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GEORGIA TECH GAUSSIAN SOFTWARE HOW TO
GEORGIA TECH GAUSSIAN SOFTWARE SERIES

One is a dollar value and one is simply a count.

GEORGIA TECH GAUSSIAN SOFTWARE HOW TO

One example might be how to compare sales to number of customers. Similar to how two waves in sync amplify each other the area that matches will amplify the result. What this does is score the two series, with possibly very different range of values, according to a similar metric. We take the filter and apply it to the original signal to produce a nomalized cross-correlation. Then we scale the image as well to the same standard deviation before applying our filter to produce a normalized correlation filter.Ĭonsider a 1-dimensional signal, which is similar to a time series, and a filter that is a subset of the signal.

In particular we will examine Normalized Correlation.įirst, we're going to normalize our filter to have a standard deviation = 1.

Previously our discussion about images as functions had the output as intensity, now we can construct properties as well. Recall that in previous discussion we looked at gaussian and salt and pepper noise. Here we have a simple impulse less a blurring (mask) to produce a sharpening filter. Putting this all together has some interesting results as you can see in the following example.

Wrap around - we think of the image as circular.

You may have also noticed that throughout our discussion our filters ignore the edges of an image. The reason is that multiplying by a row vector and a column vector is less intensive than using the H matrix. This was more useful in the past when computers were not as fast. In some cases, filter is seperable, meaning you can get the square kernal H by convolving a single column vector by some row vector. Operator behaves the same everywhere, ie the value of the output depends on the pattern in the image neighborhood, not the position of the neighborhood. But for more exotic filters this is not true. Also note that for a symmetric filter the correlation = convolution. Note that because correlation and convolution are built using multiplication and addition they remain linear operators. There's a very slight difference: Correlation uses addition and Convolution uses subtraction. Another theory is that matrices are indexed in a similar fashion from the top left ($e_ H F$ ie a page in book would be read from top-left to bottom-right. One theory is that this is similar to how people read. The reason why Cathode rays operated this way appears to be lost to history.

GEORGIA TECH GAUSSIAN SOFTWARE SERIES

The earliest TVs (aka CRTs) used cathode rays which displayed images by scanning a beam of electrons from Top-left to bottom-right as a series of horizontal lines. It turns out that computers inherited this from old Television technology.

You may wondering why computers think of the top left corner as (0,0) rather than the bottom left which is used in all mathematics. Normally M(i,j) = ith row, jth column = M(y,x) This can cause confusion as a matrix element (x,y) represents (col,row) contrary to how we think/write. Rows go down and are represented by y, cols go across and are represented by x. Remember than when processing an image as an array. Real images often don't fit an algorithm or mathematical model very cleanly. CV leans towardsĬomputer Vision lies somewhere inside a triangle composed of points representing Math, Algorithms, and real images. What's the difference vs CP-Computational PhotographyĬomputer Photography is about mostly about scene composition where as Computer Vision(CV) is about identifying the scene and the elements in it.