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9

Hold on to your article until you finish school. Once they don't have the upper hand (of possible suspension or expulsion), go ahead and post it and take all the credit you want.


7

Perceptual hashes may be the answer: http://www.phash.org/ A perceptual hash is a fingerprint of a multimedia file derived from various features from its content. Unlike cryptographic hash functions which rely on the avalanche effect of small changes in input leading to drastic changes in the output, perceptual hashes are "close" to one another if the ...


6

Check dimensions. If different => images are not the same. Check formats. If the same => Perform precise comparison, pixel by pixel. If different formats do this: Do not compare RGB (red,green,blue). Compare Brightness as half the weight and compare color/hue as the other half (or 2/3rds vs 1/3rd). Calculate the difference in values and depending on ...


5

Yes, there are, and no, they don't work very well. Deducing information about the author from a text is sub-discipline of natural language processing - most NLP applications are about extracting information about the content of a text rather than the author, but the goals, methods and state of the art are actually rather similar (currently this favors ...


3

The quality of speech recognition depends on many parameters: Microphone: as you noted, a headset microphone is better than the one in your laptop. Studio microphones will give the best results, I imagine. Environment: you'll have hard time making speech recognition work in a noisy environment compared to a quiet one (ideally a studio). Pronunciation: for ...


3

Well i don't believe the Arduino has the horse power to do this. its operating at 16Mhz An Arduino has about 32K of memory. Even 20 words sampled in Mp3 (smaller then wav) wouldnt fit in it, despite its only your own voice. The rasberi pi might do the trick, its operating at 700Mhz depending on the version it might have 512MB memory. That's still not a lot ...


3

When I was screening a bunch of images for dupes some years ago I found that reducing everything to 8x8 thumbnails and then computing a similarity score based on the square of the distance (treating the three colors separately) between the thumbnails worked pretty well. Note that you can hold a LOT of 8x8 thumbnails in memory. Virtually all dupes scored ...


2

A LONG LONG TIME AGO, I noticed a peculiarity on a section of SOME ESTABLISHMENT's website where all of the ESTABLISHMENT's services are centralized. Upon further inspection, it was a weakness in the site's authentication. For the record, ANY LIKENESS TO REAL EVENTS ARE PURELY COINCIDENTAL. This lead me to investigate what of THE ...


2

Convert the images to another color space like HSV and than just check the S component. S stands for saturation and the saturation should be (nearly) 0 for all grayscale images. Here is the documentation from ImageMagick: http://www.imagemagick.org/script/color.php


2

One way of dealing with this problem is to have a finite state machine with at least three states: not detecting anything detection phase gesture dectected Then you need to carefully design conditions for each state modification (ie going from detection phase back to "not detecting anything" in case of failure) an run them at each frame of your video ...


1

Firstly, please post some images and their corresponding plots from your implementation of Hough transform. Without images and their plots, it is difficult to tell what is going on - especially since there is no source code to critique. My suspicion is that your understanding of Hough transform may not be correct. When the input is a single point in the (x, ...


1

You're looking for Computer Vision: ...a field that includes methods for acquiring, processing, analyzing, and understanding images and, in general, high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the forms of decisions. A theme in the development of this field has been to duplicate the abilities ...


1

Your problem is setting the margin of error too strictly for classifying an image as black and white. The trick is to not go too far the other way and falsely classify color images as black and white. Do some research on Bayes classification. Basically, you do the classification manually for a sample of your images. That will give you the total ...


1

Maybe you should write some code which scans the images for likeness. You could convert all the pics to ARGB format and compare them. (in memory) A possible approach could be this way: Divide the pictures in zones. Scan the zones' average color and/or brightness to compare two pictures for likeness. If more than say, 90% of the zones match, you chose one ...


1

Why don't you post your article, leave it as is, and add a section identifying that security holes will happen and it's the quick thinking and quick reactions of organizations like this school that are required in the software industry to keep security standards up to date and information safe. +1 career +1 school rep


1

Did you sign anything saying you wouldn't talk about it? What can he do if you post it? Honestly it look likes someone wanting to keep a huge mistake quite. Post your blog entry, as long as it's discrete about the school it shouldn't be much of a problem.



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