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

共 15616 条记录，以下是321-330 订阅

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An image processing Based Approach Towards Recognition of Underground Cable Defects 2

An Image Processing Based Approach Towards Recognition of Un...

引用

2nd International conference on Smart Technologies for Power and Renewable Energy, SPECon 2024

作者： Thampi, Aparna Mahendran, N. Abraham, Linu Mariam Sidhartha, M.S. Apj Abdul Kalam Technological University Department of Electrical and Electronics Engineering Saintgits College of Engineering Kottayam Kerala India Tata Power Delhi Distribution Ltd India Digital Core Technology Kochi India

ISBN: (纸本)9798350383041

This paper presents a novel approach for detecting possible faults in underground cables at the manufacturing industry using image processing techniques. With the increasing adoption of underground cables to minimize faults and outages in power transmission systems, ensuring the quality of these cables becomes crucial. Detecting defects at the manufacturing stage can save significant costs and effort by identifying faulty cables before they are deployed in the field. We propose a system that utilizes image processing algorithms to analyze cable images and identify potential faults, such as unbalanced currents, cable sheath faults, and short circuits. The system also calculates the area of each core to ensure equal current flow, and estimates the total cross-sectional area of the cable to assess the adequacy of insulation. The proposed approach offers a non-destructive and efficient method to enhance quality control in the manufacturing process of underground cables. © 2024 IEEE.

关键词： Underground cables

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Coding Improvement in the DAC Algorithm 13

Coding Improvement in the DAC Algorithm

引用

13th International conference on Dependable systems, Services and Technologies, DESSERT 2023

作者： Makarichev, viktor Lukin, Vladimir Brysina, Iryna National Aerospace University KhAI Dept. of Information-Communication Technologies Kharkiv Ukraine National Aerospace University KhAI Dept. of Higher Mathematics and System Analysis Kharkiv Ukraine

ISBN: (纸本)9798350396119

Digital images are a particular type of data. They have a lot of applications. Huge image datasets have been collected. Their processing, storing, analyzing, and transferring via networks require great expenses. For this reason, a development of low resource intensive image processing algorithms is of particular importance. Discrete atomic compression (DAC) is an image compression algorithm based on atomic functions. It provides a combination of data compression and protection features with machine learning oriented data representation. Also, DAC has low time and spatial complexity, which makes its application promising. In this paper, we improve a compression performance of this algorithm. It is shown that, by applying the proposed coding scheme a compression ratio can be increased and, hence, greater memory savings can be obtained. © 2023 IEEE.

关键词： image enhancement

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Single particle imaging of biological specimens 6

Single particle imaging of biological specimens

引用

6th conference on Frontiers in Optical Imaging and Technology: Novel Imaging systems

作者： Cheng, Guanxiao Yu, Jinjie Key Laboratory of Optoelectronic Devices and Systems Ministry of Education and Guangdong Province College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen518060 China

ISBN: (数字)9781510679719

ISBN: (纸本)9781510679702

Single-particle imaging (SPI) at an X-ray free electron laser (XFEL) is demonstrating its potential to support the imaging and structure determination of biological specimens at atomic resolution without the need for crystallization. According to a principle of diffraction before damage, diffraction patterns of specimens with random orientations injected to the focus of ultra-short and extremely bright XFEL can be recorded before the specimens are damaged. For a successful image reconstruction, robust algorithms are needed. So far, fast and robust reconstruction algorithms from noisy and incomplete diffraction patterns have been challenges for XFEL SPI. Here we briefly outline the workflow of SPI and discuss key challenges and corresponding approaches to both phase retrieval and orientations determination. We also give an outlook of the promising algorithms for SPI by means of machine learning or deep learning and believe that the current computational challenges of XFEL SPI can be handled by utilizing techniques of modern data processing and artificial intelligence for imaging at atomic resolution and femtosecond temporal resolution in the future. © 2024 SPIE.

关键词： Diffraction patterns

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Enhancing Surveillance systems with YOLO Algorithm for Real-Time Object Detection and Tracking 2

Enhancing Surveillance Systems with YOLO Algorithm for Real-...

