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DC Field | Value | Language |
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dc.contributor.author | F. Charif, A. Benchabane, N. Djedi, A. Taleb-Ahmed | - |
dc.date.accessioned | 2013-12-19T14:13:12Z | - |
dc.date.available | 2013-12-19T14:13:12Z | - |
dc.date.issued | 2013-12-19 | - |
dc.identifier.issn | waf | - |
dc.identifier.uri | http://hdl.handle.net/123456789/2644 | - |
dc.description | The INTERNATIONAL CONFERENCE ON ELECTRONICS & OIL: FROM THEORY TO APPLICATIONS March 05-06, 2013, Ouargla, Algeria | en_US |
dc.description.abstract | In this paper, we develops a new fast on line algorithms for motion estimation. This new algorithm is based on the Horn & Schunck algorithm with a new kind of recurrent neural network called Discrete Zhang Neural Networks (DZNN) and Simoncelli’s matched-pair 5 tap filters. We simulate the network on a sequential processor and compare its performance with a sequential algorithm based on the Jacobi method. Experimental results on synthetic and real image sequences for the method are given to demonstrate its fastness in comparison with Jacobi method. | en_US |
dc.language.iso | en | en_US |
dc.subject | Motion estimation | en_US |
dc.subject | discrete Zhang neural networks | en_US |
dc.subject | Horn & Schunck method | en_US |
dc.subject | Real-time applications | en_US |
dc.title | Horn & Schunck Meets a Discrete Zhang Neural Networks for Computing 2D Optical Flow | en_US |
dc.type | Article | en_US |
Appears in Collections: | 3. Faculté des sciences appliquées |
Files in This Item:
File | Description | Size | Format | |
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F_Charif.pdf | 377,26 kB | Adobe PDF | View/Open |
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