[Kde-accessibility] Lip Reader Demo

[Yash Shah]

Hi Peter,

Though our code is superfast, We can accelerate it even more by using GPU.
OpenCV itself now supports GPU acceleration like OpenCL. Minimal code
changes are required. Main building block of GPU based aplication is GpuMat
class in contrast to Mat class in CPU OpenCV API. We can convert one into
another and mix them in code. We will use it in our project.

http://opencv.willowgarage.com/wiki/OpenCV%20GPU%20FAQ

We can even add one more filter. We can roughly estimate the distance of
the user from the webcam. We will have trained samples with us and
according to that, we can even filter the sound from its loudness. I think
the problem with the background noise will be solved by using Computer
Vision. Surely, there will be exceptions, but it will work perfectly in
general.

I also though about "mmmhhh" while implementing, but it is also kind of
noise to us. We don't have to perform anything with "mmmhhh".

We will be using libKface. Digikam also has large database of users images
and it tags people automatically. So it also will be useful to us.

Regards,
Yash

<http://opencv.willowgarage.com/wiki/OpenCV%20GPU%20FAQ>
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