Nao segmentation - Revision history

Moment92: /* Pixel classification */

2013-06-02T10:18:18Z

Pixel classification

← Older revision		Revision as of 13:18, 2 June 2013
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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 enough. The lookup table format is described [http://www.cs.cmu.edu/~jbruce/cmvision/papers/JBThesis00.pdf here] on pages 15-16.		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.<br>

			When using lookup tables, it is crucial to stop camera's from being too "smart". Things like [http://en.wikipedia.org/wiki/Color_balance white balance] and [http://en.wikipedia.org/wiki/Exposure_%28photography%29#Automatic_exposure auto-exposure] might make the picture look more natural, but it will also make lookup tables harder to use. That's why we turned them off with [http://www.aldebaran-robotics.com/documentation/naoqi/vision/alvideodevice-api.html#ALVideoDeviceProxy::setCameraParameter__ssCR.iCR.iCR ALVideoDeviceProxy::setCameraParameter], specifically we set kCameraAutoWhiteBalanceID and kCameraAutoExpositionID to zero.

	== Blob formation ==		== Blob formation ==

Siims at 16:19, 22 May 2013

2013-05-22T16:19:23Z

← Older revision		Revision as of 19:19, 22 May 2013
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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 ~~enaugh~~. The lookup table format is described [http://www.cs.cmu.edu/~jbruce/cmvision/papers/JBThesis00.pdf here] on pages 15-16.		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 ~~the~~ pixels, blobs are formed from ~~the~~ 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.		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.

Moment92: Created page with "== 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 fo..."

2013-05-22T16:12:35Z

Created page with "== 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 fo..."

New page

== 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 enaugh. The lookup table format is described [http://www.cs.cmu.edu/~jbruce/cmvision/papers/JBThesis00.pdf here] on pages 15-16.

== Blob formation ==
After we have classified the pixels, blobs are formed from the 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 ==
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