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

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Robust Calibration for Enhanced Imaging in Near-Field D-Band MIMO Radar systems

Robust Calibration for Enhanced Imaging in Near-Field D-Band...

引用

European conference on Antennas and Propagation, EuCAP

作者： Lorena Perez-Eijo Marcos Arias Borja Gonzalez-Valdes José Vázquez-Cabo Ignacio Sardinero–Meirás Jesus Grajal AtlanTTic Research Center Universidade de Vigo Vigo Spain Information Processing and Telecommunications Center Universidad Politécnica de Madrid Madrid Spain

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

This work introduces a calibration algorithm designed to improve high-precision imaging in real-time MIMO radar systems. The algorithm applies the Inverse Fourier Transform (IFFT) and advanced signal processing techniques to efficiently extract and process key data from raw measurements, focusing on critical Regions Of Interest (ROI) while filtering out unnecessary information. A robust phase correction mechanism ensures precise calibration and alignment, preventing phase errors that could otherwise lower image quality. The algorithm provides an effective balance between computational efficiency and the high accuracy required by modern MIMO radars, paving the way for further improvements in real-time imaging.

关键词： MIMO radar Signal processing algorithms Coherence Radar imaging Signal processing Real-time systems Hardware Calibration Spatial resolution image reconstruction

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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...

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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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Ballistics Algorithm for Airborne Remote Sensor Position in Catastrophe Zones 2nd

Ballistics Algorithm for Airborne Remote Sensor Position in ...

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2nd International conference on Unmanned Aerial systems in Geomatics (UASG)

作者： Pawar, Vipinkumar R. Mande, Sudhakar Rizvi, Imdad Mumbai Univ Mumbai India Mumbai Univ Don Bosco Inst Technol Mumbai Maharashtra India Higher Inst Technol Sharjah U Arab Emirates

ISBN: (纸本)9783031193118;9783031193095;9783031193088

The configuration of the camera and Field Of View (FOV) in a closed range photogrammetry are the most important parameters to obtain images of the field in quality. UAVs are central Wide Range photogrammetry tools, with cameras for large field items, for obtaining the optimal standard of photos by offering different usable algorithms of flight planning geographic location. This paper suggests an algorithm to find the exact Geo-Coordinates for the location detection of the UAV flight planning algorithm for the application of Closed Range Photogrammetry to the Battle field Susceptibility Zone analysis. Terrain Slope and Aspect have been considered in order to maintain the measured angle of view, field of vision, and land surface image quality. Identifying the pitch and aspect terrain (Azimuth) using the Digital Elevation Model (DEM) ISRO CartoSat Remote sensing info. In order to improve three-dimensional landscape pictures for 3d modelling, Battle Field Analysis, this algorithm should maximize the functionality of the numerous usable UAV airplane planners.

关键词： ISRO DEM LISS iii LSI SI LRI OSM NDVI VI

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Research on Efficient image Feature Extraction based on Low-Rank Representation for Intelligent Reading systems

Research on Efficient Image Feature Extraction based on Low-...

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image processing, Electronics and Computers (IPEC), Asia-Pacific conference on

作者： Yulan Tan Heilongjiang University of Technology Jixi China

ISBN: (数字)9798350374407

ISBN: (纸本)9798350374414

Efficient and accurate extraction of image features is essential for ensuring high-performance information transmission in intelligent reading systems. This paper introduces a computer-assisted method for intelligent image feature extraction, specifically focusing on the Low-Rank Representation (LRR) approach. The study addresses challenges presented by noisy and diverse character or image data encountered in real-world scenarios. By employing LRR, the accuracy of image extraction, which includes object recognition and scene understanding, is significantly enhanced. Subsequently, the proposed techniques for intelligent image feature extraction undergo comprehensive evaluation, assessing both effectiveness and versatility through extensive experiment verifications. The experimental results validate the efficacy of the proposed method and demonstrate its superior image content extraction accuracy compared to existing methods. This proposed method provides crucial assistance to high-performance intelligent reading systems, thereby enhancing its applicability for potential consumers.

关键词： YOLO Visualization Accuracy Process control Feature extraction Rendering (computer graphics) Classification algorithms Data mining Proposals Principal component analysis

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image Quality Assessment for Computer Vision Based Perception algorithms Using Edge and Structural Features 38

Image Quality Assessment for Computer Vision Based Perceptio...

