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

共 13626 条记录，以下是341-350 订阅

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Research on image Edge Detection Method Based on Wolf King Algorithm 7

Research on Image Edge Detection Method Based on Wolf King A...

引用

7th IEEE International conference on Information systems and Computer Aided Education, ICISCAE 2024

作者： Jian, Chang Yimeng, Fan School of Software Liaoning Technical University Huludao125105 China

ISBN: (纸本)9798350350760

Aiming at the problem that the current image edge detection algorithm is not accurate enough to capture the edge contour, an image edge detection algorithm based on Wolf king algorithm was proposed. Firstly, local principal component analysis and generalized Gaussian prior were used to improve the Gaussian filter, so that the image noise was effectively suppressed and the detail information was significantly enhanced. Secondly, multi-direction gradient calculation is proposed to reduce the influence of noise on gradient estimation and provide more information to locate the edge, so as to determine the position and direction of the edge more accurately. Finally, the Wolf king algorithm proposed in this paper is based on the grey Wolf algorithm, which is improved by using the nonlinear convergence factor and adaptive weight strategy. The improved algorithm is used to optimize Otsu to obtain the image threshold, realize the automatic acquisition of the image high and low threshold, and further improve the accuracy of the algorithm. More detailed edge contour information can be extracted from the image processed by the above method. The experimental results show that the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) of the images processed by the LPG-PCA image denoising algorithm are increased by 18.8% and 13.6% respectively compared with the traditional Gaussian filtering. In terms of threshold processing, compared with most existing edge detection algorithms, the Wolf king algorithm proposed in this paper can accurately detect the edge contour, and it is more coherent in edge extraction, with better unilateral response effect and better algorithm performance. ©2024 IEEE.

关键词： Wiener filtering

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Hand Recognition Solution as conference Room Intelligent Lighting Control Technique 7

Hand Recognition Solution as Conference Room Intelligent Lig...

引用

7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023

作者： Karail, Abdulvahit Bocekci, Veysel Gokhan Kiyak, Ismail Institute of Graduate Studies in Pure and Applied Sciences Marmara University Electrical and Electronics Engineering Istanbul Turkey Marmara University Faculty of Technology Department of Electrical and Electronics Engineering Istanbul Turkey

ISBN: (纸本)9798350342154

In communication technologies, it can be solved in systems that are extremely simple, detailed, among the tools for use in the electronic environment, internet-connected, development and integrated systems. With the rapid development of technology, smart led lamps, smart office, smart conference room lighting, road lighting fixtures and therefore smart lighting systems have been the reason why smart lighting systems can enter daily life rapidly. Sensors which are the components of smart lighting systems, feedback from users, cloud services, smart algorithms become more widespread with artificial intelligence in an easier, faster, and more economical way, causing a change in traditional lighting control and improvements in energy saving, functionality, and user centered lighting. In this study, the mediapipe framework is intended to be used for office conference room. MediaPipe is indeed an open-source framework that enables the processing of computer vision data, including video and audio. The lighting system was built using the artificial intelligence supported mediapipe hand recognition solution, and artificial intelligence and image processing algorithms were used. Each hand gesture corresponds to a different command to control the light. There are 3 main menus are determined for the hand and defined to the system. Main menus can be reached with every hand movement. After entering the main menu, intermediate menus are processed. Lighting application can be realized through control commands obtained by hand movements, digital/analog information conversion and Dali control system. It is concluded that the ease of control brought by this method is more advantageous in terms of its purpose than other methods because it is contactless and does not require any hardware device in the hands of the presenter. © 2023 IEEE.

关键词： image processing

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Algorithm of Palm Vein image Selection for Biometric Authentication systems Based on Multidimensional Markov Chain 25

Algorithm of Palm Vein Image Selection for Biometric Authent...

