding |
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The method is based on generalized cross-correlation (GCC) methods, where the phase of the Fourier components is used for motion parameter estimation. This method uses "whitening" FIR filters to sharpen the cross-correlation maximum, thereby improving the accuracy of identification of the peak. The estimators of interest are the phase transform (PHAT), and the maximum likelihood (ML) estimators. For robust motion estimation it has been found that the ML estimator is particularly suited to this purpose. The accuracy of the estimators is also discussed. 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The method is based on generalized cross-correlation (GCC) methods, where the phase of the Fourier components is used for motion parameter estimation. This method uses "whitening" FIR filters to sharpen the cross-correlation maximum, thereby improving the accuracy of identification of the peak. The estimators of interest are the phase transform (PHAT), and the maximum likelihood (ML) estimators. For robust motion estimation it has been found that the ML estimator is particularly suited to this purpose. The accuracy of the estimators is also discussed. Significant results have been obtained for sub-pixel translation of images of different nature and across different spectral bands.</description><subject>Degradation</subject><subject>Finite impulse response filter</subject><subject>Gaussian noise</subject><subject>Image sequences</subject><subject>Maximum likelihood estimation</subject><subject>Motion estimation</subject><subject>Parameter estimation</subject><subject>Phase estimation</subject><subject>Robustness</subject><subject>Video compression</subject><isbn>142441377X</isbn><isbn>9781424413775</isbn><isbn>1424413788</isbn><isbn>9781424413782</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2007</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFUEtOwzAUNEKVoKUXgI0vkODnT-wsUZqWSgEWfITYVE5igyGJqziV6O0JIhJvMxrNaGb0ELoEEgOQ9Hqb5dljTAmRMRcAVCQnaA6ccg5MKnX6T-TrDM1_jSlVCSVnaBnCJxmPC06FOkdveRhcqwfnO-wtXrmwb3RlWtMN-MVUg-_x2pmmxrb3Lb73LhzxSg8aH4Lr3vGd_nbtocWF-zKN-_C-xlOg7y_QzOommOWEC_S8zp-y26h42GyzmyJyIMUQKc7UOFCKGkSpyrpKdMm0rVNhGZQGOKtIbRll1nJeAqEKiEyqUbKClpazBbr6y3XGmN2-H9v74256DPsBODRVvQ</recordid><startdate>200712</startdate><enddate>200712</enddate><creator>El Mehdi, Ismaili Aalaoui</creator><creator>El Haj Elhassane, Ibn</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200712</creationdate><title>Estimation of Displacement Vector Field from Noisy Data using Maximum Likelihood Estimator</title><author>El Mehdi, Ismaili Aalaoui ; El Haj Elhassane, Ibn</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-843800775d15b8bdc6ab3afd95f31be143c0df323ff44b10281076c1bef52bf43</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Degradation</topic><topic>Finite impulse response filter</topic><topic>Gaussian noise</topic><topic>Image sequences</topic><topic>Maximum likelihood estimation</topic><topic>Motion estimation</topic><topic>Parameter estimation</topic><topic>Phase estimation</topic><topic>Robustness</topic><topic>Video compression</topic><toplevel>online_resources</toplevel><creatorcontrib>El Mehdi, Ismaili Aalaoui</creatorcontrib><creatorcontrib>El Haj Elhassane, Ibn</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>El Mehdi, Ismaili Aalaoui</au><au>El Haj Elhassane, Ibn</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Estimation of Displacement Vector Field from Noisy Data using Maximum Likelihood Estimator</atitle><btitle>2007 14th IEEE International Conference on Electronics, Circuits and Systems</btitle><stitle>ICECS</stitle><date>2007-12</date><risdate>2007</risdate><spage>1380</spage><epage>1383</epage><pages>1380-1383</pages><isbn>142441377X</isbn><isbn>9781424413775</isbn><eisbn>1424413788</eisbn><eisbn>9781424413782</eisbn><abstract>The present study proposes an approach for robust motion estimation between two successive image frames, from a degraded sequence. The method is based on generalized cross-correlation (GCC) methods, where the phase of the Fourier components is used for motion parameter estimation. This method uses "whitening" FIR filters to sharpen the cross-correlation maximum, thereby improving the accuracy of identification of the peak. The estimators of interest are the phase transform (PHAT), and the maximum likelihood (ML) estimators. For robust motion estimation it has been found that the ML estimator is particularly suited to this purpose. The accuracy of the estimators is also discussed. Significant results have been obtained for sub-pixel translation of images of different nature and across different spectral bands.</abstract><pub>IEEE</pub><doi>10.1109/ICECS.2007.4511256</doi><tpages>4</tpages></addata></record> |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Degradation Finite impulse response filter Gaussian noise Image sequences Maximum likelihood estimation Motion estimation Parameter estimation Phase estimation Robustness Video compression |
| title | Estimation of Displacement Vector Field from Noisy Data using Maximum Likelihood Estimator |
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