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

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Deep Learning based Parts Classification in a Cognitive Robotic Cell System 26

Deep Learning based Parts Classification in a Cognitive Robo...

引用

26th International conference on System Theory, Control and Computing (ICSTCC)

作者： Rosioru, Sabin Stamatescu, Grigore Stamatescu, Julia Fagarasan, Ioana Popescu, Dan Asti Automat Bucharest Romania Univ Politehn Bucuresti Automat & Ind Informat Bucharest Romania

ISBN: (数字)9781665467469

ISBN: (纸本)9781665467469

Advanced manufacturing systems increasingly rely on intelligent algorithms to discriminate, model and predict system behaviours that lead to increased productivity. Edge intelligence allows the industrial systems to collect, compute and act based on process data while reducing the latency and cost associated to an hierarchical control system in which complex decisions are generated in the upper layers of the automation hierarchy. Greater local computing capabilities allow the online operation of such algorithms while accounting for increased performance requirements and lower sampling periods of the control loops. In this work we present the concept of a cognitive robotic cell that collects, stores and processes data in situ for enabling the control of a robotic arm in a production setting. The main features that characterise the robotic cell are embedded computing, open interfaces, and standards-based industrial communication with hardware peripherals and digital twin models for validation. An application of part classification is presented that uses the YOLOv4 image processing algorithm for real-time and online assessment that guides the control of an ABB IRB120C robotic arm. Results illustrate the feasibility and robustness of the approach in a real application. Quantitative evaluation underlines the performance of the implemented system.

关键词： robot control deep learning part classification YOLOv4 image processing

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Transform-domain image Enhancement for Underwater Visual SLAM based on Homomorphic Filter

Transform-domain Image Enhancement for Underwater Visual SLA...

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

作者： Shuo Zhang Chaoyu Zhang Wei Wang Xinrui Zeng Bin Luo State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China China Nuclear Power Operation Technology Corporation LTD Wuhan China CNNC Engineering Research Center of Nuclear Industry Special Robot Wuhan China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Underwater visual SLAM algorithms play a vital role in autonomously localizing Autonomous Underwater Vehicles (AUV) in challenging underwater environments. The low-light conditions underwater necessitate the use of artificial lighting sources. However, this can lead to issues such as overexposed or underexposed images, resulting in reduced image contrast and information content. This study introduces a transform-domain image enhancement technique aimed at bolstering the resilience of underwater visual simultaneous localization and mapping (SLAM) systems. Specifically, a homomorphic filtering method is implemented to alleviate the impact of low-frequency lighting variations on images, thereby enhancing overall image clarity and bolstering the performance of visual SLAM systems. The efficacy of this filtering approach is validated through experiments conducted on the AQUALOC dataset. Our findings demonstrate that this method enhances the success rate of visual SLAM tracking, thereby enhancing its utility in AUV localization tasks.

关键词： Location awareness image quality Visualization Autonomous underwater vehicles Simultaneous localization and mapping Filtering Filtering algorithms Feature extraction Artificial light image enhancement

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Optimizing FPGA Hardware Design for Temporal Filtering of High-Definition Video

Optimizing FPGA Hardware Design for Temporal Filtering of Hi...

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International conference on Computers, Communications, and systems (ICCCS)

作者： Xingjian Fu College of Electrical and Electronic Engineering University of Nottingham Nottingham United Kingdom

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

In daily life, noise reduction is an inevitable aspect of video or image processing because noise can affect the subjective quality of viewing. Therefore, video denoising filtering is a crucial operation in video image processing. Among various denoising methods, motion-compensated temporal filtering is a typical and effective filtering technique. However, there is still room for further research in the hardware implementation of this method. Hence, this paper investigates the Multi-hypothesis Motion-Compensated Filter algorithm and proposes an efficient hardware structure for the filtering algorithm. By parallelizing or pipelining various modules, hardware efficiency is effectively improved. By FPGA evaluation, the final experiments demonstrate that the hardware structure for motion-compensated temporal weighted filtering presented in this paper can meet the requirements of video denoising filtering with a resolution of 1920xl080@60fps.

关键词： Filtering Communication systems image processing Noise reduction Noise Filtering algorithms Hardware

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A Comprehensive Analysis of Dithering algorithms and GPU Implementations

A Comprehensive Analysis of Dithering Algorithms and GPU Imp...

