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检索条件"任意字段=17th Image Processing: Algorithms and Systems Conference, IPAS 2019"

共 249 条记录，以下是241-250 订阅

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PotholeEye -- How Can We Effectively Maintain the Pavement Distress? (demo) 19

PotholeEye -- How Can We Effectively Maintain the Pavement D...

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Proceedings of the 17th Annual International conference on Mobile systems, Applications, and Services

作者： Juyoung Park Kiyoung Im Jung Hee Lee Korea Expressway Corporation Hwaseong Republic of Korea Geolux Seoul Republic of Korea

ISBN: (纸本)9781450366618

We propose a mobile system, called PotholeEye, for automatically monitoring the surface of a roadway and providing real-time analysis of images, with the specific goal of detecting the pavement distress. PotholeEye pre-processes the images, extracts features, and classifies the distress into a variety of types, while the road manager is driving. We have tested PotholeEye on real highway involving real settings, a camera, a mini computer, a GPS receiver, and so on, and have shown how we can effectively maintain the pavement distress.

关键词： pavement distress image processing convolution neural network

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Super-Resolving SAR images with Diffusion Models: A Dual Evaluation of Metrics and Applications

Super-Resolving SAR Images with Diffusion Models: A Dual Eva...

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International conference on Signal processing Proceedings (ICSP)

作者： Zilong Chen Siqian Zhang Boli Xiong College of Electronic Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

SAR, recognized for its capability to capture images under various weather conditions and at any time, is served as a key technology in remote sensing domains. Nonetheless, the inherent limitations of imaging systems often result in SAR images that do not achieve high resolution, thereby constraining their specific applications. Drawing inspiration from the efficacy of diffusion models in generative tasks involving natural images, we have devised a model that converts Gaussian noise into a super-resolution image derived from low-resolution input, utilizing a Markov chain framework and integrating residual prediction to facilitate faster convergence. the performance of this model is assessed in comparison to leading methodologies, including Generative Adversarial Networks and Transformers, employing both traditional evaluation metrics and semantic segmentation tasks. Experimental findings derived from the MSTAR and SARBake datasets reveal that the super-resolution algorithm utilizing a diffusion model exhibits superior performance relative to alternative models, as evidenced by conventional metrics. this algorithm not only demonstrates exceptional visual quality but also proves effective in downstream applications, underscoring its considerable potential for tasks involving super-resolution of SAR images.

关键词： Measurement Visualization Semantic segmentation Superresolution Signal processing algorithms Diffusion models Transformers Radar polarimetry Signal resolution Remote sensing

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Highly Sensitive Real-Time Detection of Surface Texture Changes

Highly Sensitive Real-Time Detection of Surface Texture Chan...

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International conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science

作者： Andrii Yarmilko Inna Rozlomii Yuliia Mysiura Department of Automated Systems Software Bohdan Khmelnytsky National University of Cherkasy Cherkasy Ukraine Department of Information Security and Computer Engineering Cherkasy State Technological University Cherkasy Ukraine Department of Information Technologies Bohdan Khmelnytsky National University of Cherkasy Cherkasy Ukraine

ISBN: (数字)9798331520564

ISBN: (纸本)9798331520571

Real-time surface analysis is becoming increasingly important in various fields, including industry, medicine, and transportation. this article examines the problem of remote monitoring of surface texture in real time. the authors review the methods and techniques used for this task, focusing on high-precision technologies such as computer vision, laser scanning, and image processing techniques. the paper examines the importance of this real-time approach to support accurate and rapid decisions in industrial processes and medical diagnostics. the authors propose a method for detecting texture artifacts with machine learning and analyze the performance and quality of possible variations of its application. Based on the discussion, the authors draw conclusions regarding the effectiveness and prospects of the proposed approach in real-time remote monitoring of surface textures. they emphasize the importance of further research in this direction and the use of innovative technologies to improve the accuracy and speed of the monitoring process. this article will be a valuable source of information for scientists, engineers and professionals in the field of industrial control and automation.

关键词： Industries Computer vision Sensitivity Accuracy Clustering algorithms Machine learning Real-time systems Surface texture Surface treatment Remote monitoring

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Sampling Phased Array,a New Technique for Ultrasonic Signal processing and Imaging Now Available to Industry

Sampling Phased Array,a New Technique for Ultrasonic Signal ...

