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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....
tfa doesn't actually show that this does anything, so all we have is AI people making big promises. Am I missing something out of the ordinary here?
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.
Is this something like an advanced version of the Metropolis-Hastings algorithm? I could certainly imagine Monte Carlo methods being employed in these types of analysis procedures.
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.
"It will probably just have a huge bias towards certain types of images."
So my handsome mug will look good in Photoshop?
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.
I'm currently googling for pics, but nothing comes up except for similarly-worded pages. Please post URL (via Coral) if you find one.
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.
Tantalizing - but not enough to go on, so it is pretty much useless. I found the abstract here but it does little to elucidate the article.
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 ?
"Interesting that they invent an image processing algorithm but publish it in a non image processing journal - I wonder why that is ?"
Well they could've published it on Wikipedia. And we both know how that'd be a boost.
Erm, anyone have a link to anything that's actually worth reading, not a short press release? You know, maybe with some PICTURES of their image processing...
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.
This looks like a clever article alright, even if I haven't bothered reading it.
Totally makes me think of Craig Reynolds's "boids" -- take a look:
http://en.wikipedia.org/wiki/Boids
What's really cool is that boids force you to re-think how you define intelligence, well, at least collective intelligence. It's like watching ants at work. Love it.
How to Download YouTube Videos
Swarm intelligence is what I research. PSO is not really an algorithm, it is a metaheuristic. Of course when I talk with non-engineers I might also use the terms algorithm or recipe, but I would expect correct terminology on a site whose readership contains a large percentage of CS/EE degree holders.
Looks like it works the same way as the old idea from Photogenetics. This time it looks like it involves less human interaction - which can be good or bad.
well i do get a very big laugh from CSI and there " enhanced photos " 1) start with a very good shot 2) degrade it and say it is the orig. 3) show the true orig. image as the "enhanced" one i still like greycstoration , pde_TschumperleDeriche2D , and pde_heatflow2D
"I don't pitch OpenSUSE Linux to my friends, i let Microsoft do it for me
The (nonlinear) threshold setting on a digital unsharp mask algorithm cause my high pass filter analog to break down, but otherwise it's valid. So ignore the threshold, for a moment, in the unsharp mask. The implementation of the unsharp mask is in the spatial domain as you said, but (without the threshold) it has a dual in the frequency domain. The unsharp mask uses a convolution of the image with a Gaussian for blurring, followed by linear additions and subtractions. Convolution, addition and subtract all have duals in the frequency domain.
The Convolution Theorem states that convolution in one domain (e.g., spatial domain) equals point-wise multiplication in the other domain (e.g., frequency domain). Taking the Fourier transform of a Gaussian function yields another Gaussian function. The frequency domain Gaussian is a low pass filter. The subtraction takes an all pass filter (the original image) and subtracts the low pass filter (the blurred) resulting in a high pass filter. The high pass filter is then added back to the original, making the transfer function of the unsharp mask = 1+a*High PassFilter(f), where a = amount, and the High Pass Filter(f) = 1 - Low Pass Filter(f).
Unsharp masking isn't really the right algorithm to compare deconvolution techniques to. They are apples and oranges in their implementation, strengths and weakness, and mathematical foundations, but like apples and oranges, they are both used for the same purpose. Wikipedia does a fine job comparing them.Deconvolution, on the other hand, is a direct high pass filter.
The Refocus program I was referring to uses a FIR filter. If by "direct" you mean implemented in the frequency domain then you won't agree that Refocus uses deconvolution.
"A FIR Wiener filter only uses a limited neighborhood of the source pixels and can be easily implemented as a convolution matrix." FIR Wiener filtering has the following advantages:
- Low memory requirements. Only the convolution matrix must be stored.
- Ease of implementation. There is no need to do a full Fourier transform.
- The transformation is local. The results only depend on a small neighborhood of the original pixel.
The Unsharp Mask and the FIR Wiener filter have all these properties in common."Follow me" the wise man said, but he walked behind.
circa 2006, a single pixel camera prototype using what sounds like a similar method was being developed and subsequently covered by slashdot here http://science.slashdot.org/article.pl?sid=06/10/19/2255239&from=rss. did it turn out that 1 pixel really wasn't enough and what we really need is brute computational force instead?
It is a picture of Uranus, not your anus! Good lord, are people moderating without reading now?
Btw, Uranus made me wet my pants! (from laughing)
We've optimized Uranus!
Forget diamonds, copyright is forever.
From TFA it sounds more like an evolutionary algorithm than anything to do with swarms. It said the word swarm over and over but didn't actually describe anything to do with them...instead it talked about how to solve the traveling salesman problem.
but have you considered the following argument: shut up.
Just a note.. If you want to read some fun swarm-centric sci-fi, pick up Crichton's "Prey", where he writes of simple one pixel cameras injected into the bloodstream, then swarm together to form an eye which acts as a miniature video camera. Among other things, he also writes of how humans are swarms themselves, consisting of tiny little dumb cells that work together to form a supposedly intelligent life-form.
Pics or it didn't happen.
"This looks like a clever concept even if I haven't seen any results"
That's what she said!