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

作者： Radhika V. Kulkarni Aaditya Deshpande Pratik Dagale Devyani Manmode Department of Computer Engineering Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

This research paper conducts a thorough comparative analysis of various dithering algorithms, prioritizing the GPU-optimized Floyd-Steinberg algorithm. Dithering plays a crucial role in improving image quality in constrained display scenarios. The study evaluates popular algorithms, including Bayer matrix, Ordered dithering, and Error Diffusion, while specifically exploring enhancements in the Floyd-Steinberg algorithm through GPU optimization. Quantitative metrics such as MSE, PSNR, and SSI are employed, alongside subjective assessments, to gauge algorithmic performance. The research delves into adaptability across color spaces and image types, providing insights for algorithm selection. Emphasizing real-time applications, the study offers developers valuable guidance in choosing efficient dithering techniques. The GPU-optimized Floyd-Steinberg algorithm emerges as a promising solution, showcasing its potential for accelerated computing environments and resource-intensive tasks.

关键词： image quality Measurement image color analysis Graphics processing units Focusing Parallel processing Real-time systems

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Learning-based fast image enhancement for driving scenes 6

Learning-based fast image enhancement for driving scenes

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6th CAA International conference on Vehicular Control and Intelligence, CVCI 2022

作者： Dong, Mingwei Liu, Runwu Kong, Huifang School of Electrical Engineering and Automation Hefei University of Technology Hefei China

ISBN: (数字)9781665453745

ISBN: (纸本)9781665453745

Visual sensors such as cameras can obtain rich image and video information, which is one of the most effective and lowest cost perception sensors for autonomous driving. However, when the environmental conditions change rapidly, the quality of acquired images decreases, resulting in various deep learning algorithms embedded in the camera may be difficult to complete object detection and other tasks. Although some existing image enhancement methods can mitigate the effects of environmental factors, these methods often involve complex calculations that ignore the importance of processing speed in the autonomous driving field. To solve these problems, double-branch image enhancement network is proposed in this paper, which includes low resolution branch and full resolution branch. With low resolution branch, local features of image can be obtained at a small computational cost. With full resolution branch, the details which are lost due to down-sampling are obtained. The enhanced image is obtained by fusing the feature graphs of two branches. The test result on BDD100K shows that our method has higher image processing speed on the premise of ensuring image quality. © 2022 IEEE.

关键词： image enhancement

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Calculation of a phase diffractive optical element that forms a given set of spheroidal functions 8

Calculation of a phase diffractive optical element that form...

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8th International conference on Information Technology and Nanotechnology, ITNT 2022

作者： Krupnikov, Andrey Kirilenko, Mikhail Samara National Research University Samara Russia Samara National Research University Samara Russia Image Processing Systems Institute RAS Branch of the FSRC 'Crystallography and Photonics' RAS Samara Russia

ISBN: (数字)9781665485715

ISBN: (纸本)9781665485715

We consider spheroidal functions calculated by the method of solving the equation for eigenvalues and eigenvectors of the finite Fourier transform. With the help of iterative algorithms, a phase-only diffractive optical element was calculated to form a superposition of eigenmodes of the optical system, which are spheroidal functions. © 2022 IEEE.

关键词： Optical systems

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International conference on Advances in Data Science and Computing Technologies, ASDC 2022

International Conference on Advances in Data Science and Com...

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International conference on Advances in Data Science and Computing Technologies, ASDC 2022

ISBN: (纸本)9789819936557

The proceedings contain 77 papers. The special focus in this conference is on International conference on Advances in Data Science and Computing Technologies. The topics include: Effect of Dropout on Convolutional Neural Network for Hyperspectral image Classification;state Derivative Optimal Control Law for Submersible Autonomous Robotic Vehicle in Steering Plane;ASR for Indian Regional Languages Using Fine-Tuned Wav2Vec2 Model;Intelligent Online Voting System for Twenty-First Century and Smart Cities 5.0: An Empirical Approach through Blockchain with ML Techniques;an Empirical Study on Credit Risk Assessment Using Ensemble Classifiers;audio Based Text Summarization Using Natural Language processing;blockchain-Enabled Security in Vehicular Ad Hoc Network;stock Selection Using Ontological Financial Analysis;Prediction of Future Career Course of Students Through RF Algorithm;components of Information Diffusion and Its Models in Online Social Networks;a Comparative Study;development of a Residual Neural Network Architecture for the Detection of Diabetic Retinopathy in Retinal Fundus images;prediction Markets Using Machine Learning;evaluation of Optimum Batting Order Based on Partnership Analysis Using Ant Colony Optimization Technique;identification of Rock images in Mining Industry: An Application of Deep Learning Technique;evaluation of Teachers’ Performance Using Fuzzy Inferences System;a Detailed Analysis on Intrusion Detection systems, Datasets, and Challenges;object Detection Under Low-Lighting Conditions Using Deep Learning Architectures: A Comparative Study;terahertz image processing: A Boon to the Imaging Technology;developing a Talent Identification Model for Predicting Player Position in Football Using Machine Learning algorithms;Survey of Various Machine Learning Techniques for Analyzing IoMT-Based Remote Patient Monitoring System;PSO-Based Traffic Signals in a Real-World City.

