Nao segmentation: Difference between revisions
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== Pixel classification == | == Pixel classification == | ||
We use a lookup table to classify pixels into colour classes. We have a special program that let's us easily construct new lookup tables and this method has worked well | We use a lookup table to classify pixels into colour classes. We have a special program that let's us easily construct new lookup tables and this method has worked well enough. The lookup table format is described [http://www.cs.cmu.edu/~jbruce/cmvision/papers/JBThesis00.pdf here] on pages 15-16. | ||
== Blob formation == | == Blob formation == | ||
After we have classified | After we have classified pixels, blobs are formed from pixels of the same colour class. Blob formation is done using the CMVision algorithms with slight modifications to work better with our own data structures. The algorithms are described in detail [http://www.cs.cmu.edu/~jbruce/cmvision/papers/JBThesis00.pdf here] on pages 16-19. | ||
== End result == | == End result == | ||
Applying those techiques on a frame gives us an array that consists of blob lists. In every blob list we have blobs that belong to the same colour class. They are also sorted by their area, so that it's easy to find the largest blobs of some specific colour | Applying those techiques on a frame gives us an array that consists of blob lists. In every blob list we have blobs that belong to the same colour class. They are also sorted by their area, so that it's easy to find the largest blobs of some specific colour. | ||
Revision as of 18:19, 22 May 2013
Overview
We need to use some criteria to detect real-world objects. The easiest method to distinguish different objects is to use their colour. As many objects on the football field are colour-coded, it is reasonable to use it, as it is also computationally very effective. It means that we claasify every pixel into a colour class and after this is done, we form the blobs of the similar pixels.
Pixel classification
We use a lookup table to classify pixels into colour classes. We have a special program that let's us easily construct new lookup tables and this method has worked well enough. The lookup table format is described here on pages 15-16.
Blob formation
After we have classified pixels, blobs are formed from pixels of the same colour class. Blob formation is done using the CMVision algorithms with slight modifications to work better with our own data structures. The algorithms are described in detail here on pages 16-19.
End result
Applying those techiques on a frame gives us an array that consists of blob lists. In every blob list we have blobs that belong to the same colour class. They are also sorted by their area, so that it's easy to find the largest blobs of some specific colour.