Name
Abstract | ||
|---|---|---|
We present a multistage affine motion segmentation method that combines the benefits of the dominant motion and block-based affine modeling approaches. In particular, we propose two key modifications to a recent motion segmentation algorithm developed by Wang and Adelson (1994). 1) The adaptive k-means clustering step is replaced by a merging step, whereby the affine parameters of a block which has the smallest representation error, rather than the respective cluster center, is used to represent each layer; and 2) we implement it in multiple stages, where pixels belonging to a single motion model are labeled at each stage. Performance improvement due to the proposed modifications is demonstrated on real video frames. |
Year | DOI | Venue |
|---|---|---|
1997 | 10.1109/83.641420 | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
Keywords | Field | DocType |
block-based affine modeling,video signal processing,real video frames,merging step,image segmentation,merging,motion estimation,adaptive k-means clustering step,multistage affine classification,image classification,dominant motion,motion segmentation | Affine transformation,Affine shape adaptation,Computer vision,Harris affine region detector,Pattern recognition,Segmentation,Image segmentation,Artificial intelligence,Motion estimation,Cluster analysis,Contextual image classification,Mathematics | Journal |
Volume | Issue | ISSN |
6 | 11 | 1057-7149 |
Citations | PageRank | References |
41 | 3.09 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| G. D. Borshukov | 1 | 41 | 3.09 |
| G. Bozdagi | 2 | 221 | 21.93 |
| Y. Altunbasak | 3 | 1042 | 85.73 |
| A Murat Tekalp | 4 | 1893 | 201.68 |