Google Summer of Code 2009 - Official Link Thread
Posted: 19 March 2009 03:19 AM   [ Ignore ]
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NUI Group GSoC Organization Page: http://socghop.appspot.com/org/show/google/gsoc2009/nuigroup

Ideas Wiki: http://wiki.nuigroup.com/Google_Summer_of_Code_2009

GSoC Melange: http://socghop.appspot.com/

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Posted: 20 March 2009 09:02 PM   [ Ignore ]   [ # 1 ]
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Hi,

I want to apply for working with NUI for the GSoC. I read the proposals and have got a few ideas for the T-Beta list:

Finger Detection Improvements (hand detection?)
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Improve finger detection using Meanshift for detecting “peaks”, read [1] for a great Meanshift explanation. This would work great for DSI, and DI where the pams of the hands detection are a side effect and currently this can be an issue because just thresholding is not always the ideal way to do things. This kind of finger detection can be used in conjunction with current contour detection methods to greatly improve accuracy and to separate fingers (gaussian peaks), non-fingers and hand palms (mid level regions non-peaks). Fingers can be seen as peaks in a 3D surface like the ones displayed here (in spanish, Figura A) [3]. This could be a basis for palm detection.

Finger Orientation
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There are two approaches to explore, a rather simplistic one is contour analysis over the already detected blobs, other idea is to apply Continuously Adaptive Mean Shift [2]. Camshift works on probability maps represented as grayscale images, and luckily the finger detection can be seen as a probability map because the finger is more likely to be where more pixels are, and where whiter pixels are. Thus the finger tracking can be seen as a probability map and the Continuously Adaptive Mean Shift created by Gary Bradsky can be applied. As a Bonus by using Continuously Adaptive Mean Shift we get an estimated window of where the fingers are, and then we can restrict detection areas to those areas and by boosting probabilities in such areas we could theoretically get improved finger tracking. Read [4] and scroll down for an animated explanation (girl painting) of how Camshift works and how it gets orientation.

I would like to propose myself to work on any of these areas, the first one is the one i think has more potential, as it can be the start point for true hand detection, it also it the one that requires more work. And additionally i can do the ROI selection stuff as this is not really a difficult task.

My background: I studied physics and math, masters in computer science (thesis in 3D monocular object tracking), and currently i am in a PhD in computer science my work is on markerless 3D human hand posture tracking.

[1] http://www.wisdom.weizmann.ac.il/~deniss/vision_spring04/files/mean_shift/mean_shift.ppt
[2] ftp://download.intel.com/technology/itj/q21998/pdf/camshift.pdf
[3] http://www.cimat.mx/~alram/VC/MSAA.htm
[4] http://www.robot.jussieu.fr/?op=view_profil&id=32&lang=fr

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Posted: 16 December 2009 10:08 PM   [ Ignore ]   [ # 2 ]
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