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

共 16443 条记录，以下是1171-1180 订阅

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A Novel Color image Encryption using DNA Code and Dual Memristor Hyperchaotic System 18

A Novel Color Image Encryption using DNA Code and Dual Memri...

引用

18th INDIAcom;11th International conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Belete, Biniyam Ayele Gelmecha, Demissie Jobir Singh, Ram Sewak Rathee, Davinder Singh Arora, Amit Besfat, Henok Mezemr Adama Science and Technology University Department of Electronics and Communication Engineering Adama Ethiopia Galgotias University Department of Electronics Gr. Noida India

ISBN: (纸本)9789380544519

This work presents a modified hyperchaotic system based on two memristors with unique Lyapunov exponents, offering a unique method for image encryption. The main goals are to use dynamic analysis to evaluate the complexity and unpredictable nature of the proposed method and then apply it to image encryption. A strong color picture encryption system is developed by combining Deoxyribonucleic acid (DNA) sequences, logistic maps, and the memristive hyperchaotic system. Using an upgraded four-dimensional dual memristor-based hyperchaotic system with three Lyapunov exponents, logistic mapping, and DNA encoding are all integrated in this technique. This research carefully combines these three methods to solve current issues with picture encryption schemes. Security performance tests and simulations, which verify the efficiency of the proposed algorithm against different attacks, such as brute-force, statistical, entropy, differential, visual, and histogram attacks, preserve methodological rigor. Comparisons with traditional chaotic systems in image processing demonstrate how much better security the proposed method exhibits. Important rmdings show how DNA operations may be successfully integrated with the hyperchaotic memristive system to produce improved picture encryption capabilities. The program not only proves its efficacy in protecting picture data, but it also performs better than traditional methods. Applications like information security, encryption, and secure communication are all affected by these discoveries. The research highlights the algorithm's resilience to many attacks, highlighting its potential as a significant advance in image encryption algorithms. The suggested method offers a feasible way to protect strict image data in a variety of real-world situations. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： DNA computing Hyperchotic system image encryption Memristor Security metrics

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Smart equipment failure detection with machine learning applied to thermography inspection data in modern power systems 11

Smart equipment failure detection with machine learning appl...

引用

11th International conference on Smart Grid (IcSmartGrid)

作者： Maria Garzon, Ana Laiton, Natalia Sicacha, Victor Celeita, David F. Trung Dung Le Univ Rosario Sch Engn Sci & Technol Bogota Colombia Univ Paris Saclay Cent Supelec CNRS Grp Elect Engn Paris GeePs Gif Sur Yvette France

ISBN: (纸本)9798350310665

This paper presents a novel approach to detecting equipment failures in modern power systems by leveraging machine learning techniques applied to thermography inspection data. Particularly segmentation and pixel processing to improve accurateness is highlighted in the methodology. The proposed method is capable of identifying early warning signs of equipment failure and predicting when the failure is likely to occur. The proposed approach demonstrates the potential for early detection of equipment failure in modern power systems with accurate clustering. The use of machine learning algorithms applied to thermography inspection data provides a reliable and effective way to identify and predict equipment failures, ultimately leading to improved system reliability and reduced maintenance costs.

关键词： Thermography inspection image segmentation predictive fault identification machine learning neural network

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Performance Comparison of AI Models for Digital image captioning

Performance Comparison of AI Models for Digital Image captio...

引用

2022 International conference on Computational Modelling, Simulation and Optimization, ICCMSO 2022

作者： Kaur, Rajwinder Singh, Gurpreet Chitkara University Institute of Engineering and Technology Punjab Rajpura India

ISBN: (纸本)9798350332889

Digital image captioning field deals with the concept of interpreting a given image by the application of different Artificial Intelligence (AI) based algorithms. These kind of systems always considered the input in the form of image and replying with an output representing some text or speech samples. This output explains the content present in the image. These kind of automated systems proven good in the areas like medical image processing, online review systems, speech conversion for visually impaired persons etc. This paper represents a comparative study among four different AI models: Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM) and kNN (k-Nearest Neighbor) in context of image captioning activity. Random scenes in the form of input images has been consider to show the part of diverse nature of these algorithms. It has been observed that SVM classifier outshine as compared to rest of the models with the overall accuracy of 98%. © 2022 IEEE.

关键词： Support vector machines

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Optimizing Multimodal Sarcasm Detection with a Dual Perceiving Network and Adaptive Fusion 12

Optimizing Multimodal Sarcasm Detection with a Dual Perceivi...

