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Particle Swarm Optimization for Picture Analysis
Roland Piquepaille writes "Particle swarm optimization (PSO) is a computer algorithm based on a mathematical model of the social interactions of swarms which was first described in 1995. Now, researchers in the UK and Jordan have carried this swarm approach to photography to 'intelligently boost contrast and detail in an image without distorting the underlying features.' This looks like a clever concept even if I haven't seen any results. The researchers have developed an iterative process where a swarm of images are created by a computer. These images are 'graded relative to each other, the fittest end up at the front of the swarm until a single individual that is the most effectively enhanced.'"
I love an article on digital imaging technology that has no pictures. This is 2008. Send out your press release with a photo...of something...anything.
Careful What You Wish For....
with PSO, ant colony optimization, genetic algorithms, etc. is that they take tons of computational effort, and typically work no better than (or significantly worse than) much more efficient direct optimization methods. Wake me up if they show good results (esp. that didn't take a year of computer time to construct).
P.S. IAAAIR (I am an AI researcher, albeit not in computer vision)
For more detail, including the citation of the paper, see this http://www.primidi.com/2008/02/03.html
The question of whether a computer can think is no more interesting than the question of whether a submarine can swim.
Unless I am REALLY missing something, it is next to impossible to go from a blurry distorted image to pin-sharp. Really close to impossible. It is a matter of data. If you start from blurry, you cannot actually obtain the information required to unblur it. It does not exist. Therefore, any results are fundamentally speculative. Contrast Levels are not exactly the same thing, since you are only shifting data already there. Edge enhancement, sharpness, is not actually representative of what the objects actually looked like. There is a big difference between taking a blurry box and enhancing the edges and taking somebodies face and effectively "refocusing" the image so you can see facial features more clearly. You could say this is a step closer and certainly novel approach to the problem. To actually get to science fiction levels of performance may be not actually be possible though.
Not really useful at all. At least from an evidence point of view. Since you cannot really be sure if that is the individual in the picture, the best you can approximate is closer to one of those sketches they provide. I'm not being racist, but certain races do look similar. If you took 100 Chinese people for example, and started progressively blurring their pictures, you would start to get pictures that you could not make a distinction between them, much less a definitive identification. So there had better be some corroborating evidence, since it won't take too much of an expert witness to shoot that down. So it would be better to say it could help identify possible suspects, not individuals. Burden of proof, reasonable doubt, and so on.
Another thought, even more concerning, is that if you took those 100 pictures and showed them to a test group that saw before and after shots for each individual, how effectively could they make identifications? What about a test group showed only the after shots? My point being, is that if you are predisposed towards identifying a certain individual you are more likely to do so. In fact, people remember faces in a similar way be exaggerating facial features. I believe it is referred to as face perception. So it might be possible for the human brain to identify, incorrectly, an individual from one of those blurred images. All in all, not solid enough for legal purposes, which CCTV identifications of individuals and license plates are certainly used for.
I could be wrong, but until I see actual pictures, I will have to play the part of the skeptic.
Great idea, and certainly thinking outside of the box, so they deserve respect for their work.
it has to know *specifically* what makes that picture better. Why not use that knowledge to jump to the best picture (that it can define) from the first picture?
Because the algorithm doesn't have that kind of knowledge. In AI-based search we don't know how to define absolute functions of quality, but we know how to define (several) relative dimensions of improvement. (Disclaimer - I do this for a living).
Intelligent search is based on iteratively improving one of those dimensions, just a little bit, one at a time. This goes on until we find a solution that is as good as we can get in all dimensions at once; but we simply don't know how to combine all dimensions to create a formula that maximizes all them, because their relative improvements interact with each other in complex, chaotic ways.
Singularity: a belief in the "God" idea with the "demiurge" relation inverted.