Modeling of Video Sequences by Gaussian Mixture: Application in Motion Estimation by Block Matching Method
<p/> <p>This article investigates a new method of motion estimation based on block matching criterion through the modeling of image blocks by a mixture of two and three Gaussian distributions. Mixture parameters (weights, means vectors, and covariance matrices) are estimated by the Expec...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2010-01-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Online Access: | http://asp.eurasipjournals.com/content/2010/210937 |
| _version_ | 1818228365323141120 |
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| author | Nsiri Benayad Boudlal Abdenaceur Aboutajdine Driss |
| author_facet | Nsiri Benayad Boudlal Abdenaceur Aboutajdine Driss |
| author_sort | Nsiri Benayad |
| collection | DOAJ |
| description | <p/> <p>This article investigates a new method of motion estimation based on block matching criterion through the modeling of image blocks by a mixture of two and three Gaussian distributions. Mixture parameters (weights, means vectors, and covariance matrices) are estimated by the Expectation Maximization algorithm (EM) which maximizes the log-likelihood criterion. The similarity between a block in the current image and the more resembling one in a search window on the reference image is measured by the minimization of Extended Mahalanobis distance between the clusters of mixture. Performed experiments on sequences of real images have given good results, and PSNR reached 3 dB.</p> |
| first_indexed | 2024-12-12T10:01:33Z |
| format | Article |
| id | doaj.art-8d7843c5216e4bb38d6128bd1c31d95e |
| institution | Directory Open Access Journal |
| issn | 1687-6172 1687-6180 |
| language | English |
| last_indexed | 2024-12-12T10:01:33Z |
| publishDate | 2010-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-8d7843c5216e4bb38d6128bd1c31d95e2022-12-22T00:27:59ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802010-01-0120101210937Modeling of Video Sequences by Gaussian Mixture: Application in Motion Estimation by Block Matching MethodNsiri BenayadBoudlal AbdenaceurAboutajdine Driss<p/> <p>This article investigates a new method of motion estimation based on block matching criterion through the modeling of image blocks by a mixture of two and three Gaussian distributions. Mixture parameters (weights, means vectors, and covariance matrices) are estimated by the Expectation Maximization algorithm (EM) which maximizes the log-likelihood criterion. The similarity between a block in the current image and the more resembling one in a search window on the reference image is measured by the minimization of Extended Mahalanobis distance between the clusters of mixture. Performed experiments on sequences of real images have given good results, and PSNR reached 3 dB.</p>http://asp.eurasipjournals.com/content/2010/210937 |
| spellingShingle | Nsiri Benayad Boudlal Abdenaceur Aboutajdine Driss Modeling of Video Sequences by Gaussian Mixture: Application in Motion Estimation by Block Matching Method EURASIP Journal on Advances in Signal Processing |
| title | Modeling of Video Sequences by Gaussian Mixture: Application in Motion Estimation by Block Matching Method |
| title_full | Modeling of Video Sequences by Gaussian Mixture: Application in Motion Estimation by Block Matching Method |
| title_fullStr | Modeling of Video Sequences by Gaussian Mixture: Application in Motion Estimation by Block Matching Method |
| title_full_unstemmed | Modeling of Video Sequences by Gaussian Mixture: Application in Motion Estimation by Block Matching Method |
| title_short | Modeling of Video Sequences by Gaussian Mixture: Application in Motion Estimation by Block Matching Method |
| title_sort | modeling of video sequences by gaussian mixture application in motion estimation by block matching method |
| url | http://asp.eurasipjournals.com/content/2010/210937 |
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