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

共 16408 条记录，以下是321-330 订阅

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Research Progress of Improved Conventional image Edge Detection Algorithm 2nd

Research Progress of Improved Conventional Image Edge Detect...

引用

2nd International conference on image, Vision and Intelligent systems, ICIVIS 2022

作者： Zhang, Meng Jia, Lina School of Information Science and Engineering Zaozhuang University Zaozhuang China Institute of EduInfo Science and Engineering Nanjing Normal University Nanjing China

ISBN: (纸本)9789819909223

image edges contain most of the information in an image, which is one of the most basic and important features in an image. image edge extraction occupies a special place in computer vision and image processing, which is the basis for implementing boundary-based image segmentation and target recognition. In order to make more researchers understand the research status of image edge detection, this paper introduces the implementation principles and methods of several traditional image edge detection algorithms, summarizes the new edge detection methods that appear on this basis, analyzes the characteristics and processing performance of various algorithms, and also compares the excellence among different algorithms to provide reference and guidance for researchers to further improve and innovate the algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Edge detection

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Identification of Sedimentary Microfacies Based on Genetic BP Algorithm and image processing 1

Identification of Sedimentary Microfacies Based on Genetic B...

引用

1st IEEE International conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Tang, Chengjia Xichang University School of Information Science and Technology Liangshan Xichang615013 China

ISBN: (纸本)9798350323795

With the development of The Times, the study of sedimentary microfacies is increasing. The study of sedimentary microfacies generally includes the division of sedimentary time unit in the study area, single well facies, well logging facies, well profile facies, plane facies, and finally the establishment of facies model. However, the current technology of sedimentary microfacies recognition is not perfect, so more excellent algorithms need to be added. This paper studies the identification of sedimentary microfacies based on gene-BP algorithm and image processing, aiming to better identify sedimentary microfacies through the two methods of gene-BP algorithm and image processing. In the experiment of this paper, the automatic identification coincidence rate of samples without screening is as low as 70% and as high as 75%, while the automatic identification coincidence rate of samples after screening is as low as 80%. The highest value is 88%. From this experimental result, it can be seen that the genetic BP algorithm is helpful for the identification of sedimentary microfacies. © 2023 IEEE.

关键词： Genetic algorithms

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Exploration on Facial image Recognition and processing algorithms under Hadoop

Exploration on Facial Image Recognition and Processing Algor...

引用

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

作者： Gu, Meifang Tang, Hailin Li, Lili Guangdong Baiyun University Institutional Research Center Guangdong Guangzhou China Guangdong Baiyun University Faculty of Intelligent Manufacturing Engineering Guangdong Guangzhou China Guangdong Baiyun University Faculty of Megadata and Computing Guangdong Guangzhou China

ISBN: (纸本)9798350315455

Face recognition is an important technology in the field of digital image processing, which has a crucial impact on subsequent work. This article introduced an example of facial recognition based on the combination of Hadoop algorithm and facial feature extraction. The experimental results were validated in the MATLAB environment and trained using neural networks to improve system performance and adaptability. The test results indicated that the processing speed was between 0.84 and 0.97;the highest resource utilization rate was 0.06, and the lowest was 0.02;the throughput range of facial data was between 13598 and 15479, and the throughput range of user data was between 5037 and 5879. This indicated that the system had fast recognition speed and high-precision image output, and exhibited good fault tolerance and robustness compared to other methods. Exploring facial image recognition and processing algorithms in the Hadoop environment can utilize Hadoop's distributed computing capabilities to accelerate the processing of large-scale facial image data, achieve fast and efficient processing of large-scale facial image data, accelerate algorithm operation, and possess distributed computing capabilities. Multiple computing nodes can be used for parallel processing to improve algorithm performance. © 2023 IEEE.

关键词： algorithm research facial recognition Hadoop algorithm image processing

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Advancements in Hyperspectral image processing Using Machine Learning

Advancements in Hyperspectral Image Processing Using Machine...

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2024 IEEE International conference on Information Technology, Electronics and Intelligent Communication systems, ICITEICS 2024

作者： Ramani, Kasarapu Yukitha, P. Karthik, Sake Chowdary, Karapakula Aravind Akash, Mandapalli Dept of Data Science Tirupati India Sree Vidyanikethan Engineering College Department of Csse Tirupati India

ISBN: (纸本)9798350382693

In several domains, such as remote sensing, agriculture, and environmental monitoring, hyperspectral image processing is essential. In this work, the Indian Pines dataset is used to investigate hyperspectral picture classification. Five machine learning algorithms - Support Vector Machines (SVM), Random Forest (RF), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Extreme Gradient Boosting (XGBoost) are assessed for performance. It improves classification accuracy for preprocessing methods such as feature extraction, PCA and noise reduction. The effectiveness of each method is evaluated using performance indicators such as kappa coefficient, average accuracy, and overall accuracy. Our results highlight how well XG Boost performs in obtaining the maximum accuracy of 96.12% when compared to other models. © 2024 IEEE.