引用

12th International conference on Information systems and Computing Technology

作者： Zhai, Yi Liu, Jin Liu, Yuxin Jiang, xiantao Shanghai Maritime Univ Coll Informat Engn Shanghai Peoples R China

ISBN: (纸本)9798350379860;9798350379877

Nowadays, posting sarcastic text or visual content on platforms like WhatsApp, Twitter, and Facebook has become a popular style, allowing individuals to avoid directly expressing pessimism, thereby indirectly conveying their thoughts or intentions. As more people express their opinions online, the need to develop effective models for accurate multimodal sarcasm detection is growing. However, most existing methods have deficiencies in extracting effective features from non-text modalities, which can result in the neglect of some sarcasm-related contextual information. Additionally, during the fusion phase of different modalities, simply using straightforward feature concatenation may introduce redundant information and noise. To address these challenges, a new model called Dual-Perceiving Network (DPN) was developed. It includes a dual-view perception module, which enables the text modality to fully perceive both the local and global features of the image modality, thereby obtaining context and background information conducive to constructing inconsistencies. Additionally, it employs an adaptive fusion method to effectively reduce the impact of redundant information. Extensive experiments comparing the proposed DPN with other multimodal sarcasm detection methods demonstrate its superior performance on public datasets.

关键词： Multimodal sarcasm detection Deep learning Large language models Heterogeneous data processing

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Prediction of Colon Cancer using DenseNet121, CNN, and REsNET50 Machine Learning Models and using image processing Techniques

Prediction of Colon Cancer using DenseNet121, CNN, and REsNE...

引用

2023 International conference on Artificial Intelligence Robotics, Signal and image processing, AIRoSIP 2023

作者： Hasan, Mahadi Islam, Jahirul Ahmed, Minhaz Hasan, Md Maruf The University of Tennessee at Chattanooga Ms in Data Analytics ChattanoogaTN United States New Mexico Institute of Mining and Technology Ms in Cse SocorroNM United States Ms in Cse *** United States The University of South Dakota Department of Comuter Science VermillionSD United States

ISBN: (纸本)9798350327472

Colon cancer is a matter of great importance in the field of global health, as it stands as one of the primary contributors to mortality rates associated with cancer. The timely identification and precise prognostication of colon cancer have the potential to significantly enhance patient outcomes and diminish mortality rates. The global public health systems face a significant challenge in addressing the impact of colon cancer, which underscores the need for efficient screening and prediction techniques. Machine learning techniques present potential solutions by utilizing extensive datasets to detect intricate patterns that may be imperceptible to human observers. This study investigates the viability of employing machine learning techniques to predict colon cancer by utilizing a dataset consisting of 10,000 colonoscopy images that have been classified into two categories: cancerous and non-cancerous cases. The main aim of this study is to assess the efficacy of various machine learning algorithms, namely DenseNet-121, CNN, and ResNet50, in the prediction of colon cancer. The models' performance was comprehensively assessed in terms of their accuracy in predicting colon cancer. Among the models that were evaluated, DenseNet-121 demonstrated exceptional performance, achieving the highest accuracy of 99.6%. © 2023 IEEE.

关键词： Lung cancer

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Online Microscopic Imaging Combined with Machine Learning image processing for Online Monitoring of Mineral Froth Flotation Process

Online Microscopic Imaging Combined with Machine Learning Im...

引用

2024 8th International conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

作者： xianfa Dong Xuezhong Wang College of New Materials and Chemical Engineering Beijing Institute of Petrochemical Technology Beijing China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

Froth flotation is an important process in the mineral processing industry for extracting valuable materials. This work investigates online microscopic imaging and machine learning based image analysis methods for real-time monitoring of the process. Previous limited work explored imaging the foam at the top surface layer of the froth flotation process. The new process imaging system in this work uses a corrosion-resistant online real-time imaging probe that can be put into the inside of the slurry and capture real-time images of bubbles. The acquired images are analyzed online using deep learning algorithms to automatically obtain key parameters of the bubbles, providing valuable data for froth flotation research and process control.

关键词： Manufacturing processes Microscopy Process control Real-time systems Minerals Materials processing Probes Surface treatment Slurries Monitoring

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RNCE: A New image Segmentation Approach

RNCE: A New Image Segmentation Approach

引用

International conference on Computer Vision and Machine Intelligence, CVMI 2022

作者： Kumar, Vikash Ali, Asfak Chaudhuri, Sheli Sinha Electronics and Telecommunication Engineering West Bengal Jadavpur Kolkata700032 India

ISBN: (纸本)9789811978661

Semantic image segmentation based on deep learning is gaining popularity because it is giving promising results in medical image analysis, automated land categorization, remote sensing, and other computer vision applications. Many algorithms have been designed in recent years, yet there is scope for further improvement in computer vision research. We have proposed a unique ensemble method called Ranking and Nonhierarchical Comparison Ensemble (RNCE) for semantic segmentation of landcover images based on the Ranking and Nonhierarchical Comparison methodology. Our approach has been tested on pretrained models showing improved accuracy and mean IoU with respect to the existing method. The code is available at: https://***/vekash2021/***. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Remote sensing

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Determining image Deblur, Detecting Faces, and Enhancing Appearance Using image processing Techniques

Determining Image Deblur, Detecting Faces, and Enhancing App...

