Computer Vision
Notes from the Udacity Computer Vision Course taught by Georgia Tech professors.
Segmentation
Mean Shift Segmentation
- For every pixel (or a sample of pixels) in the image calculate some features such as (u,v)-color or (x,y, u, v) where xy are coordinates and uv are chroma.
- For each sampled pixel, or region of interest, calculate the new center-of-mass, or weighted-mean. The weights are typically Gaussian based on distance to the center. Repeat until convergence.
- The regions will cluster into modes. All regions which cluster to the same position are in the same attraction basin.
Attraction basin: the region for which all trajectories lead to the same mode.
- Pros
- Automatically finds basins of attraction.
- Only one parameter: Window size for region of interest.
- Does not assume any shape on cluster.
- Cons
- Need to pick a window size.
- Doesn't scale well for high dimensions.