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

共 13664 条记录，以下是1261-1270 订阅

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System-level design for DPCM image Compression with SoC-FPGA Accelerator

System-level design for DPCM Image Compression with SoC-FPGA...

引用

Canadian conference on Electrical and Computer Engineering (CCECE)

作者： Amin Safaei Samira Sabeti Morteza Masoudian Department of Computer Science Lakehead University Thunder Bay ON Canada

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

The implementation of data compression in embedded platforms and portable systems is growing rapidly due to the demands for image and video processing in daily human life. Among the data compression applications, medical imaging, such as Computed Tomography (CT), and Light Detection And Ranging (LiDAR) have become active research areas, especially when data transmissions must be performed over a communication *** of the complexity of data compression algorithms and the high resolution of input streams, current embedded platforms may not be able to fully exploit data compression algorithms. To address this restriction, recent approaches utilize hardware accelerators to speed up the computation. Among available hardware accelerators, system-on-a-chip field-programmable gate arrays (SoC-FPGAs) have emerged as an important architecture approach in terms of achieving satisfactory computational performances. This study presents a hardware accelerator for the computation of a differential pulse code modulation (DPCM) algorithm implemented and synthesized on a Zynq SoC-FPGA and achieving an acceleration factor of 88x.

关键词： image coding Laser radar Computed tomography Data compression Computer architecture Parallel processing Pulse modulation

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A Conceptual Framework for Addressing Class Imbalance in image Data: Challenges and Strategies

A Conceptual Framework for Addressing Class Imbalance in Ima...

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International IEEE conference on Intelligent systems, IS

作者： Saara Asif Muhammad Uzair Akmal Leonid Koval Selvine G. Mathias Simon Knollmeyer Daniel Grossmann AIMotion Technische Hochschule Ingolstadt Ingolstadt Germany Faculty of Computer Science and Data Processing Technische Hochschule Ingolstadt Ingolstadt Germany

ISBN: (数字)9798350350982

ISBN: (纸本)9798350350999

The presence of class imbalance, denoting a dis-proportionate distribution of class instances in a dataset, has emerged as a significant challenge in the era of Deep Learning (DL) where models crave abundance in data. This issue is pervasive in various real-world applications, where certain classes exhibit limited data representation. This problem is frequently encountered when dealing with image data, which exhibits an imbalanced distribution, with one class significantly outnumbering the others. Failing to address class imbalance introduces bias in machine learning and deep learning models, favoring the majority classes and leading to subpar performance for the minority classes. This research specifically delves into the recurrent problem in the context of image data, that is “class imbalance”. The research comprehensively explores the existing challenges based on data pre-processing, algorithmic techniques, hybrid methodologies, and state-of-the-art solutions.

关键词： Deep learning Accuracy Machine learning algorithms Merging Generative adversarial networks Data models Intelligent systems

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Automated Defect Detection and Segregation in Dyed Fabrics Using image processing Techniques

Automated Defect Detection and Segregation in Dyed Fabrics U...

引用

Electronics and Sustainable Communication systems (ICESC), 2020 International conference on

作者： K. R. Prabha Aathavan. V Harshit. K Indhumathi. K Department of ECE Sri Ramakrishna Engineering College Coimbatore India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

This study addresses the critical need for efficient quality control in textile manufacturing, aiming to enhance the production of high-quality fabric to meet consumer demands and industry benchmarks. Despite the meticulous cleaning and dyeing processes involved in fabric production, there remains a challenge in identifying and segregating defective sections, which can potentially compromise the overall quality of the fabric. To overcome this challenge, this research study proposes an innovative solution: Automated Quality Control in Textile Manufacturing, utilizing advanced image processing techniques. The proposed system employs high-resolution cameras and image processing algorithms to analyze the fabric's surface in real-time. By leveraging predefined criteria and machine learning models, the system can accurately detect and isolate flawed sections within the fabric. These criteria encompass a range of defects, including stains, discolorations, or irregular patterns that deviate from established quality standards. This approach improves quality control in textile manufacturing, providing manufacturers with a reliable and efficient means of identifying and addressing defects in fabric production. By automating the quality control process, manufacturers can enhance productivity, reduce waste, and ultimately deliver higher-quality products to consumers.

关键词： Productivity image processing Process control Quality control Fabrics Real-time systems Manufacturing Reliability Surface treatment Standards

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17th Asian conference on Intelligent Information and Database systems, ACIIDS 2025

17th Asian Conference on Intelligent Information and Databas...

