Image sequence processing using spatiotemporal segmentation
We investigate the improvements that can be obtained in several conventional video-processing algorithms through the incorporation of three-dimensional (3-D) (spatiotemporal) segmentation information. Four classes of image sequence processing techniques are considered: low-pass filtering, high-pass...
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| Published in | IEEE transactions on circuits and systems for video technology Vol. 9; no. 5; pp. 798 - 807 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
New York, NY
IEEE
01.08.1999
Institute of Electrical and Electronics Engineers |
| Subjects | |
| Online Access | Get full text |
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| Abstract | We investigate the improvements that can be obtained in several conventional video-processing algorithms through the incorporation of three-dimensional (3-D) (spatiotemporal) segmentation information. Four classes of image sequence processing techniques are considered: low-pass filtering, high-pass filtering, high-frequency emphasis, and 3-D Sobel filtering. It is demonstrated that segmentation information can improve the performance of these techniques substantially so that this approach may be promising for other applications (e.g., deinterlacing and resolution conversion) as well. |
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| AbstractList | We investigate the improvements that can be obtained in several conventional video-processing algorithms through the incorporation of three-dimensional (3-D) (spatiotemporal) segmentation information. Four classes of image sequence processing techniques are considered: low-pass filtering, high-pass filtering, high-frequency emphasis, and 3-D Sobel filtering. It is demonstrated that segmentation information can improve the performance of these techniques substantially so that this approach may be promising for other applications (e.g., deinterlacing and resolution conversion) as well. We investigate the improvements that can be obtained in several conventional video-processing algorithms through the incorporation of three-dimensional (3-D) (spatiotemporal) segmentation information. Four classes of image sequence processing techniques are considered: low-pass filtering, high-pass filtering, high-frequency emphasis, and 3-D Sobel filtering. It is demonstrated that segmentation information can improve the performance of these techniques substantially so that this approach may be promising for other applications (e.g., deinterlacing and resolution conversion) as well |
| Author | Wu, G.K. Reed, T.R. |
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| Cites_doi | 10.1016/S0734-189X(85)90153-7 10.1109/ICIP.1994.413622 10.1109/TCOM.1977.1093773 10.1109/PROC.1985.13184 10.1080/02664768900000014 10.1016/0031-3203(93)90135-J |
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| Keywords | Markov process Image analysis Experimental result Algorithm performance Segmentation Image processing Image sequence Video signal Modeling Selective filter |
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| References | ref8 ref7 pudney (ref10) 1995 ref3 ref6 ref11 zhang (ref2) 1997; 7 aach (ref9) 1990; 1360 ref1 reed (ref4) 1995; 1 wu (ref5) 1995; 2 |
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| SubjectTerms | Algorithms Applied sciences Circuits Conversion Exact sciences and technology Filtering Filtration Image coding Image converters Image edge detection Image processing Image segmentation Image sequence analysis Image sequences Information, signal and communications theory Low pass filters Performance enhancement Segmentation Signal processing Spatiotemporal phenomena Telecommunications and information theory |
| Title | Image sequence processing using spatiotemporal segmentation |
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