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

共 12925 条记录，以下是131-140 订阅

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GPU-Accelerated Feature Extraction for Real-Time Vision AI and LLM systems Efficiency: Autonomous image Segmentation, Unsupervised Clustering, and Smart Pattern Recognition for Scalable AI processing with 6.6× Faster Performance, 2.5× Higher Accuracy, and UX-Centric UI Boosting Human-in-the-Loop Productivity

GPU-Accelerated Feature Extraction for Real-Time Vision AI a...

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IEEE/SEMI conference and Workshop on Advanced Semiconductor Manufacturing

作者： Kiarash Ahi Stewart Wu Satya Sriram Germain Fenger Siemens EDA USA Siemens EDA Belgium Siemens EDA India

ISBN: (数字)9798331531850

ISBN: (纸本)9798331531867

The high computational cost of digital image processing, requiring high-performance hardware and extensive resources, severely limits real-time applications. While advancements in algorithm design and GPU acceleration have significantly improved efficiency, modern AI-driven applications such as large language models (LLMs), Generative AI (GenAI), medical imaging, autonomous vehicle perception, photography, advanced nano-scale semiconductor metrology, satellite image analysis, high-precision manufacturing, robotics, and real-time anomaly detection, still demand further optimization to reduce computational overhead and improve *** this paper, we introduce GPU-Accelerated Feature Extraction to enhance runtime and efficiency in edge-based simulations. Our approach leverages AI-driven clustering, grouping images with similar visual and pattern characteristics to enable adaptive tuning on a small subset before generalizing across the full dataset. This method achieves a 3.78× reduction in ***, rather than processing an entire image, we recognize and extract a single representative pattern or region of interest (ROI) per image, removing redundant data and background noise. This refinement results in an additional 1.74× runtime improvement, culminating in an overall 6.6× speed boost, enhancing Scalable Real-Time AI processing. We also demonstrated that with a similar runtime, the accuracy achieved is 2.5× *** solution, integrated into Calibre SEMSuite™, supports multicloud and real-time deployment for enhanced scalability, usability, and performance, providing users with a powerful tool for fully automated, AI-driven image classification, making high-throughput image review feasible even at the scale required for cutting-edge *** performance gains, this approach introduces autonomous data cleaning, anomaly detection and defect identification mechanism, allowing failed patterns and defective images to be identified without h

关键词： Accuracy Runtime image edge detection Clustering algorithms Feature extraction Real-time systems Computational efficiency Artificial intelligence Anomaly detection Tuning

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Sport Safety Enhancement: A Novel Approach with Monitoring Matrix-Based image Acquisition and Analysis

Sport Safety Enhancement: A Novel Approach with Monitoring M...

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Machine Learning and Autonomous systems (ICMLAS), International conference on

作者： Xinyi Zhang Department of Physical Education Shandong Management University Jinan China

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

With the development of video array imaging technology, the accuracy and stability of video image acquisition have been improved. In this situation, this study proposes novel approach with monitoring matrix-based image acquisition and analysis for sport safety enhancement. This study includes the following four major innovations 1) Developed an image acquisition system based on a monitoring matrix, which can accurately capture the facial information of athletes through array imaging mode; 2) Proposed a new facial feature extraction algorithm, which realizes real-time analysis of facial images by fusing EAR and MAR features; 3) Proposed an improved expression recognition algorithm, and the improved activation function optimizes the expression recognition network to accurately identify the facial expressions of athletes; 4) Designed an image classification algorithm, and the 8 -pixel average mode is used for fuzzy information processing, so as to provide real-time warning information about the physical condition of athletes through the expression data. The experimental results show that the proposed method has satisfactory recognition accuracy on the Cambridge University Computer Laboratory's facial expression data set, and can accurately provide early warning of the athlete's status.