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17th Asian conference on Intelligent Information and Database systems, ACIIDS 2025

ISBN: (纸本)9789819660049

The proceedings contain 29 papers. The special focus in this conference is on Intelligent Information and Database systems. The topics include: GAMER-Pong: Game Adjustment by Monitoring Emotional Response;Machine Learning algorithms Comparison for Hand sEMG-Recorded Movements Classification;boosting Face Super-Resolution and Identification with Knowledge Distillation and Frequency Domain Loss;improving Efficiency image Captioning by Using Attention Mechanism Combined with Knowledge Graph;integrating Topological Data Analysis and Deep Learning: A Case Study in Cardiovascular Disease Prediction at Thu Duc Hospital;boosting Lightweight Multi-branch Network by Integrating Segment, Skeleton, and Attribute Information;continuous Recognition of Mouth Patterns in Japanese Sign Language for Visual Communication;Towards an UAV Visual Navigation System Based on Map processing and Spatial Matching Techniques: A Literature Review;image Enhancement with Boosted Schrödinger Bridge;kernel-Level Energy-Efficient Neural Architecture Search for Tabular Dataset;estimation of Distribution algorithms with Overlapped Subpopulations;energy-Efficient Edge Query processing for Smart City Using Query Prediction;influence Maximization with Fairness Cost on Groups in Online Social Networks;TMMP: Efficient Continual Learning via Mixture of LoRA Experts and Top Maximum Magnitude Parameter Fusion;ADMEdge: Accelerating Edge Computing Through Co-design for Enhanced Data Mobility;the Recursive Scheme of Clustering;TOPSIS-Inspired Socio-cognitive Mutation Operator for Metaheuristics;feature-Based Drift Detection in Non-stationary Data Streams Using Multiple Classifiers: A Comprehensive Analysis;a Feature Transformation Technique for Improving Ensemble Learning systems;explainable Artificial Intelligence to Improve Interpretability in Predictive Mutation Testing;an Analysis of Class Imbalance Challenges in Predictive Mutation Testing;efficient Vietnamese Name Detection Using Highly Discriminative N-Gra

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A Comprehensive Survey of AI-Driven Biomedical image processing for Intracerebral Hemorrhage Detection and Classification: Current Trends, Challenges, and Future Directions

A Comprehensive Survey of AI-Driven Biomedical Image Process...

引用

International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： P. Raguraman M. Kumaresan S. Ramesh Department of Computer Science and Engineering Jain (Deemed-to-be University) Bangalore Karnataka

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Amongst many life-threatening conditions, a fatal condition is the Intracerebral hemorrhage (ICH). It may result in death if not treated immediately and hence necessitates the need for accurate diagnosis for effective treatment. In recent times, concepts of Artificial intelligence (AI), have emerged as a transformative tool in medical image processing, offering faster and more reliable ICH detection and classification. This research study reviews the current advancements in AI-driven biomedical image processing for ICH, exploring various state-of-the-art models involving Convolutional Neural Networks (CNNs), variants of CNN with more efficient feature extraction procedures, Long Short-Term Memory (LSTM) networks, and optimization methods like Genetic algorithms (GA), Ant Colony Model (ACO), rParticle Swarm Optimization (PSO). Additionally, challenges such as data scarcity, model interpretability, and generalization across diverse populations are discussed. The paper highlights future research directions, including the need for explainable AI, multimodal data integration, and real-time, low-cost AI solutions for resourceconstrained environments. By addressing these challenges, AI has the potential to revolutionize ICH detection and improve patient outcomes globally.

关键词： Explainable AI Computational modeling Biological system modeling Data models Real-time systems Biomedical image processing Convolutional neural networks Hemorrhaging Long short term memory Genetic algorithms

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Facial Features based Recognition System using LBPH and AdaBoost

Facial Features based Recognition System using LBPH and AdaB...

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Smart Electronics and Communication (ICOSEC), International conference on

作者： Mayank Srivastava Kalash Khatiyan Pratibha Dixit Department of Computer Engineering & Applications GLA University Mathura UP India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Face recognition stands as a pivotal domain within computer vision, boasting myriad applications in security, identification, and biometrics. This research study proposes a face recognition system using image processing techniques with the combination of two algorithms, Local Binary Pattern Histogram (LBPH) and AdaBoost. The LBPH algorithm was used to extract features from the images and create a feature vector for each face. The AdaBoost algorithm was then used to train a classifier on the feature vectors and classify new faces. After testing on the Face Recognition Database, the results reveal that the fusion of LBPH and AdaBoost algorithms achieves an impressive accuracy rate of 96.15% which is better as compared to other similar algorithms. The proposed algorithm is implemented in Python to recognize faces in a dataset of images.

