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

共 14697 条记录，以下是1211-1220 订阅

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A Hybrid Density-Based Clustering Pipeline for Track Reconstruction

A Hybrid Density-Based Clustering Pipeline for Track Reconst...

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image processing, Computer Vision and Machine Learning (ICICML), International conference on

作者： Bijia You Zhiyun Xia School of Computer Science Beijing University of Posts and Telecommunications Beijing China School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

In high-energy physics, the capability to accurately and efficiently track charged particles is essential for effective data analysis. This article introduces an innovative density-based clustering pipeline intended for the track reconstruction task, incorporating Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and Ordering Points To Identify the Clustering Structure (OPTICS) algorithm. Results on simulated data suggest that the proposed method offers improvements in both effectiveness and robustness compared to traditional techniques, with performance on par with state-of-the-art neural network-based approaches. Furthermore, this pipeline demonstrates significant potential for real-time applications in high-energy physics experiments, offering a scalable and robust solution.

关键词： Machine learning algorithms Pipelines Noise Clustering algorithms Optics Robustness Real-time systems Pattern recognition Trajectory image reconstruction

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

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

引用

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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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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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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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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image Segmentation based Approach for Skin Disease Detection and Classification using Machine Learning algorithms

Image Segmentation based Approach for Skin Disease Detection...

引用

Integrated Circuits and Communication systems (ICICACS), IEEE International conference on

作者： Nazmul Hasan Sany Pintu Chandra Shill Dept. of Computer Science and Engineering Khulna University of Engineering & Technology Khulna Bangladesh

Skin disorders are ubiquitous, stemming from causes including fungus, germs, allergies, and viruses. Although laser and photonics medical technologies have revolutionised rapid and precise diagnosis, their exorbitant prices restrict accessibility. To address this, this paper describes an image processing strategy for dermatological screening and procedure that comprises obtaining digital photographs of damaged skin patches and applying image processing to detect the disease type. Moreover, when conducted manually by medical specialists, identifying skin illnesses is time-intensive and subjective. As a consequence, both patients and physicians want automated skin disease prediction, which makes the treatment plan faster. This cost-effective approach depends on a basic setup: a camera and a computer. Operating on colour photos, a solution combines resizing for feature extraction using a pertained convolutional neural network, followed by classification using a multiclass SVM. The technology efficiently recognises four separate skin illnesses with an astonishing 90.8% accuracy, giving users complete findings spanning disease kind, extent, and severity.

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Unsupervised domain adaptation in human detection for various human pose

Unsupervised domain adaptation in human detection for variou...

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International image processing, Applications and systems conference (IPAS)

作者： Benoit Lagadec Ekaterina Kostrykina Esifrance Le Cannet France Universite Cote d’Azur Nice Cedex 2 France

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

In human detection algorithms, some recurrent issues are still challenging. Indeed, in a real application we have a lack of various human poses in our data. For example, in video surveillance it could be a significant issue to forget detecting a human in uncommon pose. We propose a compensation to these less frequent cases. We introduce a detection in rotated view to compensate limited various poses in our dataset. Instead of introducing a generative enhancement for every human body pose, which could be exhausting. Indeed, data annotation has become a difficulty for many applications. Moreover, a simple data augmentation is not efficient to generalize our training. We need a strategy to learn this new poses. So, we will introduce a process to compute pseudo labels in a sequence of rotated views. It avoids to use a clustering algorithm like dbscan [Chen et al.(2021)Chen, Lagadec, and Bremond.] or K-means to compute a pseudo label. A summary of theoretical results is described in a first section to illustrate our baseline. First of all, we will illustrate how we process pseudo label on various views by two mechanisms which have been called relaxation and scanning. We introduce a sequence of rotations of original view, to process a detection in a compensated view. In second step, we will learn this distortion created by the various views and construct an approach to distill original knowledge to a more generalized *** main improvement of this approach is to propose an alternative solution at dbscan [Chen et al.(2021)Chen, Lagadec, and Bremond.] approach which is popular, but which is difficult to use in real applications. We obtain a significant result by improving accuracy in especially various pose datasets.

关键词： Training Accuracy Annotations image processing Clustering algorithms Data collection Video surveillance Distortion Data augmentation Detection algorithms

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A Fast Saturation Based Dehazing Framework with Accelerated Convolution and Attention Block

A Fast Saturation Based Dehazing Framework with Accelerated ...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Shuocheng Wang Jiaming Liu Yilian Zhong Ruoxi Zhu Jiazheng Lian Hao Zhang Yibo Fan State Key Laboratory of Integrated Chips and Systems Fudan University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Real-time image dehazmg is crucial for applications such as autonomous driving, surveillance, and remote sensing, where haze can significantly reduce visibility. However, many deep learning algorithms are hindered by large model sizes, making real-time processing difficult to achieve. Several fast and lightweight dehazing networks rely on estimating K(x), but they often fail to deliver satisfying performance. In this paper, we present a novel fast dehazing framework built upon the saturation-based algorithm. We design a new convolution module called Feature Extraction Partial Convolution (FEPC), which is faster and achieves better performance than the vanilla 3×3 convolution. Additionally, we fully leverage the information redundancy between feature map channels by dividing it into two parts along the channel dimension and designing a Self-Cross Attention Block (SCAB). The reduction in channel count significantly reduces computational load and improves the framework’s speed. Through extensive experiments, our method demonstrates not only a fast inference speed but also superior dehazing performance, providing a promising solution for real-time practical deployment. Our code will be available at https://***/superwscZFSB-Dehazing-Framework.

关键词： Deep learning image quality Convolution Surveillance Redundancy Signal processing algorithms Feature extraction Real-time systems Speech processing Remote sensing

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Implementation of Automated Forest Fire Detection System using IoT

Implementation of Automated Forest Fire Detection System usi...

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Applied Artificial Intelligence and Computing (ICAAIC), International conference on

作者： S Sujitha Harshika M Disha Aamna Nafiza V Hemavathi Department of EEE New Horizon College of Engineering Bengaluru India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

Forest fires pose a significant threat to ecosystems and wildlife, exacerbating global warming and environmental degradation. The contemporary challenge lies in the difficulty of timely detection by modern organizations. In response, the development of an automated forest fire detection system becomes imperative. The system involves the integration of various technologies, including Arduino microcontrollers, cameras, smoke detectors, flame detectors, and image processing algorithms. When a fire is detected, the system promptly sends alert messages to nearby forest departments, facilitating rapid response and mitigation efforts. This research work explores the design, implementation, and functionality of such an automated forest fire detection system, emphasizing the role of each component in enhancing the overall efficacy of early fire detection. The integration of Arduino technology, coupled with advanced sensors and image processing, exemplifies a multidimensional approach to addressing the pressing issue of forest fires.

关键词： Wildfires image processing Wildlife Prototypes Forestry Cameras Climate change Smoke detectors Global warming Environmental monitoring Autonomous systems Microcontrollers image sensors Alarm systems Internet of Things

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