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

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Research on Smoke image Recognition: Multi-Scale Dilated Convolutional Neural Network and Entropy Function Threshold

Research on Smoke Image Recognition: Multi-Scale Dilated Con...

引用

Signal processing, Communications and Engineering Management conference (ISPCEM), International

作者： Yongmei Xiao Xing Wang Officers College of PAP Chengdu China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

For the application of artificial intelligence in image-based smoke detection in emergency management, this paper proposes an image recognition algorithm based on a multi-scale dilated convolutional neural network and entropy threshold function to prevent the loss of small-scale features, low detection rate, and poor real-time performance of smoke image recognition. First, the smoke features are determined according to a multi-scale analysis model, and multi-scale information such as texture, color, and shape of the smoke image is obtained. Second, we propose a dilated convolutional neural network to solve the feature loss. Third, according to the characteristics of the entropy function, we build a model that comprehensively considers smoke features and recognition thresholds, which more accurately describes the image recognition process. Ultimately, this research develops a smoke detection system using the suggested algorithm and demonstrates its efficacy through simulation and experimental validation.

关键词： image recognition Shape image color analysis Engineering management Signal processing algorithms Signal processing Feature extraction Entropy Real-time systems Convolutional neural networks

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Automated Dengue Recognition and Analysis through image processing and Machine Learning

Automated Dengue Recognition and Analysis through Image Proc...

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

作者： Preethi N Patil Usha J Andhe Dharani Department of MCA RV College of Engineering Bangalore India

Detecting dengue fever using image processing techniques typically involves the analysis of medical images such as blood smears or tissue samples. Dengue is a viral disease transmitted by Aedes mosquitoes, and its diagnosis primarily relies on clinical symptoms, serological tests, and molecular assays. image processing can be used as a complementary tool to assist and identify the dengue virus or related disparities in the body. Dengue fever, alternatively known as break-bone fever, is a lifethreatening arboviral disease caused by the DENV virus. It is a major global health concern with an increasing incidence worldwide. The existing clinical methods for diagnosing dengue are manual, requiring significant time and labor. The present work aims to develop an automated system using machine learning and digital image analysis to detect dengue infection from blood smear images. The custom algorithms are designed to extract platelets, red blood cells, and white blood cells, and train a feature vector. By automating the analysis of blood smear images, aids healthcare professionals detect cases of dengue more efficiently, allowing for early treatment and potentially saving lives. However, it is crucial to address data quality, algorithm selection, validation, interpretability, and ethical considerations throughout the development process.

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Algorithm of Palm Vein image Selection for Biometric Authentication systems Based on Multidimensional Markov Chain

Algorithm of Palm Vein Image Selection for Biometric Authent...

引用

Digital Signal processing and its Applications (DSPA), International conference on

作者： Natalya Kharina Anton Zemtsov Sergei Chernyadyev Department of the Radio Electronic Means Vyatka State University Kirov Russia Institute of Mathematics and Information Systems Vyatka State University Kirov Russia

Biometric palm vein authentication becomes more popular in the industry. This paper proposes an algorithm for palm vein image selection. The main idea of this method is an approximation of biometric template image through discrete Markov process and applying the theory of conditional Markov process to develop the algorithm, which allows to fully utilize the statistic redundancy of the biometric template. Given method can be effectively used after performing a thresholding process on an image, using the dynamic threshold value. CASIA MS Palmprint V1 Database dataset was used to present the examples of algorithm implementation. Morphological dilation and Zhang-Suen algorithms were used to evaluate and compare the effectiveness of the proposed palm vein image thinning algorithm. The results showed that the proposed method makes it possible to compute an image that preserves the vein thickness and closely matches the real vein pattern and is effective when using a template matching algorithm based on perceptual hash functions.

关键词： Thresholding (Imaging) Biometrics (access control) Veins Heuristic algorithms Redundancy Signal processing algorithms Authentication

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Relative Gradient Matching Cost for Mitigating Feature Discrepancies in Stereo Matching 5

Relative Gradient Matching Cost for Mitigating Feature Discr...

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5th International conference on High Performance Big Data and Intelligent systems, HDIS 2023

作者： Liu, Changlin Zhang, Yuan Sun, Linjun Zhang, Kaijie Cao, Bing Li, Zhiwei School of Semiconductor Science and Technology South China Normal University Guangzhou China School of Information Science and Technology Beijing Forestry University Beijing China AnnLab Institute of Semiconductors Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350394160

In stereo matching, perspective differences in images can cause feature inconsistencies. Traditional stereo matching algorithms compare pixel disparities using local parallel windows transformed by matching costs. However, this method under the fronto-parallel windows assumption may produce incorrect matching results when there are perspective differences in the feature scale. To address this issue, we propose a new cost computation method called relative gradient matching cost (RGC). RGC effectively handles feature perspective differences by robustly constraining feature matching relationships across varying object surface depths, simulating the effect of slanted support windows. We propose different improvement strategies for common traditional methods and DNN-based methods combined with RGC. In experiments, we compare the performance of RGC with some SOTA models. The experiment results demonstrate that RGC significantly improves the accuracy and stability of stereo matching, especially in cases with edge and discontinuity areas. ©2023 IEEE.

关键词： Stereo image processing

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Optimal Digital Algorithm of a Cognitive Synthetic Aperture Radar Operation 12

Optimal Digital Algorithm of a Cognitive Synthetic Aperture ...

引用

12th IEEE International Scientific Technical conference on Dependable systems, Services and Technologies (DESSERT) - Dependable and Trustworthy AI, IoE, Big Data Analytics for a Safe and Secure World

作者： Zhyla, Semen Volosyuk, Valeriy Pavlikov, Vladimir Vlasenko, Dmytro Mazurenko, Oleksandr Lisohorskyi, Bohdan Natl Aerosp Univ Aerosp Radioelect Syst Dept Kharkiv Aviat Inst Kharkiv Ukraine Kharkiv Natl Air Force Univ Chief Marshal Aviat I Fac Radiotech Air Def Forces Kharkiv Ukraine

ISBN: (纸本)9798350333046

The problem of optimal estimation of the complex scattering coefficient of the underlying surface in a digital radar with cognitive signal processing is considered. It is shown that it is possible to develop new algorithms not only in a heuristic way by combining together all the necessary structural components of a cognitive radar. To test the new approach the formation of an optimization problem for the synthesis of the optimal algorithm for the secondary filtering of the radar image of the surface was performed, and a solution was given. On the one hand the obtained algorithm corresponds to the operation of the extended Kalman-Bucy filter, and on the other hand, it has implementation features specifically for the task of terrain radar imaging. Based on the results of the analysis of the obtained expressions a structural diagram of the cognitive radar was synthesized.

关键词： cognitive radars optimal signal processing secondary processing adaptive receiver

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A Hybrid Density-Based Clustering Pipeline for Track Reconstruction

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

引用

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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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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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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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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