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17th World conference on Non-Destructive Testing

作者： Jan VERKOOIJEN Andrey BOULAVINOV Chief Executive Officer The Sonovation Group of Companies Oosterhout The Netherlands

Over the past 10 years the improvement in the field of microelectronics and computer engineering has led to significant advances in ultrasonic signal processing and image construction techniques that are currently being applied to non-destructive material evaluation. A new phased array technique,called"Sampling Phased Array"has been developed in the Fraunhofer Institute for non-destructive *** realizes a unique approach of measurement and processing of ultrasonic signals. the sampling phased array principle makes use of the measurement of elementary waves generated by individual elements of a sensor array to reconstruct the Composite phased array signal for any arbitrary angle or focus depth. the use of special signal processing and image reconstruction algorithms, allows generating A-Scans of several angles and/or Sector-Scans,which can be implemented in real *** parallel computing structures,this principle is used for automatic testing systems at very high inspection speed. A comparative study was done with Conventional Phased Array and the Sampling Phased Array *** study shows that the signal characteristics in both techniques are *** addition,the Sampling Phased Array technique is significantly beneficial in many aspects like quality of information in specific cases,inspection speeds and adaptability to specific inspection tasks in comparison to conventional Phased Array. the development results,including relevant test methodology and equipment aspects are presented in the current work,together with the benefits for industrial ultrasonic inspection. the electronics required to enable the application of Sampling Phased Array, was,in the first instance created as a development platform for high speed automated ultrasonic inspection *** came from the requirement to speed up the inspection of manufacturing testing and of course the testing of critical components. Sonovation have now adapted Sampling Phased Array in their data acq

关键词： Ultrasonic Phased array Weld inspection Turbine shaft inspection

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Cluster storage systems gotta have HeART: improving storage efficiency by exploiting disk-reliability heterogeneity 19

Cluster storage systems gotta have HeART: improving storage ...

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Proceedings of the 17th USENIX conference on File and Storage Technologies

作者： Saurabh Kadekodi K. V. Rashmi Gregory R. Ganger Carnegie Mellon University

ISBN: (纸本)9781931971485

Large-scale cluster storage systems typically consist of a heterogeneous mix of storage devices with significantly varying failure rates. Despite such differences among devices, redundancy settings are generally configured in a one-scheme-for-all fashion. In this paper, we make a case for exploiting reliability heterogeneity to tailor redundancy settings to different device groups. We present HeART, an online tuning tool that guides selection of, and transitions between redundancy settings for long-term data reliability, based on observed reliability properties of each disk group. By processing disk failure data over time, HeART identifies the boundaries and steady-state failure rate for each deployed disk group (e.g., by make/model). Using this information, HeART suggests the most space-efficient redundancy option allowed that will achieve the specified target data reliability. Analysis of longitudinal failure data for a large production storage cluster shows the robustness of HeART's failure-rate determination algorithms. the same analysis shows that a storage system guided by HeART could provide target data reliability levels with fewer disks than one-scheme-for-all approaches: 11-16% fewer compared to erasure codes like 10-of-14 or 6-of-9 and 33% fewer compared to 3-way replication.

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Data engineering for the automation of non-contact foliar diagnostics of plants based on the HSL model of color formation

Data engineering for the automation of non-contact foliar di...

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International Computer Sciences and Information Technologies (CSIT)

作者： Natalia A. Pasichnyk Oleksiy A. Opryshko Dmytro S. Komarchuk Nadiia A. Yasinska Ruslan S. Rakhmedov Nikolay A. Kiktev Department of Agricultural Chemistry and Quality of Plant Products National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Department of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine R&D Department Fichessoft LLC 2105 Vista Oeste NW Albuquerque NM USA Presidium of the National Academy of Agrarian Sciences of Ukraine Kyiv Ukraine Software Department Stripe Inc 354 Oyster Point Blvd South San Francisco CA USA

ISBN: (纸本)9798350334326

the work is devoted to implementing traditional technologies of visual monitoring of plants for precision agriculture technologies, namely data engineering for the improvement of remote monitoring of marker vegetation indices with the help of UAVs. Classic vegetation indices such as NDVI that used to solve a limited range of problems and them mainly used to adjust the number of nitrogen fertilizers during differentiated treatment of field areas. Such indices are poorly adapted to identify the causes of stress. For stresses of a technical nature, in particular, on winter rapeseed crops, marker indices are used, which, with the traditional model of color formation, are difficult to adjust to identify anomalous coloration of affected plants. In addition, the accuracy of classical indices for the additive color formation model is affected by changes in lighting, increasing the accuracy of anomaly identification requires additional adjustment based on the state of the atmosphere at the time of image acquisition. the purpose of the work is the formation of a new approach to the automation of visual diagnostics of plants, which is based on the adaptation of machine vision technologies to the existing technologies of noncontact expert assessment of plants. A hypothesis was put forward about the possibility of creating vegetation indices based on an alternative model of HSL coloration, which would be more resistant to changes in illumination. the research was carried out on winter wheat crops in April-May 2021, archival images of winter rapeseed crops affected by technological stress taken in 2019 were also used. Photography was carried out using a Phantom 2 UAV in the visible range of the spectrum. Data processing was performed in the MathCad environment. the research was conducted on the resistance to changes in lighting during hardware adjustment of the exposure of images, as well as the features of object identification, namely, healthy and affected leaves and soil in th

关键词： Resistance Visualization image color analysis Atmospheric modeling Stability criteria Vegetation mapping Lighting

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Unsupervised Clustering Based Real-time Shot Boundary Detection for Live Broadcasting

Unsupervised Clustering Based Real-time Shot Boundary Detect...