关键词： image recognition Accuracy Face recognition Imaging Feature extraction Real-time systems Classification algorithms Safety Monitoring Sports

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Breast Cancer Prediction Using Machine Learning

Breast Cancer Prediction Using Machine Learning

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Emerging systems and Intelligent Computing (ESIC), International conference on

作者： Shiv Ashish Dhondiyal Pooja Luthra Bhupender Sharma Rajni Rajni Ishita Uniyal Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science & Engineering Chandigarh University Mohali Punjab Department of Computer Science and Application Coer University Roorkee Uttarakhand Department Of Computer Science & Engineering Guru Jambheshwar University of Science and Technology Hisar Haryana India Department of Electrical Engineering Graphic Era Hill University

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

The number of newly diagnosed cases of breast cancer exceeds 2.3 million annually worldwide. There is a severe lack of valid prognostic and predictive indicators for the clinical treatment of breast cancer patients at the present time. In order to decrease the mortality rate and increase the survival time of breast cancer patients, early detection of this deadly disease is crucial. Improving breast cancer prognosis relies on the analysis and processing of mammography data, the gold standard for screening and early detection. In order to detect breast cancer in mammograms, the Fuzzy C-means algorithm is employed for image segmentation. Next, we train it to identify the characteristics of the segmented regions, and then we use an effective classifier to categorise the trained pictures into their respective mammography classes. This research used the Breast Cancer Wisconsin Diagnostic dataset and two ML algorithms: K-Nearest Neighbours (KNN) and Random Forest. After getting the results, we compared and evaluated the performance of these two classifiers. Potentially useful tools for both early diagnosis and treatment planning include breast cancer prediction models that account for a wide range of risk factors. Effective disease management involves the collection, storage, and administration of many data sets, as well as intelligent systems based on several aspects for the prediction of breast cancer.

关键词： image segmentation Accuracy Machine learning algorithms Sensitivity Databases Nearest neighbor methods Prediction algorithms Breast cancer Classification algorithms Random forests

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Vehicle image Anomaly Detection Based on Improved K-Means Algorithm and High-Resolution Camera

Vehicle Image Anomaly Detection Based on Improved K-Means Al...

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Machine Learning and Autonomous systems (ICMLAS), International conference on

作者： Xiaoqin Yang Syazwina Alias Hui Wang FoEBEIT SEGi University Kota Damansara Selangor Malaysia College of Computer and Communication Engineering Nanjing Tech University Pujiang Institute Nanjing Jiangsu China

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

This study proposes a vehicle image anomaly detection framework based on improved K-means algorithm and high-resolution camera, which provides a new idea for road safety construction from the image processing level. The main innovation of the study contains 4 aspects: 1. A new vehicle target extraction algorithm based on deep learning is proposed. The algorithm uses the YOLOv5s network structure and combines the SimAM attention mechanism to optimize the model while improving the detection accuracy of vehicle targets. 2. A visual-based traffic flow clustering analysis is performed. This analysis achieved effective clustering of traffic flow through deep HOG feature extraction and parallel K-means clustering algorithm. 3. An optimized license plate detection and recognition algorithm based on YOLOv5 is proposed. The algorithm improves the accuracy of license plate recognition by adding the key point detectors. 4. The traditional K-means algorithm is optimized. The optimization idea is mainly to combine quantum computing and space-time theory. This optimization improves the convergence speed and clustering effect of the algorithm. The results of comparative experiments show that the improved K-means algorithm converges faster in traffic flow clustering analysis, and its license plate recognition accuracy is significantly better than traditional algorithms (such as k-means and FCM).

关键词： YOLO Technological innovation Machine learning algorithms Accuracy Clustering algorithms Cameras Road safety License plate recognition Anomaly detection Optimization

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Learning systems in Forgery image Detection 1st

Learning Systems in Forgery Image Detection

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1st International conference on Intelligent systems in Computing and Communications, ISCComm 2023

作者： Shetty, Sudheer Ganesh, Devadiga Likhit kumar Poojary, Rahul R. Raviraj Tejas, R. Hegde, Nagaratna P. Vikkurty, Sireesha Department of ISE Sahyadri College of Engineering & Management Mangaluru Affiliated to Visvesvaraya Technological University Karnataka Belagavi India Department of ISE Alvas Institute of Engineering and Technology Moodbidri Affiliated to Visvesvaraya Technological University Karnataka Belagavi India Department of CSE Vasavi College of Engineering Telangana Hyderabad India

