Shape description by image moments is a popular topic in image processing. Standard geometric moments are based on a non-orthogonal basis, which has introduced some problems for image reconstruction. Orthogonal moments such as Zernike and Legendre Moments which use orthogonal polynomials have been introduced to overcome this problem. These however are based on continuous polynomials, and are not really suited to digital images processing which is inherently rooted in a discrete domain. Hence, a new type of discrete orthogonal moments, based on Tchebichef polynomials has emerged.

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Source Code Management (SCM) is one of those things that computer scientists and geeks love to talk about, and I think I've just worked out why! When you get a new SCM tool working, and the coin drops - it's awesome! It's like the most impressive Hello World ever. Previously I've used Concurrent Versions System (CVS), which is the only time I've actively collaborated on a software project, and when I started my PhD I used Subversion. I'm a follower of fashion when it comes to SCMs, and as you've probably seen, Git has been spotted all over the internet. So I thought I'd investigate, and this is what I've discovered after using it for around 3 months.

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Well, after six months, fourteen thousand and fifty five words, seven thousand three hundred and seventy six lines of code, and many late nights and early mornings, I've finally handed in my mini-thesis. Woo Hoo! read more...

I've recently been battling with an algorithm, or rather bugs in my implementation of an algorithm, to isolate homogeneous regions in an image. Homogeneous regions? By this we mean areas of identical intensity (the pixel values are the same). As humans we can do this easily; but for a computer, it's harder than it might seem at first... read more...

SQL is really very useful. This single query allows me to find the greatest range of a particular variable for every day in my database (which is over 5000 days) read more...

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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Okay, so I need to investigate the effect of covariates on influenza infections across a country. Covariates are basically cofounding variables, that may be predictive of the outcome under study. I'm investigating influenza - so lets look at population density. To analyse such data, we need a system to store and retrieve it; a database.

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Since my standard greedy algorithm works as expected, I made a C++ class from it, using the Cool Image C++ library. This then provides blistering computation of the minimisation process, compared to Python. It does mean I've had to descend into the depths of declaring what sort of variable I want, rather than just its name, but oh well I can't have everything.

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As already stated, active contours have difficulties progressing into concave boundaries. This has motivated research in this area, with some prominent findings, one of which is discussed here.

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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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