488 research outputs found

    Median and related local filters for tensor-valued images

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    We develop a concept for the median filtering of tensor data. The main part of this concept is the definition of median for symmetric matrices. This definition is based on the minimisation of a geometrically motivated objective function which measures the sum of distances of a variable matrix to the given data matrices. This theoretically wellfounded concept fits into a context of similarly defined median filters for other multivariate data. Unlike some other approaches, we do not require by definition that the median has to be one of the given data values. Nevertheless, it happens so in many cases, equipping the matrix-valued median even with root signals similar to the scalar-valued situation. Like their scalar-valued counterparts, matrix-valued median filters show excellent capabilities for structure-preserving denoising. Experiments on diffusion tensor imaging, fluid dynamics and orientation estimation data are shown to demonstrate this. The orientation estimation examples give rise to a new variant of a robust adaptive structure tensor which can be compared to existing concepts. For the efficient computation of matrix medians, we present a convex programming framework. By generalising the idea of the matrix median filters, we design a variety of other local matrix filters. These include matrix-valued mid-range filters and, more generally, M-smoothers but also weighted medians and \alpha-quantiles. Mid-range filters and quantiles allow also interesting cross-links to fundamental concepts of matrix morphology

    Progression approach for image denoising

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    Removing noise from the image by retaining the details and features of this treated image remains a standing challenge for the researchers in this field. Therefore, this study is carried out to propose and implement a new denoising technique for removing impulse noise from the digital image, using a new way. This technique permits the narrowing of the gap between the original and the restored images, visually and quantitatively by adopting the mathematical concept ''arithmetic progression''. Through this paper, this concept is integrated into the image denoising, due to its ability in modelling the variation of pixels’ intensity in the image. The principle of the proposed denoising technique relies on the precision, where it keeps the uncorrupted pixels by using effective noise detection and converts the corrupted pixels by replacing them with other closest pixels from the original image at lower cost and with more simplicity

    On Kalman smoothing with random packet loss

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    The abstract is included in the text

    Digits Recognition on Medical Device

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    With the rapid development of mobile health, mechanisms for automatic data input are becoming increasingly important for mobile health apps. In these apps, users are often required to input data frequently, especially numbers, from medical devices such as glucometers and blood pressure meters. However, these simple tasks are tedious and prone to error. Even though some Bluetooth devices can make those input operations easier, they are not popular enough due to being expensive and requiring complicated protocol support. Therefore, we propose an automatic procedure to recognize the digits on the screen of medical devices with smartphone cameras. The whole procedure includes several “standard” components in computer vision: image enhancement, the region-of-interest detection, and text recognition. Previous works existed for each component, but they have various weaknesses that lead to a low recognition rate. We proposed several novel enhancements in each component. Experiment results suggest that our enhanced procedure outperforms the procedure of applying optical character recognition directly from 6.2% to 62.1%. This procedure can be adopted (with human verification) to recognize the digits on the screen of medical devices with smartphone cameras

    Kalman filter with impulse noised outliers : A robust sequential algorithm to filter data with a large number of outliers

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    Impulsed noise outliers are data points that differs significantly from other observations.They are generally removed from the data set through local regression or Kalman filter algorithm.However, these methods, or their generalizations, are not well suited when the number of outliers is ofthe same order as the number of low-noise data. In this article, we propose a new model for impulsenoised outliers based on simple latent linear Gaussian processes as in the Kalman Filter. We present a fastforward-backward algorithm to filter and smooth sequential data and which also detect these outliers.We compare the robustness and efficiency of this algorithm with classical methods. Finally, we applythis method on a real data set from a Walk Over Weighing system admitting around 60% of outliers. Forthis application, we further develop an (explicit) EM algorithm to calibrate some algorithm parameters

    Machine learning model selection with multi-objective Bayesian optimization and reinforcement learning

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    A machine learning system, including when used in reinforcement learning, is usually fed with only limited data, while aimed at training a model with good predictive performance that can generalize to an underlying data distribution. Within certain hypothesis classes, model selection chooses a model based on selection criteria calculated from available data, which usually serve as estimators of generalization performance of the model. One major challenge for model selection that has drawn increasing attention is the discrepancy between the data distribution where training data is sampled from and the data distribution at deployment. The model can over-fit in the training distribution, and fail to extrapolate in unseen deployment distributions, which can greatly harm the reliability of a machine learning system. Such a distribution shift challenge can become even more pronounced in high-dimensional data types like gene expression data, functional data and image data, especially in a decentralized learning scenario. Another challenge for model selection is efficient search in the hypothesis space. Since training a machine learning model usually takes a fair amount of resources, searching for an appropriate model with favorable configurations is by inheritance an expensive process, thus calling for efficient optimization algorithms. To tackle the challenge of distribution shift, novel resampling methods for the evaluation of robustness of neural network was proposed, as well as a domain generalization method using multi-objective bayesian optimization in decentralized learning scenario and variational inference in a domain unsupervised manner. To tackle the expensive model search problem, combining bayesian optimization and reinforcement learning in an interleaved manner was proposed for efficient search in a hierarchical conditional configuration space. Additionally, the effectiveness of using multi-objective bayesian optimization for model search in a decentralized learning scenarios was proposed and verified. A model selection perspective to reinforcement learning was proposed with associated contributions in tackling the problem of exploration in high dimensional state action spaces and sparse reward. Connections between statistical inference and control was summarized. Additionally, contributions in open source software development in related machine learning sub-topics like feature selection and functional data analysis with advanced tuning method and abundant benchmarking were also made

    Frequency-domain method for measuring alpha factor by self-mixing interferometry

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    Linewidth enhancement factor, also known as the alpha factor, is a fundamental characteristic parameter of a laser diode (LD). It characterises the broadening of the laser linewidth, the frequency chirp, the injection lock range and the response to external optical feedback. In the past few decades, extensive researches have been dedicated to the measurement of alpha. Among all the existing approaches, the methods based on selfmixing interferometry (SMI) are considered the most simple and effective. The core components of a SMI consist of an LD, a lens and a moving target. When a portion of laser light backscattered or reflected by the external target and re-enters the laser cavity, a modulated lasing field will be generated. The modulated laser power is also called SMI signal, which carries the information of target movement and LD related parameters, including alpha
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