引用

25th International conference on Digital Signal processing and its Applications, DSPA 2023

作者： Kharina, Natalya Zemtsov, Anton Chernyadyev, Sergei Vyatka State University Department of the Radio Electronic Means Kirov Russia Institute of Mathematics and Information Systems Vyatka State University Kirov Russia

ISBN: (纸本)9798350337235

Biometric palm vein authentication becomes more popular in the industry. This paper proposes an algorithm for palm vein image selection. The main idea of this method is an approximation of biometric template image through discrete Markov process and applying the theory of conditional Markov process to develop the algorithm, which allows to fully utilize the statistic redundancy of the biometric template. Given method can be effectively used after performing a thresholding process on an image, using the dynamic threshold value. CASIA MS Palmprint V1 Database dataset was used to present the examples of algorithm implementation. Morphological dilation and Zhang-Suen algorithms were used to evaluate and compare the effectiveness of the proposed palm vein image thinning algorithm. The results showed that the proposed method makes it possible to compute an image that preserves the vein thickness and closely matches the real vein pattern and is effective when using a template matching algorithm based on perceptual hash functions. © 2023 IEEE.

关键词： Biometrics

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Real-time image Edge Detection System Design and algorithms for Artificial Intelligence FPGAs 2

Real-time Image Edge Detection System Design and Algorithms ...

引用

2nd IEEE International conference on Artificial Intelligence of Things and Crowdsensing, AIoTCs 2022

作者： Ye, Caizeng Shandong Institute of Commerce and Technology Jinan250103 China

ISBN: (纸本)9798350334104

Extraction of image edges is the basis of image processing class algorithms, for image edge extraction is of great significance. Nowadays, video image data is greatly increases the amount of image data and the processing difficulty. FPGA technology stands out for its parallel processing method and pipeline structure, which enables high-speed processing of large amounts of image data, and its high processing speed ensures real-time image data processing. The application of FPGA technology in the field of image data processing will be unstoppable. The main objective of this paper is to investigate the design and algorithms of a real-time image edge detection system using artificial intelligence and FPGAs. Median filtering and edge detection algorithms in video acquisition and pre-processing systems are an important part of complex algorithms in the field of target recognition and tracking, and the quality of image pre-processing plays an important role in the high accuracy of subsequent algorithms. This paper presents an in-depth study and analysis of the hardware implementation of the HD video capture and pre-processing system, combining the characteristics of FPGA parallel high-speed and pipeline structure, and completes the system design and verification on the hardware platform, fully proving the feasibility and correctness of the system. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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A Novel image Captioning Approach Using CNN and MLP 1

引用

5th International conference on Data and Information Sciences, ICDIS 2023

作者： Sharma, Swati Tomar, Vivek Yadav, Neha Aggarwal, Mukul KIET Group of Institutions Ghaziabad India Dehradun India

ISBN: (数字)9789819969067

ISBN: (纸本)9789819969050

Computer vision and natural language processing researchers have dedicated significant time and energy to the problem of automatically creating image descriptions. In this work, we propose an artificially intelligent picture captioner built on a hybrid architecture of convolutional neural networks (CNNs) and multilayer perceptron (MLPs). This system will take photographs as input and generate captions based on those images using algorithms from convolutional neural networks (CNN), multilayer perception (MLP), recurrent neural networks (RNN). To generate a natural language caption, CNN first extracts feature from the input image, and then the MLP analyses these features. The proposed model is trained on a dataset of photos with captions by optimizing the parameters of a convolutional neural network (CNN) and a multilayer perceptron (MLP) via a hybrid of supervised learning and reinforcement learning. Our results demonstrate that the suggested model may produce captions that are on par with the best methods currently available in terms of accuracy and variety. The artificially intelligent picture captioner has potential uses in many areas, such as social networking, e-commerce, and image retrieval systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Recurrent neural networks

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Enhanced Text-to-image Synthesis Using Generative Adversarial Networks and Skip-Thought Vectors for Improved Visual and Semantic Accuracy

Enhanced Text-to-Image Synthesis Using Generative Adversaria...

