2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2014
DOI: 10.1109/icassp.2014.6854120
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Generalized extreme value distributions, information geometry and sharpness functions for microscopy images

Abstract: We introduce the generalized extreme value distributions as descriptors of edge-related visual appearance properties. Theoretically these distributions are characterized by their limiting and stability properties which gives them a role similar to that of the normal distributions. Empirically we will show that these distributions provide a good fit for images from a large database of microscopy images with two visually very different types of images. The generalized extreme value distributions are transformed … Show more

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Cited by 5 publications
(3 citation statements)
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“…Investigation concerning the best PDF to be used to describe the distribution of sampled surface EMG signals becomes relevant because it can help improve algorithms of onset detection applied in neuroprosthesis [ 67 ], biofeedback [ 68 ], image processing [ 69 , 70 , 71 ] and extreme climates [ 72 , 73 , 74 ]. For a given dataset, the estimation of underlying PDF for pattern recognition and machine learning has been used for many years by statisticians and engineers.…”
Section: Automated Emg Analysismentioning
confidence: 99%
See 1 more Smart Citation
“…Investigation concerning the best PDF to be used to describe the distribution of sampled surface EMG signals becomes relevant because it can help improve algorithms of onset detection applied in neuroprosthesis [ 67 ], biofeedback [ 68 ], image processing [ 69 , 70 , 71 ] and extreme climates [ 72 , 73 , 74 ]. For a given dataset, the estimation of underlying PDF for pattern recognition and machine learning has been used for many years by statisticians and engineers.…”
Section: Automated Emg Analysismentioning
confidence: 99%
“…This is based on a minimum error produced by two Goodness-of-Fit (GOF) tests, namely, Kolmogorov-Smirnov statistic and Anderson-Darling statistic compared to the Generalized Pareto (GP) and Exponential (EXP) distributions. Nevertheless, this approach is applicable to determine the maximum value only which is mostly conducted in image processing [ 69 , 70 , 71 ] and climate extremes [ 72 , 73 , 74 ]. For invasive methods, the structurally developed model developed by De Luca [ 83 ] is more adequate to describe EMG signals.…”
Section: Automated Emg Analysismentioning
confidence: 99%
“…The magnitude of such a two-dimensional vector is invariant under the group operations while the relation between the two filter results encode the group operation. For many image sources (including microscopy images) the combined magnitude values can be characterized with the help of the generalized extreme value distributions (GEV), of which the more commonly known Weibull distribution is one example (see for example [1] or [2] ). Using the GEVs to analyze the filter results requires that the contribution of flat regions with near-zero filter results has to be excluded in the GEV-fitting.…”
Section: Introductionmentioning
confidence: 99%