There are certain differences between graphic design and natural images that affect the goal and hints for segmentation:
We realized that a mean-shift based segmentation algorithm does not produce appropriate results on graphic design.
|
|
Graphic Design |
Natural Image |
|
Distinct segment boundaries |
Frequent |
Rare |
|
Color consistency in segments |
High |
Low |
|
Disjoint Elements |
Many |
Few |
We realized that a mean-shift based segmentation algorithm does not produce appropriate results on graphic design.
We decided to try segmentation using edge detection. Two techniques are proposed for now.
1- Run an Edge-Detector; find connected components in edges and fill up the gaps
2- Run an Edge-Detector; find connected components in non-edge pixels and call each a super-pixel
Update: I tried a few types of edge detectors. The problem is these edge detectors are not designed to generate regions, they generate edges that are often disjoint.
Here are the outputs of edge detection:
https://dl.dropboxusercontent.com/u/20022261/reports/edge_detection.html
1- Run an Edge-Detector; find connected components in edges and fill up the gaps
2- Run an Edge-Detector; find connected components in non-edge pixels and call each a super-pixel
Update: I tried a few types of edge detectors. The problem is these edge detectors are not designed to generate regions, they generate edges that are often disjoint.
Here are the outputs of edge detection:
https://dl.dropboxusercontent.com/u/20022261/reports/edge_detection.html
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