Computer Vision: Difference between revisions
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Notes from the [https://www.udacity.com/course/introduction-to-computer-vision--ud810 Udacity Computer Vision Course] taught by Georgia Tech professors. | Notes from the [https://www.udacity.com/course/introduction-to-computer-vision--ud810 Udacity Computer Vision Course] taught by Georgia Tech professors. | ||
==Hough Transform== | |||
The Hough transform is a voting technique used to find things in images such as lines, circles, and arbitrary shapes. | |||
==Segmentation== | ==Segmentation== | ||
Revision as of 14:26, 16 September 2020
Notes from the Udacity Computer Vision Course taught by Georgia Tech professors.
Hough Transform
The Hough transform is a voting technique used to find things in images such as lines, circles, and arbitrary shapes.
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.