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

共 16347 条记录，以下是321-330 订阅

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Advancements in Hyperspectral image processing Using Machine Learning

Advancements in Hyperspectral Image Processing Using Machine...

引用

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...

引用

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...

引用

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...

引用

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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image Recognition System Based on Distributed Intelligence Technology to Optimize User Experience 2

Image Recognition System Based on Distributed Intelligence T...

引用

2nd IEEE International conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Huang, Yihang Hanyang University Graduate School of Design Visual Design Major Ansan Korea Republic of

ISBN: (纸本)9798350372892

With the continuous progress of computer technology, distributed intelligent systems become more and more popular. Based on this background, this paper discusses the theory of image recognition based on distributed intelligent network model and aims to implement the user location function through this theory. In this paper, an automatic detection algorithm based on distributed intelligence technology is proposed for implementing the function of user location. This algorithm is based on the theory of image recognition based on distributed intelligent network model and processes the image data through a specific algorithmic logic in order to locate the user's position. The applicability of the algorithm in real projects is verified by writing experimental programmes. The Distributed Intelligence technique was compared with the other three algorithms and the results showed that the Distributed Intelligence technique performed best in terms of adaptability and stability. The comprehensive data exceeds 85.5%, indicating that the algorithm has high accuracy and efficiency in recognising and processing images, solves the problem of matching the target with the environmental scene, and provides an important reference and comparison basis for image acquisition and processing. The concluding observation of this paper is that the Distributed Intelligence Technology has the best comprehensive performance in different environments, not only superior to other algorithms in terms of adaptability and stability, but also its comprehensive data index exceeds 85.5%. © 2024 IEEE.

关键词： distributed intelligence technology image recognition optimization research user experience

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Channel Estimation of Data Reuse Proportional RLS for Single-carrier UWA Communication 16

Channel Estimation of Data Reuse Proportional RLS for Single...

引用

16th International conference on Signal processing systems, ICSPS 2024

作者： Yang, Hui-Zhong Wang, Honglei Su, Ben-Xue Wu, Fei-Yun School of Marine Science and Technology Northwestern Polytechnical University Shaanxi Xi'an China Navigation College Jimei University Fujian Xiamen China

ISBN: (纸本)9781510689251

To enhance the channel estimation accuracy in single-carrier underwater acoustic (UWA) communication, we propose integrating data reuse (DR) and proportional updating into the recursive least squares (RLS) algorithm, designing the DR proportional RLS (DRPRLS) algorithm. By using system sparsity, the convergence speed of the DRPRLS algorithm is accelerated. Additionally, the gain factor is incorporated into the weight update of the DRPRLS algorithm, computed from the reused input and reference signals, further improving the estimation accuracy. We perform numerical simulations of the proposed DRPRLS algorithm and compared it with least mean squares (LMS), improved normalized LMS, RLS, proportional RLS, and DRRLS algorithms, considering computational complexity and tracking performance. In the at-sea experiments, we obtain the bit error rates (BER) driven by different algorithms through the minimum mean square error equalizer. Real data indicates that the DRPRLS algorithm improves the BER performance. © 2025 SPIE.

关键词： Mean square error

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Low-illuminance image Enhancement Algorithm for Indoor Wine Cellar Scenes Based on Retinex-net

Low-illuminance Image Enhancement Algorithm for Indoor Wine ...

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International conference on Electronic Engineering and Information systems (EEISS)

作者： Han, Yunjie Chen, Guanyu Li, Chang Liu, Songsong Wan, Chaoye Guizhou Zhongke Repchain Cloud Technol Co Ltd Guiyang Peoples R China Guiyang Informat Technol Res Inst Guiyang Peoples R China

ISBN: (纸本)9798350351040;9798350351033

Soysauce-like aroms based wine needs to be stored in a dark place during long-term storage. When recognizing images of the base wine cellar, the quality of the collected images is greatly affected by environmental light, and the imaging quality is poor and there are many noise points in low light environments. The influence of ambient light poses significant challenges to computer vision. To address the issues of poor image visibility and high interference in low illuminance environments, this article proposes an improved low-illuminance image enhancement method based on Retinex-Net network. Convert the input image from RGB domain to IISV color space for processing, introduce denoising convolutional neural network Deam-Net network into the reflection image of V component for denoising, enhance the color of the illumination image of V component through spatial attention module and channel attention module, and perform bilateral filtering and contrast stretching on the S component. Finally, fuse all components and convert to RGB for obtain the enhanced image. Prove through verification have shown that the low -illumination images enhanced by the algorithm proposed in this article have improved brightness, prominent details, minimal image distortion, and are realistic and natural. They are superior to other algorithms from subjective feelings and objective evaluation indicators.

关键词： component Retinex Net image decomposition Attention module Low light image enhancement

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