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Filtering in Fourier Space One good example of a repetitive patterns usually seen as part of the object is a very high resolution image of a painting on canvas.  This high-resolution image would show the weave of the canvas itself already.  This can easily be removed by masking their frequencies in frequency domain thus effectively removing its existence in the newly filtered image.  In turn, you can also enhance specific frequencies as well. Convolution theorem Fourier transforms of different symmetric shapes are shown below Two dots The result of taking the FT of two dots symmetric in the x-axis (of image) gives an alternating black and white grating (fringes).  The dots are now changed into circles with varying radius.. 2 dots: Aperture and FT 2 Circles of varying radius For symmetric circles with varying radii, the trend noticed was that increasing the radii of the circles result in the decreasing of the radii of the centra...

We (and  I mean WE) usually go out at nights to have fun.. One of the ways to remember the day is by taking a picture.  Imagine taking a LOT of pictures and it turns out to be VERY DARK.  What to do? What you DO NOT do is delete the image immediately.  You can basically modify the image so that the information (details, namely faces, or even the background). Histogram Manipulation (using Scilab) Now, what we are trying to do is enhance the image (manually or through coding), like what cameras and image editing software can do.. So first of all, you get the normalized histogram of the grayscale image of your choice.  For example, the histogram is the PDF (As shown in the figure below.  Now we get the Cumulative distribution function (CDF) of the PDF.  You can wasily do this by using the cumsum() function in Scilab where it adds up the values cumulatively.  The corresponding CDF of the PDF is also shown below. PDF and CDF of a grayscal...

Usually from movies (specifically action movies involving satellite images), one would wonder, how were they able to know how large the base of the terrorist just by looking at the image, without having any surveillance at that place. [Sorry, I'm a big fan of Chuck where satellite images are always shown.. :P]  From this activity, it will show how you can actually measure the area of a specific image (from map, etc), and how accurate it is. There are two ways to know the area, that is by pixel counting or Green's Theorem, then multiply the appropriate scale. Pixel Counting The easier way is the pixel counting where you basically convert your image into black and white, and then count how many pixels are black or white (depending on which color is the area you wish to know).  This conversion to black and white image was already done in the previous activity (Activity 3) so you may refer to that for the code used.  Additional lines were ran to be ...