How do I find Waldo with Mathematica?

I've found Waldo! How I've done it First, I'm filtering out all colours that aren't red waldo = Import"

"; red = FoldImageSubtract, #1, Rest# &@ColorSeparatewaldo; Next, I'm calculating the correlation of this image with a simple black and white pattern to find the red and white transitions in the shirt. Corr = ImageCorrelatered, Image@JoinConstantArray1, {2, 4}, ConstantArray0, {2, 4}, NormalizedSquaredEuclideanDistance; I use Binarize to pick out the pixels in the image with a sufficiently high correlation and draw white circle around them to emphasize them using Dilation pos = DilationColorNegateBinarizecorr, .12, DiskMatrix30; I had to play around a little with the level.

If the level is too high, too many false positives are picked out. Finally I'm combining this result with the original image to get the result above found = ImageMultiplywaldo, ImageAddColorConvertpos, "GrayLevel", .5.

54 +1 This is just pure amazingness. – Mike Bantegui Dec 12 at 19:45 12 @MikeBantegui While Heike's solution is great, I wouldn't be so quick to package it into a WhereIsWaldo function, as it is not a general solution. Heike herself has pointed out that the levels need to be played around with before you can get a positive.To see what I mean, try your packaged function as is on "

" It's harder with this one.

– yoda Dec 12 at 19:57 12 This image is trickier: Waldo. I think though, that having something that can highlight potential Waldos is still useful (for some definition of 'useful'.) (This reminds me of some of the things iPhoto will sometimes identify as a face in our photo collection...) – Brett Champion Dec 12 at 20:21 11 Please see this Meta post: meta.stackoverflow. Com/questions/116401/… – Bill the Lizard?

Yesterday 38 You seem to have misunderstood the rules of Where's Waldo. This is clearly cheating. – Stefan Kendall 22 hours ago.

My guess at a "bulletproof way to do this" (think CIA finding Waldo in any satellite image any time, not just a single image without competing elements, like striped shirts)... I would train a Boltzmann machine on many images of Waldo - all variations of him sitting, standing, occluded etc; shirt, hat, camera, and all the works. You don't need a large corpus of Waldos (maybe 3-5 will be enough), but the more the better. This will assign clouds of probabilities to various elements occurring in whatever the correct arrangement, and then establish (via segmentation) what an average object size is, fragment the source image into cells of objects which most resemble individual people (considering possible occlusions and pose changes), but since Waldo pictures usually include a LOT of people at about the same scale, this should be a very easy task, then feed these segments of the pre-trained Boltzmann machine.It will give you probability of each one being Waldo.

Take one with the highest probability. This is how OCR, ZIP code readers, and strokeless handwriting recognition work today. Basically you know the answer is there, you know more or less what it should look like, and everything else may have common elements, but is definitely "not it", so you don't bother with the "not it"s, you just look of the likelihood of "it" among all possible "it"s you've seen before" (in ZIP codes for example, you'd train BM for just 1s, just 2s, just 3s, etc, then feed each digit to each machine, and pick one that has most confidence).

This works a lot better than a single neural network learning features of all numbers.

Besides, the wikipedia article claims that Boltzmann machines are not practical. – GClaramunt 8 hours ago.

I cant really gove you an answer,but what I can give you is a way to a solution, that is you have to find the anglde that you relate to or peaks your interest. A good paper is one that people get drawn into because it reaches them ln some way.As for me WW11 to me, I think of the holocaust and the effect it had on the survivors, their families and those who stood by and did nothing until it was too late.