Computer Vision: Difference between revisions

Revision as of 14:13, 16 September 2020

Notes from the Udacity Computer Vision Course taught by Georgia Tech professors.

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.

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 ==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 basin 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.