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Advancements in Hyperspectral image processing Using Machine Learning

Advancements in Hyperspectral Image Processing Using Machine...

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Information Technology, Electronics and Intelligent Communication systems (ICITEICS), IEEE International conference on

作者： Kasarapu Ramani P Yukitha Sake Karthik Karapakula Aravind Chowdary Mandapalli Akash Dept of Data Science Mohan Babu University(Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Department of CSSE Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

In several domains, such as remote sensing, agriculture, and environmental monitoring, hyperspectral image processing is essential. In this work, the Indian Pines dataset is used to investigate hyperspectral picture classification. Five machine learning algorithms—Support Vector Machines (SVM), Random Forest (RF), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Extreme Gradient Boosting (XGBoost) are assessed for performance. It improves classification accuracy for preprocessing methods such as feature extraction, PCA and noise reduction. The effectiveness of each method is evaluated using performance indicators such as kappa coefficient, average accuracy, and overall accuracy. Our results highlight how well XG Boost performs in obtaining the maximum accuracy of 96.12% when compared to other models.

关键词： Support vector machines Accuracy Machine learning algorithms Vectors Classification algorithms Convolutional neural networks Environmental monitoring

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Liver Disease Prediction using various image processing Techniques and Machine Learning algorithms

Liver Disease Prediction using various Image Processing Tech...

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Software, systems and Information Technology (SSITCON), International conference on

作者： M. S Saravanan Siva Rama Krishna Prasad S Dhanalakshmi Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of IT Guru Nanak Institutions Technical Campus Ibrahimpatnam India Department of Bioinformatics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

Predicting liver disease usually entails estimating a person’s chance of contracting ailments related to the liver using a variety of techniques. The prediction is made using the Novel Decision Tree algorithm and the Naive Bayes algorithm. From CHAOS Grand challenge webpage, data collected for liver images. Decision tree classifiers are compared to Naive Bayes classifiers for their accuracy and precision in diagnosing liver disease in patients. When it comes to predicting liver illness, the Naive Bayes classifier is $75.15 \%$ accurate, whereas the Novel Decision tree classifier is $80.03 \%$ accurate. 0.022 is the most significant number. g-power is used to calculate the sample size for two group at 16. The 95 percent alpha value for the g power is 0.05. The prediction of liver disease at early stage achieved by comparing Novel Decision Tree to Naive Bayes, Novel Decision Tree outperforms the latter in terms of precision and accuracy.

关键词： Accuracy Machine learning algorithms Liver diseases image processing Software algorithms Prediction algorithms Software systems Naive Bayes methods Decision trees Information technology

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Metrological characterization of different methods for recovering the optically sectioned image by means of structured light 12

Metrological characterization of different methods for recov...

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conference on Optical Measurement systems for Industrial Inspection xii

作者： Martinez, P. Bermudez, C. Carles, G. Cadevall, C. Matilla, A. Marine, J. Artigas, R. Sensofar Tech SL Barcelona Spain

ISBN: (数字)9781510643994

ISBN: (纸本)9781510643994

Imaging confocal microscopy (ICM) and focus variation (FV) are two of the most used technologies for 3D surface metrology. Both methods rely on the depth of focus of the microscope objective, which depends on its numerical aperture and wavelength of the light source to compute an optical section. In this paper we study how several methods of structured illumination microscopy affect the metrological characteristics of an areal optical profiler. We study the effect of the projection of different structured patterns, the sectioning algorithms, and the use of high and low frequency components onto the optically sectioned image. We characterized their performance in terms of system noise, instrument transfer function and metrological characteristics such as roughness parameters and step height values.

关键词： Focus Variation Confocal Optical sectioning Structured light

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