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http://hdl.handle.net/2080/653
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| DC Field | Value | Language |
|---|
| contributor.author | Subudhi, B N | - |
| contributor.author | Nanda, P K | - |
| date.accessioned | 2008-03-23T05:46:01Z | - |
| date.available | 2008-03-23T05:46:01Z | - |
| date.issued | 2008 | - |
| identifier.citation | Computational Intelligence, Control, And Computer Vision In Robotics & Automation, 10-11, March 2008, NIT Rourkela, India P 108-204 | en |
| identifier.uri | http://hdl.handle.net/2080/653 | - |
| description | copyright for the article belongs to the Proceedings Publisher | en |
| description.abstract | We propose a novel approach of moving object
detection in a video sequence. The proposed scheme uses spatial
segmentation and temporal segmentation to construct the video
object plane (VOP) and hence the detection of a moving objects.
The spatial segmentation problem is formulated in spatiotemporal
framework. A compound Markov random field model
is proposed to model the video-sequences. This compound model
employs edge features in the temporal direction. The MRF model
parameters are selected on a trial and error basis. The labels in
the spatial segmentation are estimated using Maximum a
posteriori (MAP) criterion. A hybrid algorithm is proposed to
obtain MAP estimates. These estimated labels are used to obtain
the temporal segmentation followed by construction of video
object plane. The results obtained are compared joint
segmentation scheme (JSEG). It is observed that the edge based
scheme proved to be best as compared to edgeless and JSEG
schemes. | en |
| format.extent | 308570 bytes | - |
| format.mimetype | application/pdf | - |
| language.iso | en | - |
| publisher | NITR | en |
| subject | Covariance matrices | en |
| subject | Feature extraction | en |
| subject | Gaussian distribution | en |
| subject | Gaussian process | en |
| subject | Image edge analysis | en |
| subject | Image segmentation | en |
| subject | MAP Estimation | en |
| subject | pattern | en |
| subject | Simulated Annealing | en |
| title | Moving Object Detection using Compound Markov Random Field Model | en |
| type | Article | en |
| Appears in Collections: | Conference Papers
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Files in This Item:
| File |
Description |
Size | Format |
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| Paper 38.pdf | | 301Kb | Adobe PDF | View/Open |
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