引用

2nd International conference on Automation, Computing and Renewable systems, ICACRS 2023

作者： Anish, A. Sharan, R. Hema Malini, A. Archana, T. Department of ECE Saveetha Engineering College Chennai India

ISBN: (纸本)9798350340235

A virtually Impaired Person (viP) is unable to identify objects when they cannot recognize where the object is placed. The researchers are working on it to enhance object detection and help viP. The challenges faced by researchers are performing detection under low-resolution images, insufficient sensors, portability, and cost. Making a compact device and alerting them is required. By considering the above-mentioned difficulties, an innovative solution is described in this research work. The growth of image processing and deep learning techniqueshas simplifiedthe complexity of processing data and provided accurate results within a limited time period. The suggested technique presented is a deep learning algorithm calledthe YOLO algorithm, which is combined with the web to predict objects accurately. For this approach, a dataset with a total of 500 images was chosen and trained. The proposed classifier result is satisfactory, and it achieved an overall accuracy of 94%. Furthermore, this proposed technique provides enough output in comparison with several other machine learning and image processing algorithms. © 2023 IEEE.

关键词： Deep Learning image processing Object Detection visually Impaired Person (viP) YOLO Algorithm

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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

2025 International conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

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

ISBN: (纸本)9798331513320

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. © 2025 IEEE.

关键词： Contrastive Learning

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Enhancing image Denoising Performance through a Family of algorithms 2

Enhancing Image Denoising Performance through a Family of Al...

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2nd International conference on Paradigm Shifts in Communications Embedded systems, Machine Learning and Signal processing, PCEMS 2023

作者： Gupta, Omkar Kalapala, Irika Nikhita Bhurchandi, Kishor M. Visvesvaraya National Institute of Technology Department of Electronics and Communication Engineering Nagpur India

ISBN: (纸本)9798350310719

images are an integral and indispensable aspect of various disciplines, such as medicine, surveillance, and the entertainment industry. However, the quality of images can be severely compromised by the presence of sensor noise, quantization errors, or transmission errors. This research proposes a novel approach that combines wavelet thresholding and BM3D (Block-Matching and 3D Filtering) techniques for effective image *** efficacy of the methodologies is evaluated and compared to cutting-edge denoising techniques, demonstrating superior performance in both quantitative metrics and visual quality. Furthermore, the study delves into the intricate mechanisms underlying the denoising process and the impact of various parameters on the denoising performance, contributing significantly to the field of image denoising. © 2023 IEEE.

关键词： image denoising

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Why are visually-Grounded Language Models Bad at image Classification? 38

Why are Visually-Grounded Language Models Bad at Image Class...

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38th conference on Neural Information processing systems, NeurIPS 2024

作者： Zhang, Yuhui Unell, Alyssa Wang, Xiaohan Ghosh, Dhruba Su, Yuchang Schmidt, Ludwig Yeung-Levy, Serena Stanford University United States University of Washington United States Tsinghua University China

image classification is one of the most fundamental capabilities of machine vision intelligence. In this work, we revisit the image classification task using visually-grounded language models (VLMs) such as GPT-4V and LLaVA. We find that existing proprietary and public VLMs, despite often using CLIP as a vision encoder and having many more parameters, significantly underperform CLIP on standard image classification benchmarks like imageNet. To understand the reason, we explore several hypotheses concerning the inference algorithms, training objectives, and data processing in VLMs. Our analysis reveals that the primary cause is data-related: critical information for image classification is encoded in the VLM's latent space but can only be effectively decoded with enough training data. Specifically, there is a strong correlation between the frequency of class exposure during VLM training and instruction-tuning and the VLM's performance in those classes;when trained with sufficient data, VLMs can match the accuracy of state-of-the-art classification models. Based on these findings, we enhance a VLM by integrating classification-focused datasets into its training, and demonstrate that the enhanced classification performance of the VLM transfers to its general capabilities, resulting in an improvement of 11.8% on the newly collected imageWikiQA dataset. © 2024 Neural information processing systems foundation. All rights reserved.

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FedDHr: Improved Adaptive Learning Strategy Using Federated Learning for image processing

FedDHr: Improved Adaptive Learning Strategy Using Federated ...

