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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)
Um... if the computer knew how to tell a good picture from a bad, couldn't it have just created a good picture in the first place? This all seems rather useless/confusing to me.
Just -1, Troll talking to another.
I've seen what Photoshop CS3's auto levels function does to some photos. It gets it right most of the time and when there needs to be little adjustment, it makes a little one and for really bad ones, it makes big adjustments. You could say it's judging the quality of the input image. Well it's right about 75% of the time. When it usually gets confused is when a picture is supposed to look significantly reg, green, or blue and it has no way of knowing that so it screws it up horribly while trying to tone it down. So I'm figuring any automated system won't possibly be remotely as smart as a human when it comes to sorting the "best" photos out front. It will probably just have a huge bias towards certain types of images.
Google's Super Secret Search Algorithm: SELECT @search_results FROM internet WHERE @search_results = 'good'
This procedure sounds like it has the same problem as plain-old AI search - the lack of an obvious heuristic. The article says they use the number of pixels on an edge, but there's no obvious way of finding this - they've moved the computation up one step. The article is light on details so I'm sceptical. If they have a simple procedure for the fitness function, this is a great application.
Three rights make a left. Freedom of speech, freedom of the press, freedom of assembly.
This looks like a clever concept even if I haven't seen any results.
Hell, this needs no comment, it's funny on its own. Mod TFB +1, accidently funny.
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.
They've reinvented genetic algorithms ?
Without seeing the details (read TFA but it's a summary and quite a bad one at that), I can't see why this would be better than a Bayesian optimisation with a photometric constraint. "The objective of the algorithm is to maximize the total number of pixels in the edges" sounds very, very simplified.
There are efficient ways of solving these things. Interesting that they invent an image processing algorithm but publish it in a non image processing journal - I wonder why that is ?
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.
A first pass analysis certainly reveals some elements of Metropolis-Hastings may have been folded in but they do not comprise the entirety of the final solution which seems instead to be bulked up by a n'th pass reverse locality filter feeding off a more traditionally schwelpian treatment of the core triplets. Interestingly every fifth haynes cosignatory node seems to be commulated back to it's quatenary closest fit counterpart.