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

共 12592 条记录，以下是131-140 订阅

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Detection Algorithm for High Impedance Faults on Electrical Edge Device

Detection Algorithm for High Impedance Faults on Electrical ...

引用

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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Architectural Decoration image Recognition and Classification based on Convolutional Neural Network

Architectural Decoration Image Recognition and Classificatio...

引用

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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ResNet-50 for Flower image Classification: A Comparative Study of Segmentation and Non-Segmentation Approaches

ResNet-50 for Flower Image Classification: A Comparative Stu...

引用

International conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Abdul Rachman Manga Nirmala Huzain Azis Farniwati Fattah Yulita Salim Herdianti Darwis Departement of Computer Science Universitas Muslim Indonesia Makassar Indonesia

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

Flower image classification poses a challenge in digital image processing, requiring effective methods for feature extraction and classification. The aim of this research is to improve the accuracy of flower image classification by employing ResNet-50 for feature extraction, with and without segmentation, and evaluating the effectiveness of various classification algorithms. The dataset consists of images of Calendula and Coreopsis flowers, totaling 2,025 samples. Four segmentation techniques-Canny, Thresholding, Otsu, and Mean Shift-along with a non-segmentation approach are applied. The features extracted using ResNet-50 are classified with Naive Bayes, Support Vector Machine (SVM), Decision Tree, and K-Neighbors Classifier (KNN). Performance evaluation is conducted using accuracy, precision, recall, and F1-score with 5-fold cross-validation. The results show that SVM delivers the best performance in most scenarios. The highest accuracy in segmentation scenarios was achieved with the Mean Shift technique at 0.91, while the non-segmentation approach yielded the highest accuracy of 0.95. The non-segmentation approach proves more effective, indicating segmentation is not always required for high classification accuracy with ResNet-50. This study shows that ResNet-50, especially without segmentation, can significantly improve flower image classification compared to traditional methods, opening opportunities for more efficient systems.

关键词： Support vector machines Performance evaluation image segmentation Accuracy Thresholding (Imaging) Flowering plants Streaming media Feature extraction Classification algorithms image classification

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A Comparative Study of VGG16, MobileNet, and Ensemble Methods on Fundus image-Based Cataract Detection

A Comparative Study of VGG16, MobileNet, and Ensemble Method...

引用

Advancement in Data Science, E-learning and Information systems (ICADEIS), International conference on

作者： Tabina Shafa Nabila Syahda Mochamad Nizar Palefi Ma'ady Berlian Rahmy Lidiawaty Department of Information Systems Telkom University Surabaya Surabaya Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

Artificial Intelligence (AI) in ophthalmology has been growing, driven by the increasing volume of clinical data that can be utilized for algorithm development. image processing on fundus disease is crucial in providing visual information for cataract detection. Therefore, the application of technology that can leverage fundus images for cataract detection is still a few. In this regard, several previous studies have explored CNN algorithms for automatic cataract screening using fundus images. However, other research groups have proposed or developed new algorithms as alternatives. This research compares two CNN models, VGG16 and MobileNet, while also incorporating additional approaches such as Ensemble Learning and Hybrid Method. VGG16 and MobileNet were chosen because they are known for their high accuracy performance and computational efficiency. Source data obtained from Kaggle as many as 1000 datasets. The results of the study indicate that the model with the Ensemble Learning approach achieved the highest average accuracy, followed by MobileNet, MobileNet + SVM, VGG16, and VGG16 + SVM, with accuracies of 0.9377; 0.9150; 0.9143; 0.9129; and 0.9104, respectively. This research is expected to aid medical professionals in diagnosing cataracts more accurately and guide engineers in selecting the best machine-learning models for fundus image-based cataract detection systems.

