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检索条件"任意字段=Conference on Image Processing: Algorithms and Systems IX"

共 12844 条记录，以下是91-100 订阅

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Analysis of Duplicating the Human Mind Suspicions in AI in Various Divisions

Analysis of Duplicating the Human Mind Suspicions in AI in V...

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Multi-Agent systems for Collaborative Intelligence (ICMSCI), International conference on

作者： S. P. Santhoshkumar R. Karthika N. Vijayakumar S. Gajalakshmi K. Jayanthi S. Hariharasudhan Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Information Technology School of Computing Rathinam Technical Campus Coimbatore India Computer Science and Engineering Pollachi Institute of Engineering and Technology Pollachi Coimbatore India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Uncertainty in AI refers to the degree of confidence or probability associated with the accuracy or effectiveness of an AI system's output. It is a measure of how much the AI system is unsure about the correctness of its output or decision-making. Uncertainty can arise in AI systems due to a variety of factors, including the quality of input data, variations in data formats, variations in context or environment, and the complexity of the underlying algorithms. Different AI applications can have specific types of uncertainty associated with them, as we discussed in previous questions about uncertainty in AI image recognition, natural language processing, data extraction, expert systems, planning and optimization, and robotics. To mitigate uncertainty in AI systems, researchers and developers use various methods, such as incorporating more data and diverse inputs, improving the quality of input data, enhancing the algorithms, and using feedback mechanisms to learn from errors and improve the system over time. However, complete elimination of uncertainty is often challenging or impossible, and users should always be aware of the potential for errors or unexpected behaviour in AI systems. In this paper, analysis of artificial intelligence uncertainty in various fields.

关键词： Uncertainty Accuracy image recognition Predictive models Probabilistic logic Natural language processing Planning Artificial intelligence Robots Optimization

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

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

引用

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

ISBN: (纸本)9789819660070

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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FPGA-based image processing algorithm design 24

FPGA-based image processing algorithm design

引用

Proceedings of the 2024 International conference on image processing, Multimedia Technology and Maching Learning

作者： Yi Hu Benyuan Chen Hubei University of Technology Wuhan Hubei China

ISBN: (纸本)9798400713880

As one of the most important data sources in machine vision system, camera is facing increasingly strict requirements in video image acquisition, processing and transmission. Through the in-depth analysis of the development trend of the camera, it can be clear that its main pursuit of three goals: improve video image processing performance, reduce costs and enhance flexibility. However, most of the existing video processing platforms have problems such as low data processing efficiency and lack of real-time performance, which is difficult to meet the high performance requirements of modern cameras for video image processing. Because of its high real-time performance, powerful computing power, high integration and flexibility, embedded system has become a promising direction to realize advanced video image processing algorithms. With the embedded platform, the camera system can realize more efficient real-time data processing and higher quality video image output. In the current market, three high-performance embedded processors stand out, namely DSP, ASIC and FPGA. Among them, FPGA chip has become the ideal hardware platform for camera video image processing with its parallel computing capability, rich interface resources and field programmable characteristics. Based on Anlogic EG4S20 FPGA platform, this paper realizes image processing functions, including Bayer format to RGB conversion, gray world algorithm and perfect reflection algorithm. This research makes full use of the powerful computing power of FPGAs to demonstrate image processing and enhancement techniques to address key performance challenges in camera systems.

关键词： FPGA

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Smart Oral Health Monitor

Smart Oral Health Monitor

引用

Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International conference

作者： Spandana Anilkumar Kamkar Srushti Sanjay Vaishnavi P S Karthik S Dept. of Computer Science and Engineering BNM Institute of Technology Affiliated to VTU Bangalore India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

Oral health is vital to overall well-being but is often overlooked due to inefficient monitoring tools and delayed diagnosis. This project presents a smart handheld device featuring a miniaturized camera for detecting oral issues like plaque, cavities, teeth discoloration and gum diseases in real-time. Using image processing and deep learning algorithms, the device provides precise feedback and actionable insights. Paired with a mobile application offering detailed analysis and recommendations, it empowers users to adopt proactive oral hygiene practices to prevent teeth disorder.

关键词： Deep learning image processing Teeth Cameras Real-time systems Mobile applications Monitoring Diseases

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RADCI: A Synchronized Radar-RGBT Object Detecting-Tracking Dataset And A Benchmark

RADCI: A Synchronized Radar-RGBT Object Detecting-Tracking D...

