Many spatially distributed data exhibit anisotropic spatial variation, especially when the data are distributed over a large area. The Semivariogram, or commonly (and inaccurately) just variogram is a measure of spatial correlation. It simply plots the semivariance (which is half the variance) of two points separated by a vector h against the magnitude of h. Easy right? Well there is a little bit more to it...

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The method of least squares, or even simply maximum likelihood is one of the more powerful tools available to a statistician. It is powerful because its simplicity means it can be used in a variety of regression problems.

Regression simply means line fitting, and lines are just a graphical way to represent a model, which is the mathematical way to describe the relationship between an independent variable and one or more dependent variables. There is a lot of text on linear straight line fitting, so I’m not going to go into too much detail. I will however briefly discuss the principle behind least squares.

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In order to analyse the frequency components of a curve using Fourier we must first represent it mathematically. As we are working with images, a discrete spatial domain, then we have to deal with discretisation.

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Sobel edge detection is a popular technique as it can deliver good results, without excessive computational requirements. The technique utilises the theory of optimal smoothing (Gaussian), and optimal differencing.

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The Hough Transform (HT) is an image processing operation which enables the extraction of shapes, essentially lines from images. The principle is that there are an infinite number of line that pass though any point, each at a different orientation. The HT aims to determine which of those theoretical lines pass though most features in an image.

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Now that I have background monitor working, updating a background image every 60 seconds (a default value), I want to start monitoring the foreground. For these two processes to work at the same time I want to use threads for each one, using the threading module in Python. read more...