关键词： Cataracts Support vector machines Visualization Accuracy Computational modeling Data models Ophthalmology Ensemble learning Medical diagnostic imaging Overfitting

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8th International conference on image Analysis and processing, ICIAP

引用

8th International conference on image Analysis and processing, ICIAP 1995

ISBN: (纸本)3540602984

The proceedings contain 114 papers. The special focus in this conference is on Textures and Segmentation. The topics include: Region-based segmentation of textured images;a new approach to image segmentation;using hopfield networks to segment color images;the watershed transformation for multiresolution image segmentation;bounds on the optimal elasticity parameters for a snake;efficient attributed graph matching and its application to image analysis;matching delaunay graphs;an integrated approach to grouping and matching;an adaptive reject option for LVQ classifiers;a model based method for characterization and location of curved image features;estimation of curvature and tangent direction by median filtered differencing;adaptive elimination of false edges for first order detectors;spatio-temporal data management for a 3D graphical information system;an object-oriented framework for digital image processing systems;data packing vs processing speed on low-cost massively parallel systems;a new parallel method based on a genetic approach for determination and classification of skin spots;a VLSI scalable processor array for motion estimation;evaluating digital angles by a parallel diffusion process;a pyramidal approach to convex hull and filling algorithms;a code based approach to image-processing teaching;a visual speech model based on fuzzy-neuro methods;edge detection filters based on artificial neural networks;use of cortical filters and neural networks in a self-organising image classification system;occluded objects recognition using multiscale features and hopfield neural networks and automatic video segmentation through editing analysis.

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Proceedings - 14th International conference on image Analysis and processing, ICIAP 2007

Proceedings - 14th International Conference on Image Analysi...

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14th Edition of the International conference on image Analysis and processing, ICIAP 2007

ISBN: (纸本)0769528775

The proceedings contain 132 papers. The topics discussed include: dynamic score selection for fusion of multiple biometric matchers;integrating boundary information in pairwise segmentation;spectra generative models for graphs;facing imbalanced classes through aggregation of classifiers;learning repetitive patterns for classifying non-rigidly deforming texture surfaces;motion estimation via belief propagation;a method of clustering combination applied to satellite image analysis;calibration and image generation of mobile projector-camera systems;hybrid stereo sensor with omnidirectional vision capabilities: overview and calibration procedures;image spam filtering using visual information;localization of ahead vehicles with on-board stereo cameras;subspace reduction for appearance-based navigation of a mobile robot;and topology preserving arching cubes-like algorithms on the face-centered cubic grid.

关键词： Belief propagation

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Overcoming Hardware Limitations in Massive MIMO: A Generative AI Take

Overcoming Hardware Limitations in Massive MIMO: A Generativ...

引用

IEEE conference on Wireless Communications and Networking

作者： Bitan Banerjee Ahmad Nimr Gerhard Fettweis Vodafone Chair for Mobile Communications Systems Technische Universität Dresden Germany

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Recent transition in mobile communication standards suggests massive multiple-input multiple-output (MIMO) to be an integral part of the foreseeable future. However, as antenna elements increase to hundreds in the fifth-generation (5G) and beyond, traditional signal processing methods become prone to significant hardware impairments compound from multiple chains, leading to a substantial performance degradation. This paper explores the effectiveness of generative artificial intelligence (AI) techniques in addressing these challenges within massive MIMO systems. For this purpose, the conditional generative adversarial network (CGAN), a special class of generative AI algorithms, is employed to enhance the accuracy of channel state information (CSI) estimation in a hardware-impaired transceiver setup. This problem is treated as an image-denoising task, where the noise is introduced by the hardware impairments and LS estimation error. Through simulations conducted across various antenna array sizes, the potential of generative AI to improve CSI estimation accuracy under hardware impairments is demon-strated. This highlights its capacity to address critical signal processing challenges in the next-generation wireless systems.

关键词： Accuracy Generative AI Array signal processing Massive MIMO Generative adversarial networks Throughput Hardware Transceivers Next generation networking Antenna arrays

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Quantum-Enhanced Machine Learning for Precision Breast Cancer Diagnosis

Quantum-Enhanced Machine Learning for Precision Breast Cance...

