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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

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XGBoost Prediction of Infection of Leukemia Patients with Fever of Unknown Origin 22

XGBoost Prediction of Infection of Leukemia Patients with Fe...

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7th International conference on Biomedical Signal and image processing, ICBIP 2022

作者： Li, Yan Song, Yanhui Ma, Fei Department of Applied Mathematics Xi'an Jiaotong-Liverpool University Suzhou China First Hospital Soochow University Center of Clinical Laboratory Suzhou China

ISBN: (纸本)9781450396691

Discovering the source of a patient's fever without clinically localised signs can be a daunting task for doctors. In particular for leukaemia patients with fever of unknown origin, fast discovering the source of the fever is a formidable challenge, as this population has the potential to lead to fever in many different situations. In this paper, we applied XGBoost algorithm to predict the pathogenic infections from a big data repository of leukemia patients with fever of unknown origin (FUO) and compared the performance with other machine learning algorithms. Our results illustrates that those machine learning algorithms achieves good performance. In particular, the XGBoost obtains the best performance with an area under receiving-operating-characteristics curve (AUC) of 0.8376 and F1-score of 0.7034. Compared with existing literature, our experiment provides new insights for doctors to determine the cause of fever in leukemia patients. © 2022 ACM.

关键词： Forecasting

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A Near-Duplicate Video Cleaning Method Based on AFENet Adaptive Clustering

A Near-Duplicate Video Cleaning Method Based on AFENet Adapt...

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6th International conference on Intelligent Computing and Signal processing (ICSP)

作者： Yan Fu Rui Duan Ou Ye College of Computer Science and Technology Xi’an University of Science and Technology Xi’an China

In recent years, with the growth of video data, problems such as video quality degradation and copyright protection difficulties have arisen due to the redundancy of a large number of near-duplicate video data. Aiming at the problems of insufficient video feature extraction and low accuracy of clustering algorithms in existing near-duplicate video cleaning algorithms. This paper proposes a near-duplicate video cleaning method based on AFENet adaptive clustering. The method in this paper mainly includes two parts: Firstly, in the feature extraction stage, this paper adopts the spatiotemporal features of video and enhances the features of salient objects in each frame of image, enhancing the feature expression ability of video; Secondly, in the video cleaning stage, an adaptive feature distance threshold SFD-Means clustering algorithm is proposed, and a clustering optimization function is designed to overcome the defect of the original FD-Means algorithm that is too sensitive to threshold selection. Experimental results show that our proposed method has high recognition accuracy and cleaning performance, effectively improving the quality of video data.

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EFormer-VPR: Fusing Events and Frames with Transformer for Visual Place Recognition

EFormer-VPR: Fusing Events and Frames with Transformer for V...

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International conference on Intelligent Transportation

作者： Yangjing Zhang Yanqing Shen Ziyu Zhu Renwei Hai Shitao Chen Nanning Zheng National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications and Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an Shaanxi P.R. China

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Visual place recognition (VPR) is a challenging task faced by mobile robots and autonomous driving systems. In scenarios with glare or high-speed motion, image blurring makes it difficult for traditional cameras to perform reliable and accurate place recognition. In contrast, event cameras can capture target motion information without blur in high-speed motion scenes, but lack texture information in low-speed motion scenes. To leverage the complementary characteristics of these two sensors and improve the performance and robustness of VPR algorithms, we propose EFormer- VPR, which fuses target motion events and frames with transformer. This method firstly preprocesses the events stream within an adaptive time window using a clustering method, then uses transformer-based networks to extract features from motion frames and events separately and fuse them through a scoring module. Finally, features are aggregated using a VLAD layer and the whole pipeline is supervised by a triplet ranking loss. To verify the effectiveness of the proposed algorithm, we compare it with other VPR methods on the event-based driving datasets (Brisbane-Event-VPR, NeuroGPR) with challenging scenarios. Experimental results show that on the Brisbane- Event- Vprand NeuroGPR datasets, our method achieves state-of-the-art performance.

关键词： image recognition Fuses Clustering methods Robot vision systems Clustering algorithms Feature extraction Transformers Cameras Data mining Visual place recognition

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Development of a Model for Texture Analysis of Machined Parts on CNC Machines

Development of a Model for Texture Analysis of Machined Part...

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International Russian Smart Industry conference (SmartIndustryCon)

作者： Ilya Kovalev David Malykh Alexander Kiselev department of Computer Control Systems MSUT “STANKIN” Moscow Russia department of Robotics and Mechatronics MSUT “STANKIN” Moscow Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

This work is devoted to the development of a software module for analyzing the textures of machined parts on CNC machines using computer vision methods. The main goal of the research is to create a tool capable of analyzing geometric parameters of products based on texture properties of materials, which is especially relevant in the conditions of high-volume production. The paper considers modern methods of texture analysis, as well as their robustness to various disturbances such as illumination changes, noise, blurring, rotation and image scaling. An analysis system that combines binocular and texture methods of image processing is proposed, which makes it possible to improve the accuracy of measuring distances to objects on the basis of binocular algorithm, by means of additional analysis of texture properties of surfaces of the processed part.

