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Feedback alfa-rooting algorithm for medical image enhancement 17

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Feedback alfa-rooting algorithm for medical image enhancemen...

17th Image Processing: Algorithms and systems Conference, IPAS 2019

作者： Voronin, V. Zelensky, A. Agaian, S. Don State Technical University Lab. «Mathematical Methods of Image Processing and Computer Vision Intelligent Systems» Gagarina 1 Rostov on Don Russia Moscow State University of Technology "STANKIN" Moscow Russia CUNY/ College of Staten Island Dept. of Computer Science New York United States

This paper presents a new combined local and global transform domain-based feedback image enhancement algorithm for medical diagnosis, treatment, and clinical research. The basic idea in using local alfa-rooting method is to apply it to different disjoint blocks with different sizes. The block size and alfa-rooting parameters driven through optimization using the Agaian's cost function (image enhancement non-reference quality measure). The presented new approach allows enhancing MRI and CT images with uneven lighting and brightness gradient by preserving the local image features/details. Extensive computer simulations (CS) on real medical images are offered to gage the presented method. CS shows that our method improves the contrast and enhances the details of the medical images effectively compared with the current state-of-art methods. © 2019, Society for Imaging science and Technology.

关键词： Clinical research

The quaternion-based anisotropic gradient for the color images 17

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The quaternion-based anisotropic gradient for the color imag...

17th Image Processing: Algorithms and systems Conference, IPAS 2019

作者： Voronin, V. Zelensky, A. Agaian, S. Don State Technical University Lab. «Mathematical Methods of Image Processing and Computer Vision Intelligent Systems» Gagarina 1 Rostov on Don Russia Moscow State University of Technology"STANKIN" Moscow Russia CUNY College of Staten Island Dept. of Computer Science New York United States

Image gradient, as a preprocessing step is an essential tool in image processing in many research areas such as edge detection, segmentation, smoothing, inpainting, etc. In the present paper, we develop a new gradient by integrated the quaternion framework with LPA-ICI (local polynomial approximation - the intersection of confidence intervals) based on anisotropic gradient concepts for the color image processing applications. computer simulations on the Berkeley Segmentation Dataset show that the new quaternion anisotropic gradient exhibits fewer color artifacts compared to state-of-the-art techniques including well-knowledge gradient operators. © 2019, Society for Imaging science and Technology.

关键词： Polynomial approximation

On the fairness of disentangled representations

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arXiv 2019年

作者： Locatello, Francesco Abbati, Gabriele Rainforth, Tom Bauer, Stefan Schölkopf, Bernhard Bachem, Olivier Google Research Brain Team Dept. of Computer Science ETH Zurich Dept. of Engineering Science University of Oxford Dept. of Statistics University of Oxford Max-Planck Institute for Intelligent Systems

Recently there has been a significant interest in learning disentangled representations, as they promise increased interpretability, generalization to unseen scenarios and faster learning on downstream tasks. In this paper, we investigate the usefulness of different notions of disentanglement for improving the fairness of downstream prediction tasks based on representations. We consider the setting where the goal is to predict a target variable based on the learned representation of high-dimensional observations (such as images) that depend on both the target variable and an unobserved sensitive variable. We show that in this setting both the optimal and empirical predictions can be unfair, even if the target variable and the sensitive variable are independent. Analyzing more than 12 600 trained representations of state-of-the-art disentangled models, we observe that various disentanglement scores are consistently correlated with increased fairness, suggesting that disentanglement may be a useful property to encourage fairness when sensitive variables are not observed. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

Towards Quality Assessment of Crowdworker Output Based on Behavioral Data

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Towards Quality Assessment of Crowdworker Output Based on Be...

IEEE International Conference on Big Data

作者： Shigeaki Yuasa Takumi Nakai Takanori Maruichi Manuel Landsmann Koichi Kise Masaki Matsubara Atsuyuki Morishima Dept. of Computer Science and Intelligent Systems Osaka Prefecture University Japan Faculty of Library Information and Media Science University of Tsukuba Japan

ISBN: (数字)9781728108582

ISBN: (纸本)9781728108599

In this paper, we show preliminary results on the quality assessment of crowdworker output based on the movements of the mouse and the eyes while the task is performed. We assume that the mouse and the eyes stop longer if the quality is lower due to the lack of knowledge, or confidence, etc. Because the mouse- and eye-stopping duration follows lognormal distribution, we estimate its parameters (mean and standard deviation) to evaluate the quality. Results of preliminary experiments with 10 participants show that the parameters of correct outputs are different from those of incorrect ones. As compared to the task duration, which is often used as a feature for assessment, we have found that the mouse-and the eyestopping duration is advantageous and complementary for the assessment.

关键词： Task analysis Mice Crowdsourcing Standards Quality assessment Quality control Log-normal distribution

WiFi-based Real-time Breathing and Heart Rate Monitoring during Sleep

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WiFi-based Real-time Breathing and Heart Rate Monitoring dur...