引用

International Communication systems and Networks and Workshops, COMSNETS

作者： Agniva Banerjee Dept. Electrical Engineering and Computer Science Indian Institute of Science Education and Research Bhopal Bhopal India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Cancer is the leading cause of premature mortality. Globally, the prevalence of cancer is on the rise, and early-stage diagnosis is crucial for cancer recovery and survival. However, the presence of circulating tumor cells (CTCs) and heterogeneity within the tumor makes it hard to track how cancer changes over time and how resistance to treatment develops. Unfortunately, the cancer diagnosis precision remains inadequate, endangering precious human lives and necessitating costly procedures for accurate treatment. Machine learning (ML) has proven capable of discovering meaningful information and making predictions, particularly with large datasets. Moreover, quantum computers (QCs) inherently possess superposition and parallel computation, features not found in classical computing. Researchers have demonstrated that quantum neural networks (QNNs) achieve a significantly better effective dimension than classical NNs. In this context, this study investigates the application of classical and quantum machine learning (QML) algorithms to classify breast cancer images. Region-based segmentation is used to identify regions of interest and image processing algorithms are performed on segmented breast cancer images to extract meaningful features (e.g., perimeter, area, compactness). An user interface (UI) based software is created to swiftly distinguish benign and malignant instances during image analysis. Although QNNs perform poorly than the classical NNs, there is still a long way to go in developing feature maps and techniques for better performance. In contrast, the classical NNs have gone a long way with well-established algorithms and packages available to us. This research shows that with further development, QML has the potential to enhance cancer diagnosis accuracy and speed, ultimately improving early detection and treatment outcomes.

关键词： image segmentation Accuracy Machine learning algorithms Software algorithms Artificial neural networks Machine learning Feature extraction Breast cancer Software Tumors

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IEEE VCIP 2013 - 2013 IEEE International conference on Visual Communications and image processing

IEEE VCIP 2013 - 2013 IEEE International Conference on Visua...

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2013 IEEE International conference on Visual Communications and image processing, IEEE VCIP 2013

ISBN: (纸本)9781479902903

The proceedings contain 132 papers. The topics discussed include: adaptive rounding operator for efficient Wyner-Ziv video coding;retina model inspired image quality assessment;color image guided locality regularized representation for kinect depth holes filling;motion vector refinement for frame rate up conversion on 3d video;efficient active contour model based on Vese-Chan model and split Bregman method;HEVC interpolation filter architecture for quad full HD decoding;correlation estimation for distributed wireless video communication;enhancing coded video quality with perceptual foveation driven bit allocation strategy;soft mobile video broadcast based on side information refining;quality enhancement based on retinex and pseudo-HDR synthesis algorithms for endoscopic images;and object co-segmentation based on directed graph clustering.

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High Resolution Millimeter Wave Imaging Based on FMCW Radar systems at W-Band

High Resolution Millimeter Wave Imaging Based on FMCW Radar ...

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IEEE Annual Computing and Communication Workshop and conference (CCWC)

作者： Shahrokh Hamidi MR Nezhad-Ahmadi Department of Electrical and Computer Engineering University of Waterloo Canada MMSENSE Technologies Inc. Waterloo Canada

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

In this paper, we present a unique 2D high resolution, compact, low-cost, lightweight, and highly accurate millimeter-wave imagery system capable of operating in all weather conditions. We explain different aspects of the imagery system in detail and describe the process of millimeter-wave image formation thoroughly. To create the array, we utilize the Synthetic Aperture Radar (SAR) concept. The imagery system presented in this paper, can strongly compete with the Lidar systems as the resolution limit is at the same level. Furthermore, in contrast to the Lidar systems, our imagery system can operate in heavy rain as well as dense fog and produce high quality images. Finally, we utilize our wide-band custom-made Frequency Modulated Continuous Wave (FMCW) radar, which operates at W-band with 33 GHz bandwidth, for data collection and present the results.

关键词： image resolution Laser radar Frequency modulation Imaging Signal processing algorithms Millimeter wave radar Radar imaging Millimeter wave communication Synthetic aperture radar Signal resolution

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