in image processing, programming, Python / with 3 comments /
It's not possible to protect an image against any person, but it can be protected enough for most of the users. There are a couple of ways to do this, most popular are CSS tricks, but today I will present another way.
Can you save below image? I bet you can't! ;-)
in electronic engineering, high voltage, image processing, parapsychology / with 2 comments /
Kirlian photography is an interesting photographic technique of capturing corona discharge of objects. The images basically contain only edges in a form of blue glow. Note: one of those photos is visible in "The X files" intro - read the whole article to know why!
In this post I will present my minimalist approach with common materials and without complicated construction. The results aren't that good as with more complex setups, but I think that they are still really interesting.
in image processing, programming, Python / with 12 comments /
Usually CAPTCHAs are analyzed by using neural network, it's a good approach, but it may be overcomplicated in simple cases. Presented below, much shorter algorithm can produce sufficient results for uncomplicated CAPTCHAs.
In this algorithm an image with unknown letter is compared with samples of known letters, the letter in the most similar sample is probably also the letter in analyzed image. It was implemented as a Python script, usage presented below:
bash-3.2$ python cracker.py test1.png e
bash-3.2$ python cracker.py test2.png p
It can't be directly used on a raw CAPTCHA, firstly small artifacts have to be removed from the CAPTCHA, secondly each letter should be stored in a separate image.
in image processing, Java, programming / with 26 comments /
In this post I will present an algorithm for searching edges in images by using genetic approach.
The image is preprocessed before its usage, firstly Gaussian blur is used to remove small artifacts that could pollute the result. I could be also done by using image downsizing and upsizing as I presented in the post about breaking captcha. Secondly the image is converted to black and white version to simplify the implementation.
Original image:
Edges found by using a genetic algorithm (100 generations):
In presented version each children has only one parent, so gene change is provided only by mutation. Each specimen has two genes: X and Y that describes a location on the image. If a specimen has a good set of genes (they point to a location near the edge), then he's healthy and strong so he can breed, but if his genes are weak, then he's weak too and soon he dies. The edge is defined by a fitness function as a point that has neighbor points with different color.
Strong species breed so after couple of generations theirs children will cover the whole edge. The final result is a set of genes (each is a point on the image) in the last generation.
in image processing, programming, Python / with 1 comment /
One of important steps in super resolution algorithms is the estimation of initial constants, without this the algorithms will produce useless higher resolution images.
From mathematical view, we may say that during taking a photo, the original scenery is gathered in high resolution, with some movement between each image (e.g. shaking hand or satellite moving on its orbit), next psf and the noise is added, at the end the image is downscaled.
To obtain higher resolution we need to somehow revert this process, to do that we need to estimate mentioned movements. In this post I will present two simple algorithms that I used to estimate the movement between images.
in image processing, programming, Python / with 13 comments /
At first glance, there's nothing special on below image..
..but in fact, on this picture is hidden another one:
It was done, by using steganography, the second image was stored on two lowest bits of the pixels. On two bits we may save values from 0 to 7, pixel in the image may have values from 0 to 255, so such small change is not visible to the human eye.
in C and C plus plus, image processing, PHP, programming, smart code / with 1 comment /
Recently I presented a simple way to protect an image against saving, today I will show how PHP interpreter protect some of its images against changing.
Image may be hardcoded in a regular array, where each field contains corresponding value of a byte in the original image file. You may imagine that you open image in a hex editor, you see its content as a chain of values of the bytes, and then you retype them as a fields in your array (this can be automatized of course).
in image processing, programming, Python / with 9 comments /
Super-resolution algorithms is a family of algorithms used to obtain higher resolution image from set of images in lower resolutions. It's important to notice that by using those methods the higher resolution image is more detailed in opposition to resizing and debluring.
It may be applied on many different data, including spying and meteorological satellites, telescopes, tomographs and X-ray machines. Super-resolution algorithms can be also applied to 3D sceneries or sounds.
In this article, I will describe a super resolution algorithm proposed by Irani and Peleg and its simple implementation in Python.
One of low resolution images (for easier comparison it's resized to fit the estimated image):
The image estimated by using super resolution algorithm:
in image processing, programming, Python / with 12 comments /
Almost all sites use images with text that user has to retype to prove that he's a human not spambots. A lot of those images (called captcha) contains small lines, dots and other noises to make theirs analyze more difficult for spambots. In this post I will present how to easily remove this noise from a captcha.
I used Python and its PIL library for processing captchas. The first step is to transform image to grey-scale (this makes further work easier) and blur it (it makes small objects less visible). PIL's blur filter is a bit poor for that, but SMOTH filter works great (but it needs to be applied twice made it blurred enough). Next step is to check all pixels if their value is higher that some certain constant, if yes, then pixels will become white, if not they will be black. This constant may be set from command line.
in electronic engineering, image processing / with 2 comments /
Not many people know that regular camera can be used as night vision device because it's able to take pictures in infra-red spectrum. If scenery will be lighted by source of those rays, for human eyes it will be dark, but camera will be able to take pictures.
Sensitivity to IR rays depends on many factors, from manufacturer, model. I tried with poor results SONY and Kodak cameras, but cheap mp4-player worked great. IMO it's because better equipment comes with infrared blockers.
The easiest source of infra red rays I could find was pilot remote control, but it could be also made with a couple of inexpensive IR diodes. Here are some pictures (original and computer treatment).
