Abstract
Motion-based segmentation plays an important role in dynamic scene analysis of video sequences. In this paper, we present a scheme for extracting moving objects. First, three different resolutions of the dense optical flow fields are calculated using a complex discrete wavelet transform. Surface fitting of all levels of these vectors is then performed over the affine parametric motion model. Next, the clustering by Competitive Agglomeration algorithm is applied in the parameter space of the coarsest level. The results of this step are the optimum number of clusters and the center of each cluster. Using information from the previous level, the parameter spaces of the following levels are then segmented using the classical mixture model and the expectation-maximization algorithm. Finally, the individual moving object and background are represented in layers. Experimental results showing the significance of this proposed method are provided.
Original language | English |
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Title of host publication | IEEE International Conference on Image Processing |
Place of Publication | Piscataway, NJ |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 300-303 |
Number of pages | 4 |
Volume | 1 |
Publication status | Published - 2000 |
Externally published | Yes |
Event | 2000 International Conference on Image Processing - Vancouver, Canada Duration: 10 Sept 2000 → 13 Sept 2000 |
Conference
Conference | 2000 International Conference on Image Processing |
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City | Vancouver, Canada |
Period | 10/09/00 → 13/09/00 |