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2023 International conference on Self Sustainable Artificial Intelligence systems, ICSSAS 2023

作者： Velagapudi, Anuhya Jhansi, B. Hemalikha, M. vijayalakshmi, P. Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence and Data Science Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350300857

FL (Federated learning) has grown in popularity as a field of research that allows for the training of an algorithm over many decentralised servers that are having local data samples without requiring data exchange. Numerous application domains (like medical image analysis diagnosis made by doctors) lack a significant amount of properly labeled and full data that can be found in one place. Due to the prevalence of artificial intelligence in new application sectors, there are few rising issues on privacy of data and user. New algorithms of various research works are analysed in this study and it introduces a new technique that address the data heterogeneity and adaptive learning rate of the model to perform image processing, Deep Learning. © 2023 IEEE.

关键词： Electronic data interchange

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Clinical Breast Imaging and Deep Learning Model For image Malignancy Prediction Using Resource-Constrained Embedded Devices 4

Clinical Breast Imaging and Deep Learning Model For Image Ma...

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4th International conference on Artificial Intelligence and Signal processing

作者： Chokhandre, Sumedha R. Jadhav, Ms Pranjali Gole, Nilesh Thakare, Amit N. Bondade, Akansha R. Singh, Priyanka YCCE Dept Informat Technol Nagpur Maharashtra India Vishwakarma Inst Informat Technol AI & DS Dept Pune Maharashtra India Vidharbha Inst Technol Dept CSE Nagpur Maharashtra India Cummins Coll Engn Women Nagpur Maharashtra India G H Raisoni Univ Dept CSE Saikheda India VIT AP Univ Sch Comp Sci Amaravati Andhra Pradesh India

ISBN: (纸本)9798350350661;9798350350654

Computer assisted diagnosis(CAD) of diseases provides more accurate and precise diagnostic reports towards better information regarding the medical condition of patients. A clinician can minimize the error by applying his experience acquired by practice, cognitive intuition or scientific research backed by laboratory reports and computer assisted medical image analysis. The findings by the experts based on the analysis of such data are crucial as the suggested treatment is dependent on evaluation at this stage. Machine learning techniques while applied in the medical field performs decision making by mimicking the steps performed by a medical expert in diagnosing the disease, but using algorithms rather intuitive. It brings out accurate medical data through analysis of images performed by computing devices that can reveal valuable information regarding the disease prognosis. Computer aided disease diagnosis with state-of-the-art machine learning and deep learning offers seamless assistance in medical care with near human accuracy. Technology integrated medical support systems combined with sophisticated algorithms can reduce the number of false positive incidents as well as false negative cases. Convolutional neural network (CNN) models can be trained using handcrafted features to derive conclusive inferences for binary class as well as multi-class classification. Artificial Intelligence (AI) supported techniques in disease diagnosis provide assistance to medical experts in decision making by virtue of cloud based data analytics tools for storage and computing.

关键词： Computer assisted diagnosis(CAD) Convolutional neural network (CNN) Artificial Intelligence (AI) cloud based data analytics

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A Comparative Analysis of image Partitioning and Compression Mechanisms 15

A Comparative Analysis of Image Partitioning and Compression...

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15th International conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Chaudhari, Sampada P. Thakur, Nileshsingh V. Dept. of Computer Science and Engineering Prof Ram Meghe College of Engineering & Management Badnera India Dept. of Computer Science and Engineering Yeshwantrao Chavan College of Engineering Nagpur India

ISBN: (纸本)9798331300579

This paper presents a comprehensive comparative analysis of image partitioning and compression mechanisms, two fundamental techniques in image processing and data compression. image partitioning involves dividing an image into smaller components, while compression aims to reduce the size of digital images while preserving visual quality. The analysis encompasses various aspects of both techniques, including methodologies, algorithms, and performance metrics. For image partitioning, segmentation methods such as block-based, region-based, and hierarchical partitioning are examined, along with their respective advantages and limitations. Additionally, the paper explores compression mechanisms such as transform coding, quantization, entropy coding, and predictive coding, highlighting their roles in achieving efficient compression. Furthermore, the comparative analysis evaluates the tradeoffs between different partitioning and compression techniques in terms of compression ratio, image quality, computational complexity, and applicability across different types of images and applications. Various compression standards such as JPEG and PNG are also reviewed to provide insights into real-world implementations. Finally, the paper discusses future research directions and emerging trends in image partitioning and compression, including the integration of machine learning techniques for improved segmentation and compression performance. The findings presented in this comparative analysis can serve as a valuable resource for researchers, practitioners, and developers working in the fields of image processing, data compression, and multimedia systems. © Grenze Scientific Society, 2024.

关键词： image compression

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