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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401-410 订阅

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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 ...

引用

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

引用

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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International conference on Integrated Intelligence and Communication systems, ICIICS 2023

International Conference on Integrated Intelligence and Comm...

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

ISBN: (纸本)9798350315455

The proceedings contain 286 papers. The topics discussed include: utilizing machine learning to analyze and implement task scheduling algorithms in cloud;real time fire detection and alert triggering system;a dynamic model for blended learning of university English based on K-Means clustering algorithm;real time eye-tracking mouse control using recurrent neural network;collaborative virtual storage technology based on mobile cloud computing;enhanced intrusion detection system using Pearson correlation based long short-term memory;construction of group algorithm model under green innovation capability technology;numerical simulation and analysis of integrated mobile medical building based on finite element method;the implementation and optimization of high-performance image processing algorithms based on artificial intelligence;and research on photographic image classification based on multi-model fusion and data augmentation.

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Wavelet based Convolutional Autoencoder for Medical image Compression

Wavelet based Convolutional Autoencoder for Medical Image Co...

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2023 International conference on Intelligent systems for Communication, IoT and Security, ICISCoIS 2023

作者： Venugopal, Muthalaguraja Palanisamy, Kalavathi Department of Computer Science and Applications Gandhigram Tamil Nadu India

ISBN: (纸本)9798350335835

Deep learning approaches have seen tremendous progress lately, particularly in image processing. In image compression, the deep autoencoders are predominantly showing promising results. Autoencoders have demonstrated exceptional performance in image compression tasks when compared to conventional lossy algorithms used for compression, in both the quantitative and qualitative aspects, despite the fact that they are lossy image compression approaches. This paper, puts forth a wavelet-based autoencoder-style compression approach for medical image compression. The proposed architecture is composed of four modules namely Pre-processing, Encoder, Decoder and Enhancement module. In order to achieve remarkable compression performance on the medical images, this method utilizes the advantage of wavelet decomposition technique to extract finer details from the low and high frequency components of the medical image. The experimental findings suggest that our approach achieves notable image compression performance. It outperforms the established JPEG in terms of the objective and subjective metrics PSNR and SSIM, and it also yields results that are comparable to those of JPEG2000 and BPG. © 2023 IEEE.

关键词： image compression

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Plant Leaf Disease Identification Method Using Computer vision and Machine Learning algorithms

Plant Leaf Disease Identification Method Using Computer Visi...

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

作者： Sohan, Ismail Hossain Rahman, Saidur Chhabra, Gunjan Kaushik, Keshav Haque, Rezaul Dept. of Cse Dhaka Bangladesh Graphic Era Hill University Dept. of Cse Dehradun India University of Petroleum and Energy Studies School of Computer Science Dehradun India East West University Dept. of Cse Dhaka Bangladesh

ISBN: (纸本)9798350315455

This paper proposes an image processing-based method for detecting plant diseases. The system's three key phases are acquisition, pre-processing, and disease identification. Pictures of plant leaves are taken during the image acquisition stage using a digital camera. The image preprocessing stage involves enhancing and pre-processing the collected images to eliminate noise and extraneous data. The pre-processed photos are finally sorted into healthy or unhealthy, utilizing machine learning techniques in the illness identification step. This paper will represent how we can detect plant leaf diseases using a specific software system. It will also find the percentage of accuracy and error of disease detection based on the number of detected images. The leaf disease finding procedure information will also be explained here step by step. According to experimental data, the proposed approach has a high level of accuracy in detecting plant illnesses. Farmers can utilize the system as a valuable tool to detect plant illnesses early, minimizing yield losses and enhancing crop management techniques. © 2023 IEEE.

关键词： Disease detection Feature extraction image classification image processing Machine Learning

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Federated heuristic optimization based on fuzzy clustering and red fox optimization algorithm

Federated heuristic optimization based on fuzzy clustering a...

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IEEE International conference on Fuzzy systems (FUZZ-IEEE)

作者： Polap, Dawid Prokop, Katarzyna Srivastava, Gautam Silesian Tech Univ Fac Appl Math Kaszubska 23 PL-44100 Gliwice Poland Brandon Univ Dept Math & Comp Sci Brandon MB R7A 6A9 Canada

ISBN: (纸本)9798350332285

Heuristic algorithms are dependent on many coefficients like the number of iterations or individuals. However, quite often these algorithms move individuals toward the best in the population. Based on this observation, we propose the idea of federated heuristics. The proposed idea is to initially distribute individuals into certain intervals. Then, it performs a specified number of iterations of the algorithm to identify the potentially best intervals. Sorted intervals (in relation to the best-adapted individual) make it possible to separate the appropriate size of the population in each of them. Moreover, these clusters are merged by a fuzzy algorithm due to a decrease in their numbers. The more significant the interval, the greater the number of individuals and iterations allocated in these areas. As a consequence, several instances of the selected heuristic algorithm are triggered, which can divide the best individual. The proposed technique was described using the red fox algorithm and tested at a classic set of functions with different parameters of the used heuristic.

