This thesis proposes a new chroma keying method that can automatically
detect background, foreground, and unknown regions. For background color
detection, we use K-means clustering in color space to calculate the limited
number of clusters of background colors. We use spatial information to clean
the background regions and minimize the unknown regions. Our method only
needs minimum inputs from user.
For unknown regions, we implement the alpha matte based on Wang's robust
matting algorithm, which is considered one of the best algorithms in the
literature, if not the best. Wang's algorithm is based on modified random walk.
We proposed a better color selection method, which improves matting results
in the experiments. In the thesis, a detailed implementation of robust
matting is provided.
The experimental results demonstrate that our proposed method can handle
images with one background color, images with gridded background, and images
with difficult regions such as complex hair stripes and semi-transparent
clothes.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/20351 |
| Date | January 2011 |
| Creators | Luo, Zhenyi |
| Contributors | Zhao, Jiying |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.0024 seconds