关键词： Convolutional neural networks

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Dual-band image Sequence Registration based on Description of Velocity and Curvature Distribution 16

Dual-band Image Sequence Registration based on Description o...

引用

16th International conference on Signal processing systems, ICSPS 2024

作者： Zhao, Bingqing Tang, Shanjun Li, Chuchen Yang, Beier Yang, Qingyu Shanghai Electro-Mechanical Engineering Institute Shanghai201109 China

ISBN: (纸本)9781510689251

The composite seeker based on multi-band imaging detection is the development focus of future precision-guided weapons. A certain seeker adopts a recognition and tracking strategy based on the fusion of long-wave infrared and visible light images. The quality of the fused image is closely related to the image registration accuracy. In order to solve the problem that the feature description of infrared and visible dual-band images is not identical due to large difference in grayscale texture information, we propose a registration algorithm based on description of velocity and curvature distribution. In particular, we first extract the infrared and visible light target foreground, and use the created velocity distribution description to complete the FAST corner point matching on the foreground contour. All the matched point pairs are used to achieve coarse contour registration. Then, we use the created curvature distribution description to complete the CSS corner point matching of the target foreground. Finally, a real-time updated matching reservoir is built to calculate the fine registration matrix. The experimental results of multiple sets of actual infrared/visible light image sequences show that the proposed algorithm can achieve better feature description identity and better registration accuracy than the other algorithms. © 2025 SPIE.

关键词： image fusion

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Alzheimer's Disease Classification and Severity Level Identification in image processing using Deep Learning algorithms 2

Alzheimer's Disease Classification and Severity Level Identi...

引用

2nd International conference on Sustainable Computing and Smart systems (ICSCSS)

作者： Devi, T. Sangeetha Raghavendran, V Vels Inst Sci Technol & Adv Studies VISTAS Dept Comp Sci Sch Comp Sci Chennai 600117 Tamil Nadu India

ISBN: (纸本)9798350391558;9798350379990

Alzheimer's disease (AD) is a neurodegenerative condition that deteriorates brain cells and impairs a patient's memory. It is progressive and incurable. Early identification can shield the patient from more brain cell damage and, as a result, help them avoid irreversible memory loss. The scientific community has employed a number of deep learning algorithms to automatically identify Alzheimer's patients. These comprise binary classification of patient scans into stages of AD as well as moderate cognitive impairment (MCI). Limited research has been done on the multiclass classification of Alzheimer's disease (AD) up to six distinct stages. This research proposes novel technique in Alzheimer disease detection with severity level analysis utilizing deep learning (DL) model. Input is collected as MRI brain images and processed for noise removal and smoothening. Then processed image classification and disease stage is detected using pre-trained multi-layer convolutional residual transfer Random Forest with InceptionV3 model. Experimental analysis is carried out in terms of training accuracy, mean average mean average precision, sensitivity, AUC for various MRI brain image dataset. Training accuracy attained by proposed technique is 96%, mean average precision of 93%, sensitivity of 95%, AUC of 90%.

关键词： Alzheimer disease detection severity level analysis deep learning model InceptionV3 Random Forest

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IMPROVING CROSS-DOMAIN FEW-SHOT CLASSIFICATION WITH MULTILAYER PERCEPTRON 49

IMPROVING CROSS-DOMAIN FEW-SHOT CLASSIFICATION WITH MULTILAY...

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49th IEEE International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Bai, Shuanghao Zhou, Wanqi Luan, Zhirong Wang, Donglin Chen, Badong Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Peoples R China Xian Univ Technol Sch Elect Engn Xian Peoples R China Westlake Univ Inst Adv Technol Westlake Inst Adv Study Hangzhou Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Cross-domain few-shot classification (CDFSC) is a challenging and tough task due to the significant distribution discrepancies across different domains. To address this challenge, many approaches aim to learn transferable representations. Multilayer perceptron (MLP) has shown its capability to learn transferable representations in various downstream tasks, such as unsupervised image classification and supervised concept generalization. However, its potential in the fewshot settings has yet to be comprehensively explored. In this study, we investigate the potential of MLP to assist in addressing the challenges of CDFSC. Specifically, we introduce three distinct frameworks incorporating MLP in accordance with three types of few-shot classification methods to verify the effectiveness of MLP. We reveal that MLP can significantly enhance discriminative capabilities and alleviate distribution shifts, which can be supported by our expensive experiments involving 10 baseline models and 12 benchmark datasets. Furthermore, our method even compares favorably against other state-of-the-art CDFSC algorithms.