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2024 International conference on Intelligent algorithms for Computational Intelligence systems, IACIS 2024

作者： Monisha, D. Keerthana, D.R. Manoj Kumar, M.V. Prashanth, B.S. Sneha, H.R. Puneeth, B.H. Nitte Meenakshi Institute of Technology Department of Information Science & Engineering Karnataka Bangalore560064 India Bapuji Institute of Engineering and Technology Department of Computer Science and Business Systems Karnataka Davanagere577004 India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360660

Creating realistic images from textual descriptions is a major challenge in computer vision and natural language processing. Traditional methods often fail to accurately translate text into visual representations, resulting in images that lack detail and context. This study addresses this issue by combining Generative Adversarial Networks (GANs) with Skip-Thought *** proposed approach involves three key stages. First, textual descriptions are embedded using Skip-Thought Vectors, which utilize a Recurrent Neural Network (RNN) encoder with Gated Recurrent Unit (GRU) activation to capture semantic nuances. Next, low-resolution images are generated using GANs, providing a basic visual structure. Finally, these images are refined to high resolution to ensure they are detailed and contextually *** method was validated using the Caltech-UCSD Birds 200 (CUB-200) dataset, showing notable improvements in both visual quality and semantic accuracy over previous methods. This approach enables the generation of detailed and contextually relevant images from text, with potential applications in design, storytelling, e-commerce, and *** automating the generation of high-quality images from text, this research enhances user experience and opens new possibilities for creative and practical uses, marking a significant advancement in the integration of computer vision and natural language processing. © 2024 IEEE.

关键词： Generative adversarial networks

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Enhancing Downhole image Reconstruction in Oil and Gas Drilling through Low Rank Sparse Models 3

Enhancing Downhole Image Reconstruction in Oil and Gas Drill...

引用

3rd International conference on Digital Society and Intelligent systems, DSInS 2023

作者： Zhou, Yixi Li, Lin Xue, Chaoyang Ji, Kexin School of Electronic Engineering Xi'an Shiyou University Xi'an710065 China

ISBN: (纸本)9798350331387

para>Effective subsurface monitoring is vital in oil drilling. This study emphasizes the need for an algorithm to streamline video transmission from underground to the wellsite, overcoming bandwidth and interference challenges for high-quality transmission. It employs low-rank and sparse models to enhance image quality. Comparing three image processing algorithms, the Structured Low Dimensional Representation (SLDR) algorithm stands out. The research aims to improve subsurface image accuracy, ensuring underground pipeline system safety and integrity. © 2023 IEEE.

关键词： image reconstruction

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Brain image Denoising and Segmentation Using Advanced Mixed Thresholding and Edge Preservation Analysis Technique

Brain Image Denoising and Segmentation Using Advanced Mixed ...

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2024 International conference on Knowledge Engineering and Communication systems, ICKECS 2024

作者： Raman, Ramakrishnan Kumar, Vikram Pillai, Biju G. Rabadiya, Dhaval Patre, Smruti Meenakshi, R. India Srv Media Pvt. Ltd India India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India Symbiosis Institute of Business Management Nagpur India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350359688

In the realm of medical imaging, particularly brain imaging, the clarity and accuracy of images are paramount for effective diagnosis and treatment planning. However, the inherent presence of noise in imaging modalities such as MRI often degrades the quality of these crucial images, complicating the segmentation process and potentially leading to inaccurate diagnoses. Addressing this challenge, our research introduces an innovative approach that synergizes advanced mixed thresholding with edge preservation analysis to significantly enhance brain image denoising and segmentation. This technique leverages a novel mixed thresholding method that dynamically adjusts to the specific noise characteristics of brain images, effectively reducing noise while retaining critical image details. Concurrently, our edge preservation analysis ensures that essential structural information is maintained, preventing the loss of vital features during the denoising process. The combination of these methods provides a robust solution to the persistent issue of noise in brain imaging, promising to improve the accuracy of segmentation and, by extension, the reliability of medical diagnoses derived from these images. By integrating advanced algorithms and mathematical models, our approach not only outperforms existing denoising and segmentation techniques but also establishes a new benchmark for image processing in medical diagnostics. © 2024 IEEE.