引用

IEEE International conference on Automatic Control and Intelligent systems (I2CACIS)

作者： Norazliani Md Sapari Khairul Huda Yusof Nicholas Tan Kien Lung Madihah Md Rasid Siti Maherah Hussin Centre of Electrical Energy Systems (CEES) Faculty of Electrical Engineering UTM Skudai Johor Faculty of Information Science & Engineering Management & Science University Selangor Malaysia

ISBN: (数字)9798350372106

ISBN: (纸本)9798350372113

This paper explores the utilization of MATLAB for digital signal processing (DSP) techniques in image processing tasks, focusing on image deblurring, face detection, and facial feature enhancement. Blind deconvolution methods are employed to address image blurriness, while face detection is facilitated using cascaded object detectors. Enhancements to detected facial features involve histogram equalization, smoothing filters, skin tone adjustment, and contrast enhancement techniques, followed by seamless integration using resizing methods. MATLAB serves as a robust platform for implementing and analyzing DSP algorithms, providing insights into practical solutions for common challenges in digital image processing.

关键词： Visualization Histograms Digital images Signal processing algorithms Digital signal processing Skin image restoration

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Improved ShuffleNetV2 for Garbage image Classification 18th

Improved ShuffleNetV2 for Garbage Image Classification

引用

18th International conference on Intelligent Information Hiding and Multimedia Signal processing, IIH-MSP 2022

作者： Zhang, Jinghan Li, Cheng Guo, Baolong Institute of Intelligent Control and Image Engineering Xidian University Xi’an China

ISBN: (纸本)9789819906048

This paper proposes a novel lightweight classification network based on ShuffleNet V2 to promote the accuracy of garbage image classification. Compared with the conventional network, there is a package of optimizations to both ameliorate the classification accuracy and inference speed of the model. Firstly, the optimized network introduces the Leaky ReLU as a substitute for the activation function. It also adopts the squeeze and excitation network to enhance important features. In addition, the flattened layer is applied to downscale the data, and three fully connected layers are used for enhancing the learning capability. Furthermore, the model is pre-trained based on transfer learning, wherein the feature knowledge learned from imageNet datasets is transferred to garbage classification task. Extensive experiments on the garbage image dataset verify the overall performance, which improves the classification accuracy by 6.3% with a 35.4% reduction in time consumption. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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Enhanced YOLOv8 for rapid diagnosis and analysis of infrared spectra in power industry insulator detection 4

Enhanced YOLOv8 for rapid diagnosis and analysis of infrared...

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4th International conference on Advanced algorithms and Neural Networks, AANN 2024

作者： Zhou, Ming Wu, Qiang xiong, Wei Ma, Li Cheng, Zhao Sun, Liping State Grid Hubei Electric Power Company Wuhan China Economic and Technological Research Institute State Grid Hubei Electric Power Company Wuhan China

ISBN: (纸本)9781510686106

Insulator defects in power transmission systems pose significant risks to grid stability and safety. This paper presents an improved method for rapid diagnosis of insulator defects using infrared images, based on an enhanced YOLOv8 algorithm. Our approach incorporates Haar wavelet downsampling and a Polarized Self-Attention (PSA) mechanism to boost detection accuracy while maintaining real-time performance. The Haar wavelet downsampling Boosts the model's capability to grasp multi-scale features, concurrently enhancing the PSA mechanism's emphasis on pertinent thermal patterns. We evaluate our method on a comprehensive dataset of 10,000 infrared images collected from various power transmission environments. Experimental results demonstrate that our improved YOLOv8 model achieves SOTA, with a mean Average Precision of 94.7%, outperforming the original YOLOv8 and other object detection algorithms. The proposed method offers a 3.2% increase in mAP compared to the original YOLOv8, while maintaining a processing speed of 42.8 frames per second. This research contributes significantly to the advancement of more efficient and precise outcomes power grid maintenance techniques, potentially reducing the need for manual inspections and enhancing overall system reliability. © 2024 SPIE.

关键词： image enhancement

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