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

共 14633 条记录，以下是301-310 订阅

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Deep Learning-based Early Lung Cancer Detection using Multi-Model image Fusion Technique 2

Deep Learning-based Early Lung Cancer Detection using Multi-...

引用

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

作者： Wahengbam, Monita Sriram, M. Singh, Tonjam Gunendra Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Tamil Nadu Chennai India

ISBN: (纸本)9798350340235

Lung cancer, a significant global health concern, continues to take many lives, especially as a result of late-stage diagnoses. Early detection continues to have the most significant impact on patient outcomes and mortality rates. This research examines how modern image-processing methods might transform the early diagnosis of lung cancer. To investigate the application of deep learning algorithms, computer-aided diagnosis systems, and the synergistic integration of multimodal imaging techniques to increase the accuracy and efficiency of lung cancer diagnosis. The article thoroughly examines current approaches, presents a novel framework designed for early detection, and rigorously assesses the efficacy of the suggested system using actual medical imaging data. The research has the potential to radically alter lung cancer diagnosis, encourage a new era in early intervention, and eventually help to significantly lower lung cancer mortality rates by bridging the gap between cutting-edge image processing and clinical practice. © 2023 IEEE.

关键词： Computer Aided Diagnosis Deep Learning image processing Lung Cancer Multimodal Imaging Fusion

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A Survey on Automated Disease Diagnosis and Classification of Herbal Plants Using Digital image processing 6

A Survey on Automated Disease Diagnosis and Classification o...

引用

6th International conference on Information systems and Computer Networks, ISCON 2023

作者： Khandelwal, Ankit Shukla, Aasheesh Sain, Mangal GLA University Electronics and Communication Dept. Mathura India Dongseo University Information Engineering Dept. Busan Korea Republic of

ISBN: (纸本)9798350346961

This paper presents a review on automated disease detection processing. The primary issue in herbal plants is the diagnosis and stratification of its diseases. The conventional process is unpredictable and inconsistent, therefore techniques for classifying plants diseases using digital method are necessary as they speed up the identification process. This literature discusses various digital methods for classifying plant diseases. The first step is image processing of herbal plants, which make use of traditional image processing techniques and algorithms to categories diseases in herbal plants based on attributes like texture, color, and shape. The second step is plant categorization based on deep learning and machine learning, such as CNN, KNN, logistic regression etc. Deep learning applications mainly lessen the reliance on designs and preprocessing techniques by joining the entire process. This review provides a basic understanding and knowledge of the current state-of-the-art researches, as well as some dynamic trends for future work.. © 2023 IEEE.

关键词： Computer vision

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Design of Cloud Platform for Efficient Remote Sensing Big Data processing Based on Open Stack

Design of Cloud Platform for Efficient Remote Sensing Big Da...

引用

2023 International conference on Integrated Intelligence and Communication systems, ICIICS 2023

作者： Huang, Yan Zhang, Weimeng Xi'an Traffic Engineering Institute Shaanxi Xi'an China Xi'an Technical School Shaanxi Xi'an China

ISBN: (纸本)9798350315455

The rapid preparation of computing resources is an important step to realize efficient remote sensing image processing. It takes a lot of time for researchers to prepare hardware resources and deploy computing framework. It often mistakes in the preparation and careless deployment of the framework which can lead to high research costs. To solve this problem, this paper uses Open Stack to virtualize hardware resources and uses Linux Shell scripting language to quickly prepare and deploy the Spark parallel computing environment. In this paper, based on the remote sensing image change detection method of connection condition, for example, the data processing algorithm in advance import platform algorithms library, component library, used to support the remote sensing image change detection of business flow of creation and recognition, resources rapid preparation, execution and presentation of the results of the algorithm and implement of massive remote sensing image change detection and efficient. It effectively improves the efficiency of remote sensing data management. © 2023 IEEE.

关键词： linux shell open stack remote sensing data management spark

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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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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...

引用

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...

引用

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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