ibuildthings

http://bluebrain.epfl.ch/cms/lang/en/pid/56882 Here are more details of the European project (labelled Blue Brain Project) other posters had mentioned about in this thread. Also, there is this TED talk http://www.ted.com/talks/henry_markram_supercomputing_the_br... by Henry Markram, the project director of the blue brain project.

Though I agree to most of what you said, for learning algorithms you need not think about low level memory management.

I had used CLRS many times to explain algorithms to non-CS students and collaborators ( who almost always preferred dynamically typed scripting languages like python and to be honest were scared of ints and floats ). At the same-time some of them were very good in algorithm design, and came up with clever (algorithmic) ways to solve the problem at hand much more efficiently.

Yes, this is a very good idea, and a very simple constraint to incorporate. I will include this.

Yes, in most cases the algorithm will ensure that the face is included. However, there are a few instances ( for example, if the face is blurred, or is in a weird pose ) when it will miss it.

A more elaborate talk about this work by one of the authors appears at: http://www.youtube.com/watch?v=aKu20y1f_RU

New for the sake of "new" doesn't make much sense to me. Quoting Einstein "It can scarcely be denied that the supreme goal of all theory is to make the irreducible basic elements as simple and as few as possible without having to surrender the adequate representation of a single datum of experience." If indeed Higgs boson is spotted, it will be indeed a exemplary triumph of this thought, since the core premise of standard model is symmetry and simplicity.

I consider LHC as the best microscope we humans ever build. Our exploratory quest and adventure will remain ever more interesting and baffling when we start to probe in higher energy levels. This will be irrespective of whether the presence of Higgs boson is established or not. And there is no reason to think otherwise, since in the past the deeper and in finer scales we probed, strange phenomena had popped out, requiring more new theorems and scientific understanding to explain them.

One of the tricky thrones in this method is extracting/guessing the camera motion path purely from image measurements. Better the estimate, the better the deblur kernel will be. What might be cool is if they can extract out meta-information using some kind of inertial and gyroscope-akin sensors (which are fast becoming standard in phones and cameras), which can supplement the motion path computation algorithm.

Actually human eye/brain is much more forgiving to motion blur in video that in still images. The chances are that one will spot quite a number of artifacts [compression as well as blur] in a grabbed frame of an HD quality video, which is otherwise unacceptable in a photograph.

In theory, you can tweak this method such that it is run in parallel frames 'almost' independently. I say 'almost' since you will have to factor in temporal coherence, such that deconvolution kernels do not vary much between subsequent frames (otherwise it will cause unwarranted flicker and jitter like effects)

However, more pertinent and related problem with respect to video is that of image stabilization. And computer vision community is making some exciting strides in the front; for eg: http://web.cecs.pdx.edu/~fliu/ .