关键词： fuzzy neural network heuristic image processing

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Cones 2: Customizable image Synthesis with Multiple Subjects 37

Cones 2: Customizable Image Synthesis with Multiple Subjects

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37th conference on Neural Information processing systems (NeurIPS)

作者： Liu, Zhiheng Zhang, Yifei Shen, Yujun Zheng, Kecheng Zhu, Kai Feng, Ruili Liu, Yu Zhao, Deli Zhou, Jingren Cao, Yang USTC Hefei Peoples R China SJTU Shanghai Peoples R China Ant Grp Hangzhou Peoples R China Alibaba Grp Hangzhou Peoples R China Alibaba DAMO Acad Hangzhou Peoples R China

ISBN: (纸本)9781713899921

Synthesizing images with user-specified subjects has received growing attention due to its practical applications. Despite the recent success in single subject customization, existing algorithms suffer from high training cost and low success rate along with increased number of subjects. Towards controllable image synthesis with multiple subjects as the constraints, this work studies how to efficiently represent a particular subject as well as how to appropriately compose different subjects. We find that the text embedding regarding the subject token already serves as a simple yet effective representation that supports arbitrary combinations without any model tuning. Through learning a residual on top of the base embedding, we manage to robustly shift the raw subject to the customized subject given various text conditions. We then propose to employ layout, a very abstract and easy-to-obtain prior, as the spatial guidance for subject arrangement. By rectifying the activations in the cross-attention map, the layout appoints and separates the location of different subjects in the image, significantly alleviating the interference across them. Both qualitative and quantitative experimental results demonstrate our superiority over state-of-the-art alternatives under a variety of settings for multi-subject customization. Project page can be found here.

关键词： Embeddings

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Analysis of Optimization on Sparse Matrix Vector Multiplication Model Application in Digital Signal processing 3

Analysis of Optimization on Sparse Matrix Vector Multiplicat...

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3rd IEEE International conference on Electrical Power and Energy systems, ICEPES 2024

作者： Shaji, Alden C Khare, Kavita Maulana Azad National Institute of Technology Electronics & Communication Engineering Department Bhopal India

ISBN: (纸本)9798350390728

Conventional digital signal processing (DSP) technology, utilizing DSP and FPGA, excels in real-time signal processing but faces limitations in handling large-scale data and achieving high frequency resolution. This restricts its effectiveness for tasks involving offline data processing, analysis, and mining in applications with extensive data volumes. Some of the SpMV applications in image editing operations like linear filtering, resizing and mixing are reformulated in the SpMV multiplication form. The growing complexity of applications and the demand for top-notch digital data necessitate a boost in processing power. With energy efficiency becoming a critical concern, there is research on designing SpMV algorithms that minimize energy consumption. This is particularly relevant for embedded DSP systems and applications where power efficiency is a key consideration. A comparative study of High-Level Synthesis (HLS) techniques to reduce the power consumption and resource utilization conducted on Xilinx-Zynq 7000 FPGA is presented. This paper explores various HLS pragmas techniques using OpenCL framework applied on Sparse Matrix Vector Multiplication (SpMV) kernel for optimizing power efficiency and resource utilization. HLS has emerged has a pivotal technology for simplifying the design process of complex digital systems by allowing developers to work at higher levels of abstraction, using high-level programming languages. From the analysis, identified the best optimization technique for the low power and the minimum resource utilization for the SpMV implementation on FPGA hence it can be more useful for applications in the field of CNN. © 2024 IEEE.

关键词： High level synthesis

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Research on Parallel Computation and Memory Optimization of Multi-Core Embedded DSP Technology in High Precision Missile Terminal Guidance image Tracking Algorithm 6

Research on Parallel Computation and Memory Optimization of ...

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6th IEEE International conference on Power, Intelligent Computing and systems, ICPICS 2024

作者： Xia, Fuming Jing, Bo Huo, Mingge Jiao, Xiaoxuan Wang, Shenglong Pan, Jinxin School of Aeronautical Engineering Air Force Engineering University Xi'an710038 China Beijing Electro-Mechanical Engineering Institute Beijing100074 China

ISBN: (纸本)9798350374315

This paper mainly discusses how to effectively use multi-core embedded digital signal processor (DSP) technology to realize parallel computation of image tracking algorithm and memory optimization in high precision missile terminal guidance system. With the increasing requirement of missile strike accuracy in modern war, the vision-based terminal guidance technology has been widely concerned because of its strong autonomy and anti-jamming ability. However, such image processing algorithms are usually highly computation-intensive and data dependent, which requires high real-time and hardware resources. In this paper, the key steps of image tracking algorithm are analyzed and reconstructed deeply, and the architecture suitable for parallel processing of multi-core DSP is designed by modularization. The parallel computing advantages of multi-core DSP are given full play through task division and scheduling strategies to improve the execution efficiency and real-time performance of the algorithm. Secondly, the research also focuses on memory optimization, including the design of data cache strategy, reducing data redundancy, improving memory access locality, etc., and strives to maximize the data reading speed and reduce the memory bandwidth pressure under the condition of limited embedded system resources. To sum up, this research is committed to building an efficient and stable multi-core embedded DSP environment, parallel computing model and memory optimization scheme for high-precision missile terminal guidance image tracking algorithm, which has important theoretical value and practical significance for improving the intelligence level and combat efficiency of missile terminal guidance system. © 2024 IEEE.

关键词： Guided missiles

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