关键词： Histograms Accuracy image recognition Law enforcement Face recognition Feature extraction Vectors Real-time systems Classification algorithms Testing

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HPDNET: Hyper Prior Dependent Demosaic Neural Network

HPDNET: Hyper Prior Dependent Demosaic Neural Network

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International image processing, Applications and systems conference (IPAS)

作者： Youngil Seo Sungho Jun Dongpan Lim Seongwook Song System LSI Division Samsung Electronics Yongin Republic of Korea

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Recent advancements in deep neural networks have shown remarkable improvements in image quality during the demosaicking process, surpassing conventional algorithms. However, these deep neural network techniques are often characterized by heavy computational requirements, rendering them unsuitable for deployment on resource-constrained platforms. This presents a critical challenge in the field of image demosaicking: while deep learning approaches excel in enhancing image quality, their computational intensity poses a significant hindrance to their wider adoption. Consequently, there is a pressing need for methodologies that can strike an optimal balance between achieving superior image quality and maintaining computational efficiency. In this work, we propose a new deep framework, hyper-prior dependent demosaic neural network, HPDNet that utilizes the significant concepts of the conventional algorithm and the characteristic of image sensor data. We designed the network that exploits three concepts, those are pixel gradient prior attention, phase separation, and multi-level sparse and dense feature extraction. We designed the deep neural network that extracts the optimal gradient prior and the multi-level extracted features are fused and attended by gradient prior. It can fully utilize spatially variant information. Also to make the network deployed in the mobile platform, we devised self-pruned image convolution that adopts image filter characteristic and reduce computations. Experiments show that proposed network outperforms SOTA demosaic networks both in terms of image quality and computation.

关键词： image quality Deep learning image sensors Artificial neural networks Pressing Feature extraction Rendering (computer graphics) Information filters Computational efficiency image restoration

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2nd International conference on Science, Engineering Management and Information Technology, SEMIT 2023

2nd International Conference on Science, Engineering Managem...

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2nd International conference on Science, Engineering Management and Information Technology, SEMIT 2023

ISBN: (纸本)9783031722837

The proceedings contain 46 papers. The special focus in this conference is on Science, Engineering Management and Information Technology. The topics include: Verification and Validation of Knowledge Engineering systems: A Life Cycle Framework;The Critical Factors of Success of Gamification in Digital Banking Services: Using Analytic Hierarchy Process (AHP) Approach;e-Shopping Sites Preference Analysis with Multi-criteria Decision-Making Methods;advancing Anemia Diagnosis: Harnessing Machine Learning Methods for Accurate Detection;monkeypox Detection with K-mer Using Machine Learning algorithms;cloud Computing Model for Handling Medical Big Data: A Mobile Hospital Pervasive Healthcare Application;Harnessing Advanced AI Techniques: An In-Depth Analysis of Machine Learning Models for Improved Diabetes Prediction;image processing in Toxicology: A Systematic Review;augmented Reality Immersive World with Hologram Special Effect in Early Childhood Education;improving the Visual Ergonomics of Computerised Workplaces Through the Use of Specialised Eye-Rest Software;a Diagnosis Model Based on Federated Learning for Lung Cancer Classification;Arrhythmia Detection from ECG Traces images Using Transfer Learning Approach;enhancing Traffic Flow Prediction in Urban Areas Through Deep Learning and Probe Information: A Comparative Study;an Approach to Multi-agent Deep Q-Network Optimization of Signal Control in Multi-intersection Road Environments to Enhance Urban Traffic Flow;blockchain-Driven Smart Contracts: An Overview of Application Areas and Gap Identification in Construction Management Literature;stochastic Optimization Methodology for Production Planning with Uncertain Demand and Lead Time Based on the Digital Twin;role of Top Management Commitment and Information Technology Investment in Digital Transformation;antecedents of Mobile Banking Apps Adoption Among Consumers in Ghana.

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17th Asian conference on Intelligent Information and Database systems, ACIIDS 2025

17th Asian Conference on Intelligent Information and Databas...

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17th Asian conference on Intelligent Information and Database systems, ACIIDS 2025

ISBN: (纸本)9789819660070

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Analysis of Decision Tree and Random Forest algorithms for Nutrient Deficiency Classification in Citrus Leaves through image processing

Analysis of Decision Tree and Random Forest Algorithms for N...

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International conference on Telecommunication systems, Services, and Applications (TSSA)

作者： Lia Kamelia Rin Rin Nurmalasari Electrical Engineering Department UIN Sunan Gunung Djati Bandung Bandung Indonesia

The research presents a hybrid approach to identify and categorise nutritional deficiency syndrome in citrus leaves using image processing and machine learning. The method includes processing images, segmenting images and extracting color-texture information. The Decision Tree and the Random Forest algorithms use various statistical measures for classification, including mean, skewness, variance, entropy, maximum probability, angular second moment, contrast, correlation, dissimilarity, energy, and homogeneity. The test results show a range of performance levels, with an accuracy of 80.28% being attained by utilising an 80:20 data ratio in the Decision Tree approach. The Random Forest method’s highest accuracy, 91.25%, was reached using an 80:20 ratio. In conclusion, the accuracy of the Random Forest approach regularly beats that of the Decision Tree method.

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