关键词： Computer vision image analysis Software algorithms Noise User interfaces Software Robustness Classification algorithms Surface texture Surface treatment

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3rd International conference on Communication, Computing and Electronics systems, ICCCES 2021

3rd International Conference on Communication, Computing and...

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3rd International conference on Communication, Computing and Electronics systems, ICCCES 2021

ISBN: (纸本)9789811688614

The proceedings contain 75 papers. The special focus in this conference is on Communication, Computing and Electronics systems. The topics include: Energy-Efficient Hybrid Secured Routing for 5G Vehicular Ad Hoc Network (VANET);a Study on Recent Advances in Artificial Intelligence and Future Prospects of Attaining Superintelligence;development of Automated Protection and Monitoring System for Poor Railway Infrastructure;a System Design Perspective for Measurement of Parameters Using Different Scatterings Associated with Fibre Optic Sensors;Utilization of Machine Learning Techniques for Prediction of COVID-19 Epidemic;internet of Things: Security Mechanisms;Performance Analysis of Video Transmission Over OWC-PON in Weak Turbulence Regimes;fish Species Detection Using Deep Learning for Industrial Applications;remaining Useful Life Prediction Using Machine Learning algorithms;dietary Assessment and Nutritional Analysis Using Deep Learning;binary and Multiclass Classifications Using a Deep Fusion Network;image processing-Based Switching Matrix for PV Array Under Partial Shading Conditions;an Enhanced Approach to Predict Re-occurrences of Breast Cancer Using Machine Learning;A Review on IOT Response to COVID-19 Pandemic, Challenges and Open Issues;Food Assessment Model for Indian Elderly Persons Using CNN and image processing Techniques;changes in Commodity Circulation Channels Based on International Trade E-commerce Platform from Computing and Electronics System Perspectives;key Technologies of English Document Grammar Recognition System with Cloud Computing and Electronics systems;Machine Learning-Based Approach for Depression Detection Using PHQ-9 and Twitter Dataset;systematic Review on Churn Prediction systems in Telecommunications;generating Recommendations for Various Problems Using Data Mining and Machine Learning algorithms;music Emotion Recognition Using Neural Networks;preface.

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Research on Online Monitoring of Print image Quality Detection Based on image Technology 21

Research on Online Monitoring of Print Image Quality Detecti...

引用

21st IEEE International conference on Communication Technology, ICCT 2021

作者： Wang, Yi Yang, Menghao Xi'An University of Technology Faculty of Printing Engineering and Digital Media Technology Xi'an China

ISBN: (纸本)9781665432061

In this paper, a set of detection algorithms that can identify print defects is studied in view of the various defects that may occur in the printing process. Firstly, in the image preprocessing part, this paper mainly focuses on the problem of information loss and the influence of image noise when the image is grayed, the algorithms of image graying and image denoising are studied. Secondly, aiming at the position mismatch between the detected image and the standard image, the algorithm of image registration is studied. image registration using cross correlation algorithm. Thirdly, aiming at the problem of printing defect detection, the threshold of image binarization and the algorithm of image morphology processing are studied, using image difference method to calculate image defects, binarization of image using maximum cumulative variance threshold method, morphological processing of image using open operation. Fourthly, aiming at the problem of defect classification and recognition, the method and algorithm of classification are studied. In this paper, print image quality problems are classified as point defects, line defects and surface defects, BP neural network is used to realize the recognition and classification of printed image defects. © 2021 IEEE.

关键词： image registration

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SAI: Self-Adjusting Incremental Quantile Estimation for Sparse Training of Neural Networks on Hardware Accelerators 23

SAI: Self-Adjusting Incremental Quantile Estimation for Spar...

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23rd IEEE International conference on High Performance Computing and Communications, 7th IEEE International conference on Data Science and systems, 19th IEEE International conference on Smart City and 7th IEEE International conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Yang, Jianchao Wen, Mei Tang, Minjin Shen, Junzhong Zhang, Chunyuan College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781665494571

Supporting sparse training of neural networks has become a trend for hardware accelerators. Existing sparse training algorithms generally rely on sorting to compress neural network models. The accelerated sparse training of hardware accelerators is inseparable from the hardware implementation of sorting, which should have the characteristics of one-pass, storage-friendly, and non-dependent hyperparameters. In this paper, we propose a hardware friendly and one-pass Self-Adjusting Incremental Quantile Estimation method, shorten to SAI, to replace the sorting operations commonly used in sparse training algorithms. After applying SAI to sparse training of neural network models, our method reduces the time cost of determining to clip and activate connections to O(n), and avoids auxiliary storage and frequent I/O operations, which is unattainable by sorting. SAI-based sparse training algorithms show the performance advantages under the condition of smaller sparsity, and can effectively develop parallelism. We design the quantile estimation module of SAI, and test its effect on the image classification combined with sparse training algorithm on CIFAR-10 dataset. Experimental results show that SAI-based sparse training algorithms can achieve high accuracy, and has obvious parallel optimization ability. © 2021 IEEE.