IEEE Global Communications Conference

作者： Yu Gu Xiang Zhang Zhi Liu Fuji Ren School of Computer and Information Hefei University of Technology China Dept. of Mathematical and Systems Engineering Shizuoka University Japan Dept. of Information Science & Intelligent Systems Univ. of Tokushima Japan

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Good quality sleep is essential for good health and sleep monitoring becomes a vital research topic. This paper provides a low cost, continuous and contactless WiFi-based vital signs (breathing and heart rate) monitoring method. In particular, we set up the antennas based on Fresnel diffraction model and signal propagation theory, which enhances the detection of weak breathing/heartbeat motion. We implement a prototype system using the off-shelf devices and a real-time processing system to monitor vital signs in real time. The experimental results indicate the accurate breathing rate and heart rate detection performance. To the best of our knowledge, this is the first work to use a pair of WiFi devices and omnidirectional antennas to achieve real-time individual breathing rate and heart rate monitoring in different sleeping postures.

关键词： Fresnel reflection Sleep apnea Monitoring Heart rate Wireless fidelity Real-time systems Antennas

WiFi-based real-time breathing and heart rate monitoring during sleep

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arXiv 2019年

作者： Gu, Yu Zhang, Xiang Liu, Zhi Ren, Fuji School of Computer and Information Hefei University of Technology China Dept. of Mathematical and Systems Engineering Shizuoka University Japan Dept. of Information Science & Intelligent Systems Univ. of Tokushima Japan

关键词： Patient monitoring

BeSense: Leveraging WiFi Channel Data and Computational Intelligence for Behavior Analysis

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arXiv 2019年

作者： Gu, Yu Zhang, Xiang Liu, Zhi Ren, Fuji School of Computer & Information Hefei University of Technology Hefei China Dept. of Mathematical & Systems Engineering Shizuoka University Shizuoka Japan Dept. of Information Science & Intelligent Systems University of Tokushima Tokushima Japan

The ever evolving informatics technology has gradually bounded human and computer in a compact way. Understanding user behavior becomes a key enabler in many fields such as sedentary-related healthcare, human-computer interaction (HCI) and affective computing. Traditional sensor-based and vision-based user behavior analysis approaches are obtrusive in general, hindering their usage in real world. Therefore, in this article, we first introduce WiFi signal as a new source instead of sensor and vision for unobtrusive user behaviors analysis. Then we design BeSense, a contactless behavior analysis system leveraging signal processing and computational intelligence over WiFi channel state information (CSI). We prototype BeSense on commodity low-cost WiFi devices and evaluate its performance in real-world environments. Experimental results have verified its effectiveness in recognizing user behaviors. Copyright © 2019, The Authors. All rights reserved.

关键词： Channel state information

ConvLab-2: An open-source toolkit for building, evaluating, and diagnosing dialogue systems

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arXiv 2020年

作者： Zhu, Qi Zhang, Zheng Fang, Yan Li, Xiang Takanobu, Ryuichi Li, Jinchao Peng, Baolin Gao, Jianfeng Zhu, Xiaoyan Huang, Minlie Dept. of Computer Science and Technology Tsinghua University Beijing China Institute for Artificial Intelligence Tsinghua University Beijing China State Key Lab of Intelligent Technology and Systems Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China Microsoft Research Redmond United States

We present ConvLab-2, an open-source toolkit that enables researchers to build task-oriented dialogue systems with state-of-the-art models, perform an end-to-end evaluation, and diagnose the weakness of systems. As the successor of ConvLab (Lee et al., 2019b), ConvLab-2 inherits ConvLab’s framework but integrates more powerful dialogue models and supports more datasets. Besides, we have developed an analysis tool and an interactive tool to assist researchers in diagnosing dialogue systems. The analysis tool presents rich statistics and summarizes common mistakes from simulated dialogues, which facilitates error analysis and system improvement. The interactive tool provides a user interface that allows developers to diagnose an assembled dialogue system by interacting with the system and modifying the output of each system component. Copyright © 2020, The Authors. All rights reserved.

关键词： Speech processing

Disentangling factors of variation using few labels

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arXiv 2019年

作者： Locatello, Francesco Tschannen, Michael Bauer, Stefan Rätsch, Gunnar Schölkopf, Bernhard Bachem, Olivier Google Research Brain Team Dept. of Computer Science ETH Zurich Switzerland Dept. of Information Technology and Electrical Engineering ETH Zurich Switzerland Max-Planck Institute for Intelligent Systems

Learning disentangled representations is considered a cornerstone problem in representation learning. Recently, Locatello et al. (2019) demonstrated that unsupervised disentanglement learning without inductive biases is theoretically impossible and that existing inductive biases and unsupervised methods do not allow to consistently learn disentangled representations. However, in many practical settings, one might have access to a very limited amount of supervision, for example through manual labeling of training examples. In this paper, we investigate the impact of such supervision on state-of-the-art disentanglement methods and perform a large scale study, training over 29 000 models under well-defined and reproducible experimental conditions. We first observe that a very limited number of labeled examples (0.01-0.5% of the data set) is sufficient to perform model selection on state-of-the-art unsupervised models. Yet, if one has access to labels for supervised model selection, this raises the natural question of whether they should also be incorporated into the training process. As a case-study, we test the benefit of introducing (very limited) supervision into existing state-of-the-art unsupervised disentanglement methods exploiting both the values of the labels and the ordinal information that can be deduced from them. Overall, we empirically validate that with very little and potentially imprecise supervision it is possible to reliably learn disentangled representations. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning