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Reviews Of Classic Papers
The Papers
| • | HoultMR - The Quantum Origins of Free Induction decay Signal and Spin Noise, D. I. Hoult and N. S. Ginsberg |
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| • | ShannonEntropy - A Mathematical Theory of Communication, C. E. Shannon |
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| • | RichardNeuralNetworks - Neural Network Classifiers Estimate Bayesian a Posterioi Probabilities ,M. D. Richard and R. P. Lippmann. |
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| • | StephensHough - A Probabilistic Approach to the Hough Transform, R.S. Stephens |
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| • | LorusoTransforms - Estimating 3D Rigid Body Transformations : A Comparison of Four Algorithms, A. Lorusso et. al. |
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| • | TrivediStereoMotion - Estimation of Stereo and Motion Parameters using a Variational Principle, H. P. Trivedi |
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| • | GaoMorphological - Statistical Characterisation of Morphological Operator Sequences, X. Gao et. al. |
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| • | BooksteinVBM - Voxel-Based Morphometry should not be used with Imperfectly Registered Images, F. L. Bookstein |
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The Motivation For This Page
This page lists those papers in the areas of machine vision and medical image analysis that, in our opinion, are important in defining the scope of research in these fields. Each has a brief review that summarises the most important points to be made about the contents: often the conclusions of our own research into the subject rather than those of the paper itself. Some of these papers have been widely overlooked. If you have read a paper that you think deserves a mention here, then add you own review. The only firm rule here is that you cannot review one of your own papers.
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In order to achieve the ultimate aim of this page, which is to provide a resource that optimally concatenates the results of some of the exprience gained by researchers in the above fields, it will be considered perfectly acceptable to submit a review that rants about your favourite bugbear. Therefore, papers listed here may be present for one of three reasons: they definte the right way to do something; they discredit other papers on the wrong way to do something; or they motivate a discussion of the limitations of a certain approach. Hopefully, with some community input, this may evolve into a valuable resource for new Phd students and RAs.
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