引用

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

作者： Heng Yu Ruiheng Zhang Haoyang Sun Zhe Cao Biwen Yang Jin Zhang Guanyu Liu Beijing Institute of Technology Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

High-quality perception is crucial in autonomous driving and monitoring systems, where millimeter-wave radar and infrared cameras play important roles due to their robustness and reliability under harsh conditions. Both technologies can serve as low-cost supplements to optical image detection, improving overall system robustness. However, there is currently a lack of widely applicable feature-level fusion methods and multimodal datasets to effectively integrate visible light with these two heterogeneous data types for multiple tasks. In this work, we collect a new multimodal dataset, RADCI8, which synchronizes data from a camera, an infrared camera, and a radar for target detection and tracking. The dataset includes 2D image annotations, radar RAD tensor data with distance, angle, and Doppler information, as well as target ID annotations in both data formats. In addition, to address the incomplete use of radar data in previous fusion algorithms, we propose a detection method that fuses image and radar features using feature concatenation and an attention mechanism. Our proposed algorithm achieves 51.5% AP with an IOU of 50:95 on 2D bounding box prediction, significantly improving average detection accuracy over vision-based methods and maintaining robustness even when a single sensor degrades.

关键词： Tensors Radar detection Signal processing algorithms Radar imaging Radar tracking Cameras Prediction algorithms Robustness Doppler radar Synchronization

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Digital Twin-Driven Bearing-Fault Detection in Induction Motor and Drives using Graph Sampling and Aggregation Network

Digital Twin-Driven Bearing-Fault Detection in Induction Mot...

引用

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

作者： Haraprasad Badajena Suryanarayan Majhi Bivash Chakraborty Mamata Jenamani Aurobinda Routray Ronit Dutta Advanced Technology Development Centre Indian Institute of Technology Kharagpur India Industrial and Systems Engineering Indian Institute of Technology Kharagpur India Dept. of Electrical Engineering Indian Institute of Technology Kharagpur India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Bearing fault diagnosis is crucial for ensuring the reliability and safety of industrial systems, particularly in preventing operational failures and maintaining product quality. Traditional signal processing methods and deep learning algorithms, while useful, often overlook the complex structural relationships within sensor data, limiting their diagnostic effectiveness. To address this, we present a novel Digital Twin-Driven Fault Diagnosis Framework that integrates graph-based learning techniques with advanced signal analysis. Our approach employs XGBoost and GraphSAGE embeddings to capture both spatial and temporal correlations within the current signals. The raw sensor data is then processed using a sliding window technique and time-frequency domain features are extracted then transformed into a graph structure that represents the intricate relationship in the signal. GraphSAGE is then applied to these graph structures, generating embeddings that enhance fault detection accuracy. Additionally, XGBoost is utilized for classification, improving the overall robustness of the system. The proposed method, deployed on edge devices, delivers real-time diagnostics, providing a scalable and efficient solution for industrial applications. Experimental results using real-world datasets demonstrate that our method significantly outperforms state-of-the-art algorithms, improving detection accuracy and Area under the curve (AUC) scores up to 97% and 99%, respectively.

关键词： Fault diagnosis Time-frequency analysis Accuracy Correlation Fault detection image edge detection Signal processing algorithms Feature extraction Real-time systems Speech processing

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Automated Pomegranate Leaf Disease Identification: An image processing Perspectives

Automated Pomegranate Leaf Disease Identification: An Image ...

引用

International conference on Communication systems and Network Technologies (CSNT)

作者： B Meghana Janani J S Lalitha Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Agriculture is a predominant occupation in India, and to date, automated plant disease detection has been applied primarily to crops such as paddy, wheat, and various vegetables (e.g., tomato, cucumber, and potato). In this work, a novel approach is proposed for the automated detection and labeling of pomegranate leaf diseases by employing Support Vector Machine (SVM) for classification and K-Means Clustering for image segmentation. The study focuses on four specific plant diseases—Bacterial Blight, Alternaria Alternata, Cercospora Leaf Spot, and Anthracnose—as well as the detection of healthy leaves when no affected areas are present. To enhance the visibility of the leaf images, contrast enhancement is performed as a preliminary step. Subsequently, K-Means Clustering is applied to segment the input image in the Lab color space. A Multiclass SVM is then utilized to classify the segmented regions based on identified affected portions of the leaf. Finally, the Region of Interest (ROI) is determined to calculate and display the total affected area, facilitating precise disease-type detection. Experimental results indicate that the proposed method achieves an accuracy of 0.96, a precision of 0.95455, a recall of 0.9333, an F1 score of 0.978, and a kappa statistic of 0.947, thereby demonstrating the efficacy of the proposed system for automated pomegranate leaf disease detection.