ISBN: (纸本)9783031756078

In an era dominated by the ubiquitous sharing of visual content on social media, the proliferation of fake photographs and digital fakes has emerged as a critical issue. This paper explores how fake images are detected with fusion of by various techniques of image processing, CNN, and machine learning, presenting an innovative approach to address the intricate challenge of identifying manipulated images. This paper gives insight on various approaches of image processing techniques and state-of-the-art machine learning algorithms used in a robust forgery image detection system. A comprehensive framework is proposed on various image forensic methods, ensuring the precise detection of fake images across diverse contexts. The paper also emphasizes the impact of countering the dissemination of fake photographs to preserve the credibility of digital media. This combination of image processing, CNN and machine learning propels the ongoing quest to uncover the truth behind the surge of digital fakes in today's digital landscape. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Experimental Results of Local Backprojection for Monostatic THz SAR Imaging

Experimental Results of Local Backprojection for Monostatic ...

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International Workshop on Mobile Terahertz systems (IWMTS)

作者： Yevhen Ivanenko Aman Batra Viet Thuy Vu Mats I. Pettersson Thomas Kaiser Blekinge Institute of Technology Karlskrona Sweden University of Duisburg-Essen Duisburg Germany

ISBN: (数字)9798350365832

ISBN: (纸本)9798350365849

The use of THz frequencies has enabled the opportunity to perform synthetic-aperture radar (SAR) imaging at the sub-mm level. It is of great interest for applications where high-resolution remote sensing in short range is required. However, with the increase of the operating frequencies from microwave to THz, the SAR image formation algorithms work with a larger amount of data and become more sensitive to phase errors that can be caused by insufficient signal sampling rate or physical factors that cause platform deviations. This motivates the use of fast image formation capable to handle phase errors. In this paper, we present the experimental results on the performance of the local backprojection (LBP) algorithm for processing THz SAR signals. The LBP algorithm has been tested with the real data in the frequency range 0.325–0.5 THz. The results demonstrate the efficiency of the LBP algorithm for the SAR scene reconstruction and highlight the necessity of the use of two times higher signal upsampling to achieve reconstruction accuracy similar to the global backprojection algorithm.

关键词： Terahertz wave imaging Radar remote sensing Accuracy Heuristic algorithms Signal sampling Approximation algorithms Radar polarimetry Reliability image reconstruction Remote sensing

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Detection Algorithm for High Impedance Faults on Electrical Edge Device

Detection Algorithm for High Impedance Faults on Electrical ...

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Grid Edge Technologies conference & Exposition (Grid Edge), IEEE PES

作者： Xiangying Meng Souvik Chandra Dmitry Ishchenko Michael Nowak Soumyabrata Talukder Eaton Corporation USA

ISBN: (数字)9798350352528

ISBN: (纸本)9798350352535

In recent years, there has been a growing interest in using machine learning algorithms for solving challenging power systems protection problems. One such difficult problem, High impedance (HiZ) faults are characterized by low fault currents and erratic harmonic behavior which are not detected traditional overcurrent protection. These faults when not immediately cleared pose public safety hazard and potential to start a wildfire. This paper presents advanced signal processing techniques that quantify harmonic energy and randomness in current signature, enabling timely identification and clearance of HiZ faults. The detection method includes an anomaly event monitoring algorithm which identifies time points with sustained changes in harmonic features, distinguishing abnormal events from normal operation. Once an anomaly event is detected, features associated with HiZ faults are extracted and applied to identify faults. This approach uses features which are trained using a machine learning algorithm but still can be implemented in regular edge devices like relays, recloser controls etc. with significantly reduced computational burden. The method shows promising detection time and accuracy for lab emulated actual high impedance faults.

关键词： Fault diagnosis Machine learning algorithms Wildfires image edge detection Signal processing algorithms Power system harmonics Signal processing Harmonic analysis Feature extraction Impedance

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Underwater image Enhancement Using Gaussian Smoothing and Edge Detection

Underwater Image Enhancement Using Gaussian Smoothing and Ed...