关键词： Training Smart cities Neural networks Estimation Parallel processing Market research Classification algorithms

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Amplifying Membership Exposure via Data Poisoning 36

Amplifying Membership Exposure via Data Poisoning

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36th conference on Neural Information processing systems (NeurIPS)

作者： Chen, Yufei Shen, Chao Shen, Yun Wang, Cong Zhang, Yang Xi An Jiao Tong Univ Xian Peoples R China City Univ Hong Kong Hong Kong Peoples R China NetApp San Jose CA USA CISPA Helmholtz Ctr Informat Secur Saarbrucken Germany

ISBN: (纸本)9781713871088

As in-the-wild data are increasingly involved in the training stage, machine learning applications become more susceptible to data poisoning attacks. Such attacks typically lead to test-time accuracy degradation or controlled misprediction. In this paper, we investigate the third type of exploitation of data poisoning - increasing the risks of privacy leakage of benign training samples. To this end, we demonstrate a set of data poisoning attacks to amplify the membership exposure of the targeted class. We first propose a generic dirty-label attack for supervised classification algorithms. We then propose an optimization-based clean-label attack in the transfer learning scenario, whereby the poisoning samples are correctly labeled and look "natural" to evade human moderation. We extensively evaluate our attacks on computer vision benchmarks. Our results show that the proposed attacks can substantially increase the membership inference precision with minimum overall test-time model performance degradation. To mitigate the potential negative impacts of our attacks, we also investigate feasible countermeasures.(1)

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Detecting Counterfeit Indian Currency A Comparative Analysis of Machine Learning algorithms and image processing

Detecting Counterfeit Indian Currency A Comparative Analysis...

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International conference on Computing and Networking Technology (ICCNT)

作者： M R V Vyshnavi N.L. Varshitha Konda V. S. Harshith Kumar Tripty Singh Mansi Sharma Suman Chatterjee Department of Computer Science Engineering Amrita School of Computing Bengaluru India Graphics Era (Deemed to be university) Dehradun India Department of Mechanical Engineering Kongju National University Cheonan India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The degree of secured currency is one of the key areas that makes its contribution in solving the problem of economic insecurity, highlights the most important. Even as it can be quite strained to actually identify which precise time frame this fake process was prepared in, complex fakes render the use of validation techniques quite challenging. This paper has aimed at displaying and evaluating the overall outcome of the different strategies of image, and the acquired classifiers in the identification of the fake FI currency. The following are the subsequent of the other operation that was applied in order to achieve the objectives of the intended work; image capture This is whereby the images to be used are taken by the appropriate camera or scanner. Others used include the Naïve Bayes, Support Vector Classifier and Gradient Descent and Artificial Neural Network in this paper. As in the conclusion, with the help of the mathematical formulation, it can be confirmed that in fact, within an experiment, Neural Networks do have a higher level of optimization, much more than an average image processing if it is provoked to identify fake notes. In other words if used and implemented with the best of the machine learning approach and best of the image processing method then solitaire for detection of the counterfeit currency can be made more dependable than all the combined methods stated above. The objective of this study is to build and compare the performance of different machine learning algorithms and image processing methodologies for the identification of fake Indian currency notes.

关键词： Economics Training Machine learning algorithms Machine learning Artificial neural networks Vectors Real-time systems Bayes methods Reliability Currencies

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Automated System for the Personalization of Retinal Laser Treatment in Diabetic Retinopathy Based on the Intelligent Analysis of OCT Data and Fundus images 14th

Automated System for the Personalization of Retinal Laser Tr...

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14th International KES conference on Intelligent Decision Technologies, KES-IDT 2022

作者： Ilyasova, Nataly Demin, Nikita Shirokanev, Aleksandr Andriyanov, Nikita Image Processing Systems Institute of the RAS—Branch of the FSRC "Crystallography and Photonics" RAS Molodogvardeyskay 151 Samara443001 Russia Financial University Under the Government of the Russian Federation Leningradsky pr-t 49 Moscow Russia

ISBN: (纸本)9789811934438

In this work, we propose an automated system for the personalization of retina laser treatment in diabetic retinopathy. The system comprises fundus images processing methods, algorithms for photocoagulation pattern mapping, and intelligent analysis methods of OCT data and fundus images. The feasibility of introducing corrections at any interim stage of data processing makes for a safe treatment. A key module of the proposed software architecture is the system for the intelligent analysis of the photocoagulation pattern, allowing the proposed plan to be analyzed and the treatment outcome to be prognosticated. Working with the proposed system, the surgeon is able to map an effective photocoagulation pattern, which is aimed at providing a higher-quality diabetic retinopathy laser treatment when compared with the current approaches. The software developed is intended for the use of high-performance algorithms that can be parallelized using either a processor or a graphics processing unit. This allows achieving high data processing speed, which is so important for medical systems. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Graphics processing unit

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