关键词： Support vector machines image segmentation Plant diseases Machine learning algorithms image color analysis Clustering algorithms Crops Machine learning Labeling Diseases

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Sprinter Performance Evaluation System Based on Improved image Segmentation Method

Sprinter Performance Evaluation System Based on Improved Ima...

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Digital Analysis and processing, Intelligent Computation (DAPIC), International conference on

作者： Haili Meng Chongqing Vocational Institute of Engineering Chongqing China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

This project attempts to build a sprinter performance analysis system using optimized image segmentation technology, aiming to solve the limitations of traditional evaluation methods in dynamic blur and complex backgrounds. By introducing the improved AO-GrabCut algorithm, the image segmentation process is optimized, the separation quality of athletes and backgrounds is improved, and dynamic images of athletes moving at high speed on the track are collected through high-resolution cameras. The speed and stride of athletes are calculated in real time using the optical flow method to screen key attributes of the action. The experimental results show that the system can accurately capture the performance indicators of athletes such as starting time, acceleration time, and sprint time. The accuracy and recall rate have reached a high level, and it is outstanding in complex and changing situations. This tool provides an effective auxiliary means for the training and competition analysis of sprinters, and provides strong data support for sports science research.

关键词： Performance evaluation Training image segmentation Accuracy Heuristic algorithms Stability analysis Real-time systems Performance analysis Optimization Sports

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Data processing, analysis and Machine Learning of Electrical Atomic Force Microscopy (AFM) in Data Cube mode: conduction of a nanomaterial for Lithium-ion battery electrodes

Data processing, analysis and Machine Learning of Electrical...

引用

International image processing, Applications and systems conference (IPAS)

作者： Rosine Coq Germanicus Peter De Wolf Petr Klapetek Unicaen Ensicaen CNRS CRISMAT UMR6508 Normandie Univ Caen France Bruker Nano Surfaces Santa Barbara CA USA Czech Metrology Institute Brno Czech Republic

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

With the increasing integration of functional systems, nanoscale characterization has become crucial not only for material investigation but also for advancing the understanding of local behavior and optimizing performances. This paper details data processing techniques and machine learning methodologies for the analysis and prediction of advanced electrical Atomic Force Microscopy datasets. The datasets are acquired using a hyperspectral Data Cube approach, which combines both mapping and spectroscopy acquisitions. In the context of decarbonization and electrification, current challenges are related to energy storage and transport. A cathode material used in Lithium-ion battery electrodes is investigated for its electrical transport properties. To comprehensively characterize the electrical properties, the conductive mode of Atomic Force Microscopy is employed using the Data Cube method, which enables current-voltage spectroscopy at each pixel of the acquisition. The analysis of experimental datasets reveals localized electrical behavior of the material’s components as a function of applied voltage. Furthermore, machine learning algorithms are employed to predict current maps and cluster distinct regions based on their electrical characteristics.

关键词： Lithium-ion batteries Atomic measurements Atomic force microscopy Spectroscopy Microscopy Force Conductivity Nanoscale devices Cathodes Hyperspectral imaging

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Face Mask Detection in Real Time using Machine Learning algorithms

Face Mask Detection in Real Time using Machine Learning Algo...

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Electronics and Renewable systems (ICEARS), International conference on

作者： Kavitha R Tanishq Sultania Padhmashri V Shreya A Department of Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Coronavirus pandemic caused by a deadly virus that rapidly spread worldwide, necessitated the usage of face mask to minimize the airborne transmission of the virus. An automated face mask recognition system has made it easier to identify if an individual is wearing a face mask or not. HAAR-CASCADE and ResNet10 algorithms are applied for initial face recognition and then Mobilenet V2 and VGG16 algorithm are employed for recognizing face masks in gatherings. Amongst the stages involved in accomplishing the AI training are the pre-processing augmenting data, training, testing, and segmenting images. During each step, the CNN algorithms are utilized to generate an image segmentation of the input dataset highlighting those wearing masks and those without mask. The model is then trained with dataset, Mobilenet V2 yields an accuracy of 96.6% and VGG16 yield an accuracy of 99.42% in detecting individuals with and without wearing masks. VGG16’s deep architecture, feature extraction and transfer learning abilities enable it to be highly precise and effective. However, VGG16 is highly resource intensive and needs extensive computation time.

关键词： COVID-19 Training Face masks image segmentation Accuracy Computer viruses Face recognition Transfer learning Static VAr compensators Testing

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