关键词： Multilayer Perceptron cross-domain few-shot classification distribution shift

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Marine Targets InISAR Imaging via Virtual Antenna Construction Method under Squint Scenarios 16

Marine Targets InISAR Imaging via Virtual Antenna Constructi...

引用

16th International conference on Signal processing systems, ICSPS 2024

作者： Yan, Haodong Shao, Shuai Liu, Hongwei National Key Laboratory of Radar Signal Processing Xidian University Xi’an710071 China

ISBN: (纸本)9781510689251

The squint imaging scenarios introduce the additional phase, leading to distortion in interferometric inverse synthetic aperture radar (InISAR) imaging results and presenting a formidable challenge for existing algorithms. To address this issue, the virtual antenna construction method (VACM) is proposed in this paper for squint InISAR imaging. In this method, the squint imaging scenarios can be transformed into simpler emmetropia imaging scenarios by compensating for the wave path difference resulting from antenna construction. Combined with the particle swarm optimization (PSO) and the angular motion parameter estimation algorithm (AMPEA), the proposed method enables the squint InISAR imaging of marine targets. Extensive experiments have validated the effectiveness and robustness of this approach for squint InISAR imaging. © 2025 SPIE.

关键词： Inverse synthetic aperture radar

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PiVisionSort: Integrating image processing and Machine Learning for Material Recognition on Conveyor Belts 2

PiVisionSort: Integrating Image Processing and Machine Learn...

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2nd International conference on Data, Information and Computing Science, CDICS 2024

作者： Kia, Shohreh Leiding, Benjamin Technische Universität Clausthal Institute for Software and System Engineering Clausthal-Zellerfeld Germany

ISBN: (纸本)9781643685762

This research aims to develop an advanced material detection system for conveyor belts, utilizing state-of-the-art image processing and machine learning techniques to automate the identification of various materials, thereby enhancing operational efficiency and accuracy in industrial settings. Current methods in material detection, such as traditional manual sorting and basic automated systems, often need more precision and adaptability in dynamic industrial environments. This paper identifies a gap in the practical and reliable detection of diverse materials under varying environmental conditions. The primary research questions addressed include: How can modern image processing and machine learning techniques improve material detection accuracy? What optimizations can be applied to ensure real-time processing efficiency? The proposed solution integrates a strategic camera setup with controlled lighting, robust image preprocessing algorithms (noise reduction, normalization, and resizing), and custom-designed detection algorithms using machine learning models. This system outperforms existing solutions by offering higher detection accuracy and adaptability to diverse industrial conditions. An interface is developed to display detection results and provide intuitive controls for system adjustments, ensuring practical use by industrial professionals. Rigorous testing and validation processes are implemented to enhance detection accuracy and processing speeds, with specific performance metrics established to measure efficacy. This paper provides a transformative impact on the manufacturing and processing industries by addressing environmental variability and material diversity. © 2025 The Authors.

关键词： Noise abatement

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Conservation of Wildlife in Hunting Tourism Using Artificial Intelligence and image processing in Smart Firearms 2nd

Conservation of Wildlife in Hunting Tourism Using Artificial...

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2nd International conference on Information Technologies and Their Applications

作者： Asil, Ufuk Nasibov, Efendi Dokuz Eylul Univ Izmir Turkiye

ISBN: (纸本)9783031734199;9783031734205

This paper presents an innovative technological approach to ensure the sustainability of state-controlled nature park hunting tourism and to protect wildlife. The developed smart fire control systems utilize artificial intelligence (AI) and advanced image processing techniques to prevent firing by activating the safety pin in various situations. Through an integrated camera module and a minicomputer from the Jetson family, animals can be detected in real-time, and various data analysis techniques, along with object recognition algorithms, allow for ethical intervention in many situations that are invisible to the human eye and could be exploited by hunters. Particularly in various national parks, the frequent reports of accidental or uninformed hunting of rare and endangered species, especially during their breeding seasons, underline the importance of this technology. Furthermore, hunters' preference for the most dominant animal in a herd as a trophy poses a significant threat in evolutionary terms. This technology contributes significantly to the conservation of wildlife while promoting ethical and sustainable practices in hunting tourism. The study addresses the development, testing, and implementation of this technology and adds a technological dimension to wildlife conservation strategies.

关键词： Artificial Intelligence image processing Smart Firearms Wildlife Conservation Hunting Tourism

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