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IEEE Int Conf on parallel and distributed processing with Applications, Big Data and Cloud Computing, Sustainable Computing and Communications, Social Computing and Networking (ISPA/BDCloud/SocialCom/SustainCom)

作者： Li, Hanyu Zhuang, Peixian Wang, Wei Li, Jingjing Nanjing Univ Informat Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9781728143286

Clear and clean underwater images can provide valuable information, which are crucial for developing, exploring, and protecting the underwater resources. However, the raw underwater image seldom fulfills the requirements concerning the underwater research due to the serious image degradation. To enhance single underwater image, we propose an effective and new method combined on the dehazing and color correction algorithm. First, we obtain the dehazed image by a fusion dehazing method calculated on the difference between maximum green-blue dark channels and maximum red dark channel, and the top 0.1% bright pixels in green-blue dark channels. Second, we obtain the enhanced image via the color restoration method corresponding to the human visual system. Finally, we use an efficient and simple weight fusion strategy to incorporate the dehazed image and the enhanced image for yielding the final high quality underwater image. Subjective assessment demonstrates that our method can remove haze, correct color cast, improve image brightness, and preserve image naturalness. The enhanced results obtain the highest values in terms of entropy, average gradient, UCIQE, UIQM, and CCF, which is superior to several existing underwater enhancement methods. Moreover, our method can improve other degraded image quality, such as low-light image and haze image, and can benefit the application tests, such as key points matching and edge detection.

关键词： Single underwater image enhancement image dehazing retinex color correction

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A novel fusion method based on dynamic threshold neural P systems and nonsubsampled contourlet transform for multi-modality medical images

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SIGNAL processing 2021年 178卷 107793-107793页

作者： Li, Bo Peng, Hong Wang, Jun Xihua Univ Sch Comp & Software Engn Chengdu 610039 Sichuan Peoples R China Xihua Univ Sch Elect Engn & Elect Informat Chengdu 610039 Peoples R China

Dynamic threshold neural P systems (DTNP systems) are a distributed parallel computing model with an interesting mechanism involving the cooperative spiking of neurons in a local region. In this paper, this mechanism is combined with the nonsubsampled contourlet transform (NSCT) to develop a novel fusion method for multi-modality medical images. The complementary information of multi-modality images is extracted using an improved novel sum-modified Laplacian (INSML) feature, which is used in the fusion rules for the low-frequency NSCT coefficients. Moreover, the high-frequency NSCT coefficients are extracted using the WLE-INSML features, which are used to construct the fusion rules for these coefficients. The proposed fusion method is evaluated on an open dataset consisting of twelve pairs of multi-modality medical images. In addition, it is compared with nine previously reported fusion methods and four deep learning based fusion methods. The qualitative and quantitative experimental results demonstrate the advantage of the proposed fusion method in terms of the visual quality and fusion performance. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Dynamic threshold neural P systems Nonsubsampled contourlet transform image fusion Multi-modality medical images

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parallel coordinates visualization in the ELK stack 30

Parallel coordinates visualization in the ELK stack

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30th International Conference on Computer Graphics and Machine Vision, GraphiCon 2020

作者： Galkin, Timofei Grigorieva, Maria National Research Nuclear University "MEPhI" Kashirskoe shosse 31 Moscow115409 Russia Scientific Research Computing Center Lomonosov Moscow State University Leninskie Gory 1 p.4 Moscow119991 Russia Moscow Center of Fundamental and Applied Mathematics Leninskie Gory 1 Moscow119991 Russia

Modern large-scale distributed computing systems, processing large volumes of data, require mature monitoring systems able to control and track in resources, networks, computing tasks, queues and other components. In recent years, the ELK stack has become very popular for the monitoring of computing environment, largely due to the efficiency and flexibility of the ElasticSearch storage and wide variety of Kibana visualization tools. The analysis of computing infrastructure metadata often requires the visual exploration of multiple parameters simultaneously on one graphical image. Stacked bar charts, heatmaps, radar charts are widely used for the multivariate visual data analysis, but these methods have limitations on the number of parameters. In this research the authors propose to enhance the capacity of Kibana, adding parallel Coordinates diagram - one of the most powerful method for visual interactive analysis of high-dimensional data. It allows to compare many variables together and observe correlations between them. This work describes the development process of parallel Coordinates as a Kibana plugin, and demonstrates an example of visual data analysis based on the Nginx logs metadata. © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Visualization

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Excitement Surfeited Turns to Errors: Deep Learning Testing Framework Based on Excitable Neurons

arXiv

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arXiv 2022年

作者： Jin, Haibo Chen, Ruoxi Zheng, Haibin Chen, Jinyin Cheng, Yao Yu, Yue Liu, Xianglong College of Information Engineering Zhejiang University of Technology Hangzhou China Institute of Cyberspace Security Zhejiang University of Technology Hangzhou China Huawei International Singapore National Laboratory for Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha China State Key Laboratory of Software Development Environment Beihang University Beijing China