引用

38th IEEE Region 10 conference, TENCON 2023

作者： Akshaya, V.G. Rithika, S. Lanciya Angel, A. Amirtha Pravin, K.S.M. Balasubramanian, Karthi Srinivasan, Suganthi Rajegowda, Rakesh Bhattacherjee, Payal Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Electronics and Communication Engineering Coimbatore India R & D-Computer Vision Systems Valeo India Pvt.ltd Comfort Driving Assistance Group Chennai India

ISBN: (纸本)9798350302196

image Quality Assessment (IQA) is a critical task in image processing, computer vision, and other related fields, as it helps in evaluating the effects of various impairments on the Quality of Experience (QoE) of consumers. However, current IQA metrics may have limitations in evaluating image quality accurately for different types of images or under different conditions. To address this, a proposed approach involves calculating an overall image quality score based on linear combination of individual image quality metrics. This method considers multiple image features such as sharpness, contrast, color, and noise, and assigns weights to each feature based on its relative importance in determining overall image quality. By combining and weighting multiple image features, the proposed approach aims to provide a more comprehensive and accurate evaluation of image quality compared to using individual metrics alone. In this study, we focus on the importance of edge features and structure-based metrics in object detection and analyze existing No-Reference (NR) IQA metrics, including Just Noticeable Blur (JNB), Cumulative Probability Blur Detection (CPBD), Visual Quality Assessment (VQA), Blind/Referenceless image Spatial Quality Evaluator (BRISQUE) and No-Reference Low- Light image Enhancement Evaluation (NLIEE), and propose a linear combination formula that combines these metrics to evaluate image. We evaluate the performance of the proposed metric on several datasets by calculating the linear combination of two, three, and all the five metrics. The weights assigned to each metric in the linear combination formula are determined through experimental analysis. The combined metric scores and MOS scores are then used to compute the Spearman's rank order correlation coefficient, which measures the monotonic relationship between two variables. A higher SROCC value indicates a stronger correlation between the combined metric and MOS scores and is used to evaluate the performanc

关键词： image quality

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Enhanced K-means Color Clustering Based on SLIC Superpixels Merging incorporated within the Entomology Software: AInsectID

Enhanced K-means Color Clustering Based on SLIC Superpixels ...

引用

IEEE Workshop on Evolving and Adaptive Intelligent systems (EAIS)

作者： Haleema Sadia Parvez Alam School of Engineering The University of Edinburgh Edinburgh Scotland UK

ISBN: (数字)9798350366235

ISBN: (纸本)9798350366242

Superpixel-based segmentation is an important pre-processing step for the simplification of image processing. The subjective nature behind the determination of optimal cluster numbers in segmentation algorithms can result in either underor over-segmentation burdens, depending on the image type. Insect wings, with their intricate color patterns, pose significant challenges for the accurate capture of color diversity in clustering algorithms, assuming a spherical and isotropic cluster distribution is used. This paper introduces a hybrid approach for color clustering in insect wings, integrating the Simple Linear Iterative Clustering (SLIC) method to generate the initial superpixels, and a DeltaE 2000 function the precisely discriminated merging of superpixels. Color differences between superpixels serve to measure homogeneity during the merging process. The proposed new algorithm demonstrates enhanced segmentation as it overcomes the issue of over-segmentation and under-segmentation, as evidenced by the results derived from the Boundary Recall, Rand index, Under-segmentation Error, and Bhattacharyya distance using ground truth data. The Silhouette score and Dunn Index are also used to quantitatively evaluate the efficacy of our new proposed clustering technique.

关键词： image segmentation image color analysis Insects Merging Clustering algorithms Software Hybrid power 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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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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Research on Graph Neural Network Algorithm and image Recognition Application 20

Research on Graph Neural Network Algorithm and Image Recogni...

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20th International Computer conference on Wavelet Active Media Technology and Information processing, ICCWAMTIP 2023

作者： Xu, Jia-Hao Li, Jian-Ping School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China

ISBN: (纸本)9798350318982

The emergence of graph neural networks makes up for the limitations of traditional neural networks in dealing with complex graph data, and they are widely used in the fields of social networks and recommender systems, but at the same time, there are some defects in themselves. This paper introduces the basics of graph neural networks, and also summarizes the advantages and disadvantages of GSN, GraphSAGE, DeepWalk and Node2Vec algorithms. Finally, it also discusses the application of graph neural networks in the field of image recognition, summarizes its deficiencies in practical applications, and puts forward the prospect of the future development of graph neural networks. © 2023 IEEE.

关键词： image recognition

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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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