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This is just a confirmation. Are barcodes symmetric.
By symmetric I mean, if I rotate the barcode by 180 degrees. Will it have the same data decoded.
Basically does angle matter while scanning barcode? And are there any exceptions in the type of barcode.
No, the barcodes themselves are generally not symmetrical (I'll clarify that - you may be able to find one that is symmetrical but the vast majority of the standard ones are not).
However, any decent reader (such as the ones at your local supermarket) will scan in a large number of different directions to take care of this, not just backwards and forwards but at other angles as well. So you can generally rotate them to your heart's content.
Even the ones that scan in a line (such as some hand-held units) may scan both directions - it depends on what you've paid for. Of course, if you have a "Dodgy Brothers" brand reader, you'll probably find it won't do that.
Some barcode standards will allow for detecting upside down barcodes. For example UPCA swaps black and white on the right hand side so that readers can adjust for it.
Usually there is a start code and a check code in each bar code so the angle doesn't matter.
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In the past few days i was thinking of a solution for an AI problem. The problem sounds like this:
I want to determine an arrangement for a few given geometric forms(that do not exceed the given board size) on a square board of given size, in such a way that the board will be uniform covered and the forms will
not overlap.
I want to apply Depth first search / Greedy best first search, but it feels difficult to find a proper representation of the forms and the actual board in order to traverse it. I'm new to python so that makes it a bit more difficult. Any suggestions?
Visual example:
What you are describing is a variation on rectangle/square fitting. Versions of the problem exist where unused cells have to be minimised for an optimal placement of the figures, whereas other versions, like the one you are describing, require for the whole board to be covered uniformly. These are called 'perfect square/rectangle placement' problems.
Typical ways to solve these problems involve the usage of finite integer domains representing the variables of the rectangles and a set of constraints making sure the geometrical placements are valid ones (i.e. don't cross the board borders, don't overlap with each other mutually, ..).
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What of this algorithms is output sensitive ? (their base algorithm)
ray tracing
gpu rendering
splatting
How can we make them with acceleration method to be likely output sensitive ?
I think ray tracing and gpu is not output sensitive.
http://en.wikipedia.org/wiki/Output-sensitive_algorithm
For the folks who didn't understand the question, in computer science, an output-sensitive algorithm is an algorithm whose running time depends on the size of the output, instead of or in addition to the size of the input.
Ray Tracing is output sensitive, in fact many ray tracing programs can generate smaller size images or movies in faser time.
GPU rendering is output sensitive, the fact that the GPU can parallelise the task, can speed up, but far less computations are required to render a smaller size image than a bigger one.
Texture splatting, is also output sensitive, since typically textures are repeated, so you can generate a huge image joining many of them, thus requiring more cpu power (and memory).
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Does any have an algorithm for creating infinite terrain/landscape/surface?
Constraints
The algorithm should start by a random seed
The algorithm should be one to one, (the same seed gives the same result)
Other input parameter are allowed as long as 2 is fulfilled
The algorithm may output a 2d map
It suppose to create only surface with varying height (mountains), not three, ocean etc.
I’m looking for an algorithm and not a software.
It should be fast
None of other related questions in here answers this question.
If anything is unclear please let me know!
I would suggest something like Perlin noise, I've used it before for something like you're describing above, and it fits the bill. Check out this Example and you can see the sort of output you would expect from the noise generator.Here is a link to algorithm p-code too.
http://freespace.virgin.net/hugo.elias/models/m_perlin.htm
As others already said perlin noise is a possibility. Gpugems 3 has a nice capter about procedual generation using (IIRC, it has been some time since I read this) 3D Perlin noise.
Of course there are other methods too, e.g. Vterrain.org might be worth a look.
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I have always thought the way they zoom in and enhance on TV and movies was strictly impossible. Essentially because you cannot create more information that there is to begin with.
There were ways to get better looking or clearer images like with re-sampling, but never to the extent seen on film.
Now, it seems that is not true.
I was reading this article, and it seems they have a way to do that now?
Or, is this just a better version of what was already possible? You still need to have a fairly clear image to start with? Otherwise, what are the limits of this technique?
There is something called Super-resolution. Some companies claim to use fractal theory to enhance images when they are upscaled. But, what you see in most movies is just fiction.
Image enhancement always involves pixel interpolation (aka. prediction) - in one way or the other. Interpolation can be good, bad or whatever, but it will never out-perform real pixel which was recorded by imaging device at greater resolution.
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Does any one have an idea regarding what sort of algorithm might Google be using to find similar images ?
No, but they could be using SIFT.
I'm not sure this has much to do with image processing. When I ask for "similar images" of the Eiffel tower, I get a bunch of photos of Paris Hilton, and street maps from Paris. Curiously, all of these images have the word "Paris" in the file name.
Currently the Google Image Search provides these filtering options:
Image size
Face detection
Continuous-tone ("Photo") vs. Smooth shading ("Clipart") vs. bitonal("Line drawing")
Color histogram
These options can be seen in its Image Search Result page.
I don't know about faces, but see at least:
http://www.incm.cnrs-mrs.fr/LaurentPerrinet/Publications/Perrinet08spie
Compare two images the python/linux way
I have heard, that one should use this when comparing images
(I mean: make the prob model, calc. the probs, use this):
http://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence
Or then it might even be one of those PCFG things that MIT people tend to use with robotics stuff. One I read used some sort of PCFG model made of basic shapes (that you can rotate magically) and searched the best match with
http://en.wikipedia.org/wiki/Inside-outside_algorithm