Despite impressive capabilities and outstanding performance, deep neural networks (DNNs) have captured increasing public concern about their security problems, due to their frequently occurred erroneous behaviors. Therefore, it is necessary to conduct a systematical testing for DNNs before they are deployed to real-world applications. Existing testing methods have provided fine-grained metrics based on neuron coverage and proposed various approaches to improve such metrics. However, it has been gradually realized that a higher neuron coverage does not necessarily represent better capabilities in identifying defects that lead to errors. Besides, coverage-guided methods cannot hunt errors due to faulty training procedure. So the robustness improvement of DNNs via retraining by these testing examples are unsatisfactory. To address this challenge, we introduce the concept of excitable neurons based on Shapley value and design a novel white-box testing framework for DNNs, namely DeepSensor. It is motivated by our observation that neurons with larger responsibility towards model loss changes due to small perturbations are more likely related to incorrect corner cases due to potential defects. By maximizing the number of excitable neurons concerning various wrong behaviors of models, DeepSensor can generate testing examples that effectively trigger more errors due to adversarial inputs, polluted data and incomplete training. Extensive experiments implemented on both image classification models and speaker recognition models have demonstrated the superiority of DeepSensor. Compared with the state-of-the-art testing approaches, DeepSensor can find more test errors due to adversarial inputs (∼ ×1.2), polluted data (∼ ×5) and incompletely-trained DNNs (∼ ×1.3). Additionally, it can help DNNs build larger l2-norm robustness bound (∼ ×3) via retraining according to CLEVER's certification. We further provide interpretable proofs for effectiveness of DeepSensor via excitable neuro

关键词： Deep neural networks

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IO Challenges for Human Brain Atlasing using Deep Learning methods - An in-depth analysis 27

IO Challenges for Human Brain Atlasing using Deep Learning M...

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27th Euromicro International Conference on parallel, distributed and Network-Based processing (PDP)

作者： Oden, Lena Schiffer, Christian Spitzer, Hannah Dickscheid, Timo Pleiter, Dirk Forschungszentrum Julich JSC Julich Germany Forschungszentrum Julich Inst Neurosci & Med INM 1 Julich Germany Fernuniv Fak Math & Informat Hagen Germany

ISBN: (纸本)9781728116440

The use of Deep Learning methods have been identified as a key opportunity for enabling processing of extreme-scale scientific datasets. Feeding data into compute nodes equipped with several high-end GPUs at sufficiently high rate is a known challenge. Facilitating processing of these datasets thus requires the ability to store petabytes of data as well as to access the data with very high bandwidth. In this work, we look at two Deep Learning use cases for cytoarchitectonic brain mapping. These applications are very challenging for the underlying IO system. We present an in depth analysis of their IO requirements and performance. Both applications are limited by the IO performance, as the training processes often have to wait several seconds for new training data. Both applications read random patches from a collection of large HDF5 datasets or TIFF files, which result in many small non-consecutive accesses to the parallel file systems. By using a chunked data format or storing temporally copies of the required patches, the IO performance can be improved significantly. These leads to a decrease of the total runtime of up to 80%.

关键词： Training Task analysis Deep learning Graphics processing units Brain Bandwidth image resolution

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Underwater image Enhancement with a Total Generalized Variation Illumination Prior 17

Underwater Image Enhancement with a Total Generalized Variat...

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作者： Zhao, Zhengjie Dai, Yuxiang Zhuang, Peixian Nanjing Univ Informat Sci & Technol Sch Elect & Informat Engn Nanjing 210044 Peoples R China

ISBN: (纸本)9781728143286

In order to promote the visual quality of degraded underwater images, we develop an innovative variational model based on Retinex with a total generalized variation (TGV) prior on the illumination. The TGV prior is adopted to approximate piecewise smoothness and piecewise linear smoothness of the illumination, which combines the first-order and second-order total variation (TV) to model the variation of illumination. When adopting this illumination prior in the Retinex-based algorithm, the illumination and reflection are well separated, and underwater enhanced results appear more natural and their details and edges are better preserved. Then an efficient iterative optimization method is derived to settle the proposed model via alternately calculating the illumination and the reflection simultaneously. Numerous experiments on both visual results and objective metrics demonstrate the superiority of our method compared with several underwater enhancement methods. In addition, the proposed method can be extended for dehazing, sandstorm removal and low illumination image enhancement, which can illustrate better capacity of our model.

关键词： Underwater image enhancement Total generalized variation Retinex Alternating optimization

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DeepPFCN: Deep parallel Feature Consensus Network for Person Re-identification 7th

DeepPFCN: Deep Parallel Feature Consensus Network for Person...