关键词： image denoising

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Anomaly Detection in Load Forecasting for Electric Vehicles Using image processing Techniques 24

Anomaly Detection in Load Forecasting for Electric Vehicles ...

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24th EEEIC International conference on Environment and Electrical Engineering and 8th I and CPS Industrial and Commercial Power systems Europe, EEEIC/I and CPS Europe 2024

作者： Marandi, Saba Moradzadeh, Arash Moayyed, Hamed Assi, Chadi Ghafouri, Mohsen Vale, Zita Concordia University Ciise Department Montreal Canada Qatar University Electrical Engineering Department Doha Qatar Polytechnic of Porto GECAD-LASI Department Porto Portugal

ISBN: (纸本)9798350355185

The incorporation of electric vehicles (EVs) into the power grid presents significant challenges, particularly in meeting the electricity requirements of EVs through effective distribution strategies, efficient energy allocation, and optimal charging station placement. Accurate forecasting of load demand is critical for both the operational and planning aspects of energy systems, particularly due to the influence of EV travel behavior. This study introduces a hybrid approach to EV load forecasting using deep learning models to tackle this challenge. The proposed CLSTM model integrates convolutional neural network (CNN) and long short-term memory (LSTM) algorithms. Furthermore, the study introduces and applies an anomaly detection model based on image processing. The forecasting process initially uses clean data and then advances to a scenario involving a simulated cyber-attack by manipulating certain input data, which is referred to as a false data injection attack. After analyzing the data using various evaluation metrics and confirming anomalies, the image processing model identifies these anomalies through data visualization. In conclusion, a thorough evaluation of the results under both clean and compromised forecasting conditions unequivocally demonstrates that the CLSTM model not only surpasses the performance of traditional models but also exhibits greater robustness and accuracy. This superior performance highlights the CLSTM model's potential as a more reliable and effective solution for forecasting in scenarios impacted by data integrity issues. © 2024 IEEE.

关键词： Vehicle-to-grid

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UAV image object detection based on improved Yolov8 4

UAV image object detection based on improved Yolov8

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4th International conference on image processing and Intelligent Control, IPIC 2024

作者： Zhang, Wei Wang, Jiaxin Wang, Dan Kong, Xiangwei Institute of Measurement and Testing Technology Chinese Flight Test Establishment Xi'an710089 China

ISBN: (纸本)9781510682313

In a variety of UAV application scenarios, such as agricultural monitoring, urban safety and traffic management, high-precision image object detection is crucial for real-time analysis and decision-making. Traditional object detection algorithms are often ineffective in dealing with small objects in complex environments. To this end, this paper proposes an improved YOLOv8 model that integrates a NAM (Normalization-based Attention Module) attention mechanism and a Bidirectional Feature Pyramid Network (BiFPN) specifically to optimize the object detection performance of UAV images. The NAM module enhances the model's spatial attention capability by utilizing a normalization process that This enables the model to adaptively emphasize important feature regions in the image, which significantly improves the recognition accuracy of small-scale objects without adding too much computational burden. BiFPN further optimizes the flow of information between different scales, which improves the efficiency of feature utilization and the overall performance of detection. Extensive experiments on the VisDrone2019 dataset show that the improved YOLOv8 model improves the mAP metrics by 11.3% compared to the original model, and performs especially well in scenes with complex backgrounds and the presence of multi-scale objects. This study not only validates the potential application of NAM in UAV vision tasks, but also provides a new technical path for the implementation of deep learning models in real monitoring systems. © 2024 SPIE.

关键词： image analysis

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