799 research outputs found
Statistical Comparison of Classifiers Applied to the Interferential Tear Film Lipid Layer Automatic Classification
The tear film lipid layer is heterogeneous among the population. Its
classification depends on its thickness and can be done using the interference pattern categories proposed by Guillon. The interference phenomena can be characterised as a colour texture pattern, which can be automatically classified into one of these categories. From a photography of
the eye, a region of interest is detected and its low-level features are extracted, generating a feature vector that describes it, to be finally classified
in one of the target categories. This paper presents an exhaustive study
about the problem at hand using different texture analysis methods in
three colour spaces and different machine learning algorithms. All these
methods and classifiers have been tested on a dataset composed of 105
images from healthy subjects and the results have been statistically analysed. As a result, the manual process done by experts can be automated
with the benefits of being faster and unaffected by subjective factors, with
maximum accuracy over 95%
Extracting Axial Depth and Trajectory Trend Using Astigmatism, Gaussian Fitting, and CNNs for Protein Tracking
Accurate analysis of vesicle trafficking in live cells is challenging for a number of reasons: varying appearance, complex protein movement patterns, and imaging conditions. To allow fast image acquisition, we study how employing an astigmatism can be utilized for obtaining additional information that could make tracking more robust. We present two approaches for measuring the z position of individual vesicles. Firstly, Gaussian curve fitting with CNN-based denoising is applied to infer the absolute depth around the focal plane of each localized protein. We demonstrate that adding denoising yields more accurate estimation of depth while preserving the overall structure of the localized proteins. Secondly, we investigate if we can predict using a custom CNN architecture the axial trajectory trend. We demonstrate that this method performs well on calibration beads data without the need for denoising. By incorporating the obtained depth information into a trajectory analysis, we demonstrate the potential improvement in vesicle tracking
Maximising with-profit pensions without guarantees
Currently, pension providers are running into trouble mainly due to the ultra-low interest rates and the guarantees associated to some pension benefits. With the aim of reducing the pension volatility and providing adequate pension levels with no guarantees, we carry out mathematical analysis of a new pension design in the accumulation phase. The individual's premium is split into the individual and collective part and invested in funds. In times when the return from the individual fund exits a predefined corridor, a certain number of units is transferred to or from the collective account smoothing in this way the volatility of the individual fund. The target is to maximise the total accumulated capital, consisting of the individual account and a portion of the collective account due to a so-called redistribution index, at retirement by controlling the corridor width. We also discuss the necessary and sufficient conditions that have to be put on the redistribution index in order to avoid arbitrage opportunities for contributors
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