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7th National Conference on Computer Vision, Pattern Recognition, image processing, and Graphics, NCVPRIPG 2019

作者： Singh, Shubham Kumar Miyapuram, Krishna P. Raman, Shanmuganathan Indian Institute of Technology Gandhinagar PalajGujarat382355 India

ISBN: (纸本)9789811586965

Person re-identification aims to associate images of the same person over multiple non-overlapping camera views at different times. Depending on the human operator, manual re-identification in large camera networks is highly time consuming and erroneous. Automated person re-identification is required due to the extensive quantity of visual data produced by rapid inflation of large scale distributed multi-camera systems. The state-of-the-art works focus on learning and factorize person appearance features into latent discriminative factors at multiple semantic levels. We propose Deep parallel Feature Consensus Network (DeepPFCN), a novel network architecture that learns multi-scale person appearance features using convolutional neural networks. This model factorizes the visual appearance of a person into latent discriminative factors at multiple semantic levels. Finally consensus is built. The feature representations learned by DeepPFCN are more robust for the person re-identification task, as we learn discriminative scale-specific features and maximize multi-scale feature fusion selections in multi-scale image inputs. We further exploit average and max pooling in separate scale for person-specific task to discriminate features globally and locally. We demonstrate the re-identification advantages of the proposed DeepPFCN model over the state-of-the-art re-identification methods on three benchmark datasets - Market1501, DukeMTMCreID, and CUHK03. We have achieved mAP results of 75.8%, 64.3%, and 52.6% respectively on these benchmark datasets. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Network architecture

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基于动态称量和图像识别技术的井下煤矸石智能分选装置研发

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矿业研究与开发 2023年第2期43卷 178-183页

作者：陈佳鑫赵国贞程伟常枫懿太原理工大学矿业工程学院山西太原030024 太原理工大学原位改性采矿教育部重点实验室山西太原030024

井下煤矸石分选是实现井下矸石“不升井”的重要技术措施。以干法选煤为基础,提出了间断煤流硐室式煤矸石分选和连续煤流皮带式煤矸石分选两种方法,以智能图像识别和动态称量技术为核心,设计了适用于井下硐室分选的紧凑式煤矸石分选装... 详细信息

井下煤矸石分选是实现井下矸石“不升井”的重要技术措施。以干法选煤为基础,提出了间断煤流硐室式煤矸石分选和连续煤流皮带式煤矸石分选两种方法,以智能图像识别和动态称量技术为核心,设计了适用于井下硐室分选的紧凑式煤矸石分选装置以及适用于顺槽或大巷的分布式煤矸石分选装置。前者使用了多级筛网结构,弥补了粒径差异对物体识别的影响;采用大角度传送带,有效减小了设备尺寸。后者使用小皮带轮作为原料运输的主要动力转换装置,减小了能源消耗,通过转盘装置使煤矸石识别和分选并行处理,提高了工作效率。装置的研发对推动煤炭绿色开采具有一定的意义。

关键词：井下煤矸石分选干法选煤智能分选装置

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Task Distribution Method for Index Classification based on Color Segmentation in Remote Sensing

Task Distribution Method for Index Classification based on C...

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International Conference on Advanced Information Technologies (ICAIT)

作者： Yan Naing Htun Bawin Aye Aung Aung Higher Education Center Pyin Oo Lwin Myanmar

ISBN: (数字)9781728183640

ISBN: (纸本)9781728183657

Digital processing of remotely sensed image data has been great importance in recent times. This research work discusses task distribution method in parallel image processing and load balancing under the circumstance of multi-tasks and multi-processors in remote sensing. Task distribution method can speed up computation and improve efficiency and perform larger computations which are not possible on single processor system. The tasks are distributed based on the segmentation of color and the Support Vector Machine (SVM) is used to classify the indices of the input image and intends to design and improve the color segmentation based task distribution method for index classification using machine learning. In the system, the RGB satellite image is used as an input image and the output is the four indices of forest, building, road and land The results of the system are more accurate and less time consumption than non-distributed computing methods. It is implemented in MATLAB platform with parallel computation toolbox because the system can solve computationally and data-intensive tasks using multicore processors and clusters of computer.

关键词： Task analysis image color analysis image segmentation Program processors Indexes Support vector machines Multicore processing

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Convolutional neural network-based age estimation using B-mode ultrasound tongue image

arXiv

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arXiv 2021年

作者： Xu, Kele Csapó, Tamás Gábor Feng, Ming National Key Lab of Parallel and Distributed Processing National University of Defense Technology Changsha China School of Computer National University of Defense Technology Changsha China Department of Telecommunications and Media Informatics Budapest University of Technology and Economics Budapest Hungary MTA-ELTE Lendület Lingual Articulation Research Group Budapest Hungary Tongji University Shanghai China

Ultrasound tongue imaging is widely used for speech production research, and it has attracted increasing attention as its potential applications seem to be evident in many different fields, such as the visual biofeedback tool for second language acquisition and silent speech interface. Unlike previous studies, here we explore the feasibility of age estimation using the ultrasound tongue image of the speakers. Motivated by the success of deep learning, this paper leverages deep learning on this task. We train a deep convolutional neural network model on the UltraSuite dataset. The deep model achieves mean absolute error (MAE) of 2.03 for the data from typically developing children, while MAE is 4.87 for the data from the children with speech sound disorders, which suggest that age estimation using ultrasound is more challenging for the children with speech sound disorder. The developed method can be used a tool to evaluate the performance of speech therapy sessions. It is also worthwhile to notice that, although we leverage the ultrasound tongue imaging for our study, the proposed methods may also be extended to other imaging modalities (e.g. MRI) to assist the studies on speech production. © 2021, CC BY-NC-SA.

关键词： Ultrasonics

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