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International conference on Computer and Communications (ICCC)

作者： Ning Su Jun Zhang Yana Zhang Guoting Zhang Communication University of China Beijing China Academy of Broadcasting Science NRTA Beijing China

ISBN: (数字)9781728147437

ISBN: (纸本)9781728147444

the development of mobile Internet greatly facilitates information communication. the mobile live broadcasting platform has made video-sharing popular. In a long live broadcast, it is necessary to generate a video summary or some pieces of news for secondary information spreading. In order to produce a video summary in real-time, shot boundary detection (SBD) of video stream is badly needed. Traditionally, shot boundaries are determined by a threshold of feature distance in adjacent frames. It is difficult to decide a threshold applied in kinds of media content with good performance. Another, researchers have proposed Support Vector Machines (SVM) to classify shot boundaries, whose model relies much on the characteristics of training data. In terms of live broadcasting, real-time signal processing is required. this paper proposes an Unsupervised Clustering based Real-time SBD method (UCR-SBD), in which consideration is given to both detection efficiency and accuracy. Experiments show that the F value of SBD reaches 97.24% and the detection speed is 17 ms per frame on average. Finally, the proposed algorithm has been successfully applied in a real-time news reporting system for the live broadcast of CUC TV station.

关键词： Feature extraction Histograms image edge detection image color analysis Clustering algorithms Streaming media Real-time systems

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Brain Tumor Classification Using Efficient Deep Features of MRI Scans and Support Vector Machine

Brain Tumor Classification Using Efficient Deep Features of ...

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International conference on Wavelet Active Media Technology and Information processing (ICWAMTIP)

作者： Ahmad Neyaz Khan Habibeh Nazarian Noorbakhsh Amiri Golilarz Abdoljalil Addeh Jian Ping Li Ghufran Ahmad Khan MITS A.P. India Yasuj Branch Islamic Azad University Yasuj Iran School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Electrical and Computer Engineering Babol Noshirvani University of Technology Babol Iran School of Information Science and Technology Southwest Jiaotong University Chengdu China

ISBN: (数字)9781665405058

ISBN: (纸本)9781665405065

Brain cancer is a life-threatening disease and hampers the normal operation of the human body. For correct diagnosis and methodical treatment planning, it is essential to detect the brain tumor in initial stages of development. this study proposes an intelligent diagnostic method for early detection of brain tumor. In the developed method, the deep features of magnetic resonance imaging (MRI) scans are used as the input of support vector machine (SVM). In the first step of the proposed method, Grab cut method is applied for segmenting tumor region, then the segmented images are fed to convolutional neural network (CNN) for deep feature extraction. Following feature extraction module, minimum Redundancy Maximum Relevance (mRMR) algorithm is used to select the most efficient deep features. Finally, the selected deep features are fed into SVM in the classification module. In addition, we applied the black widow optimization algorithm (BWOA) for optimal tuning of hyperparameters of CNN and SVM. the developed method applied on BraTS 2015 datasets and the obtained results showed that the developed method is effective and may be employed in computer-aided diagnosis systems to classify the type of tumor.

关键词： Tumors Feature extraction Support vector machines Magnetic resonance imaging Classification algorithms Optimization image segmentation

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Self-supervised fast reconstruction method for diffusion MRI based on multi-directional assistance

Self-supervised fast reconstruction method for diffusion MRI...

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International conference on Signal processing Proceedings (ICSP)

作者： Yuxin Wu Jian Zhang Lihui Wang Ying Cao Linhai Wu Yunjia Wu Chen Ye Yuemin Zhu State Key Laboratory of Public Big Data College of Computer Science and Technology Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Guizhou University Guiyang China INSA Lyon CNRS Inserm CREATIS UMR 5220 U1206 Univ Lyon France

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Diffusion MRI (dMRI) is widely used for the detection and diagnosis of various pathologies due to its non-invasive nature. However, its signal acquisition process is relatively slow and susceptible to motions. In order to accelerate reconstruction process, many deep learning-based methods have been developed in recent years to achieve promising results, while most of them are supervised based methods and require fully sampled data which are difficult to obtain in practical applications to train the network. Self-supervised based methods were proposed to deal with this problem to a certain extent, however, they are devoted only to conventional MRI data, ignoring the correlation between the directions of dMRI data. To address this issue, we propose a self-supervised fast reconstruction method for dMRI based on multi-directional assistance. the method better utilizes the information between dMRI data directions by Partition-Replace operation, cleverly constructing data pairs and designing effective self-supervised strategy for bi-directional prediction, which can overcome the problem of data incompleteness and guide the model to reconstruct unsampled signals in spite of the difficulty of acquiring fully sampled data. Experimental results show that our method achieves better reconstruction in the absence of fully sampled data, even with an 8-fold acceleration factor.

关键词： Learning systems Pathology Correlation Magnetic resonance imaging Bidirectional control Reconstruction algorithms Signal processing Predictive models Data models

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