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International conference on Inventive Computation Technologies (ICICT)

作者： V. Lambodara Aravind C. Bagyalakshmi Department of Computer Applications Hindustan Institute of Technology and Science

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Underwater acoustic communication is used for oceanographic data collection, environmental monitoring, disaster alarm systems, and navy applications. Underwater has its peculiarities such strong signal attenuation and limited bandwidths, multipath nature of signals, and ambient noises. All these characteristics bring a great deal of interference probably in transmission with certainty and effectiveness. This work propounded an optimized hybrid model that works concurrently with advanced signal processing and noise-reducing techniques for communication enhancement. The purpose of the proposed model is to develop high signal-to-noise ratio (SNR) received signals with lesser transmission error using time-frequency domain analysis and filtering techniques, which are specifically designed for underwater applications. Thus, the main aim of this work, in fact, is towards the development of a robust underwater communication platform, which ensures a very reliable transfer of data under conditions of excessive noise even in the rapidly changing aquatic environments. Comparison of the proposed model with traditional techniques shows that this model stands much higher than the earlier techniques regarding transmission accuracy, noise resilience, and overall efficiency, thus extending the development of more reliable underwater communication systems toward future marine and defense applications.

关键词： Adaptation models Underwater communication Smoothing methods image edge detection Computational modeling Signal processing algorithms Reliability Underwater acoustics image enhancement Signal to noise ratio

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Architectural Decoration image Recognition and Classification based on Convolutional Neural Network

Architectural Decoration Image Recognition and Classificatio...

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Intelligent systems and Computational Networks (ICISCN), International conference on

作者： Wenhao Zhang Jianyang Yang Mingjie Yang Weifang Engineering Vocational College Weifang China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Architectural decoration images are of various types and complex features. Therefore, fast and accurate recognition and classification of architectural decoration images has become a key challenge to improve design efficiency and project management level. This paper proposes an innovative solution - a CNN-based architectural decoration image recognition and classification method. The paper first collects and annotates various types of architectural decoration images to construct a dataset containing multiple architectural decoration styles and preprocesses these images. In the construction of CNN model, a deep network structure integrating convolutional layer, pooling layer, fully connected layer, etc. is designed. The CNN model is trained using the preprocessed architectural decoration image dataset, and the model parameters are adjusted through the back propagation algorithm to minimize the classification error. The results show that the average recognition accuracy of CNN in architectural decoration image recognition reaches 97.61%, which is significantly higher than the 91.35% of DeepLabv3+, and the processing speed is also faster. This paper provides a new technical means for the field of architectural decoration, which helps to promote technological progress and innovation in this field.

关键词： Backpropagation Training Technological innovation image recognition Accuracy Computational modeling Data models Classification algorithms Convolutional neural networks Unsupervised learning

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Optical Authenticity in Pushbroom System for Spectral Information Protection

Optical Authenticity in Pushbroom System for Spectral Inform...

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

作者： Pablo Gomez Roman Jacome Emmanuel Martinez Hans Garcia Henry Arguello Department of Electrical Electronics and Telecommunications Engineering Universidad Industrial de Santander Bucaramanga Colombia Department of Systems and Informatics Engineering Universidad Industrial de Santander Bucaramanga Colombia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In remote sensing and environmental monitoring, the authenticity of the collected hyperspectral image (HSI) is critical since unauthorized changes could cause inaccurate evaluation. Traditional approaches ensure HSI integrity after the acquisition, leaving the data vulnerable to unauthorized access or modification before applying a protective strategy. In response, this work presents a pushbroom HSI system that integrates an authentication key on the acquisition process, ensuring the key imperceptibility without affecting performance in spectral classification tasks. In addition, this work develops an algorithm designed to simulate attacks through class modifications in the HSI. Additionally, a detection algorithm is proposed to identify modifications by comparing the signed HSI with the altered version, effectively recognizing the embedded key. The proposed method was tested using simulated data and acquired data in the optical laboratory. Results demonstrate that the system maintains classification accuracy, further enhancing imperceptibility. Additionally, modifications are detectable by the key with 99% accuracy.

关键词： Integrated optics Accuracy Authentication Signal processing algorithms Optical imaging Classification algorithms Optical sensors Optical signal processing Speech processing Hyperspectral imaging

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