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Research on Forest Fire image Recognition Algorithm based on Color Feature Statistics 6

Research on Forest Fire Image Recognition Algorithm Based on...

引用

6th ieee international conference on Intelligent Computing and signal Processing, ICSP 2021

作者： Li, Wang Xiaobo, Shen Jun, Cai Ying, Li Huainan Normal University College of Electronic Engineering Huainan232038 China

ISBN: (纸本)9780738143705

In order to improve fire recognition, a forest fire feature extraction algorithm based on color feature statistics is proposed, which combines manual segmentation and statistics of the mean and variance of each channel in RGB color space to construct a Gaussian distribution probability density function. Set a reasonable threshold according to the probability density distribution of the fire area image, and then the threshold is used to judge the probability density of the sample image. Experimental results show that this method can quickly and effectively identify the flames of forest fires in complex scenarios. © 2021 ieee.

关键词： Fires

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Multi-Prototype Grouping for Continual Learning in Visual Question Answering

Multi-Prototype Grouping for Continual Learning in Visual Qu...

引用

international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Licheng Zhang Zhendong Mao Yixing Peng Zheren Fu Yongdong Zhang School of Information Science and Technology University of Science and Technology of China Hefei China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Visual Question Answering (VQA) aims to answer questions utilizing information from both textual and visual modalities. New data categories and novel combinations of the two modalities will continuously emerge in practical applications, necessitating continual learning. For this unique compositional generalization challenge, existing work adopts prototype learning to separately capture sample-invariant features of question types and image objects, retrieving the most similar prototype from two modalities as generalizable representations. However, for semantic fuzzy samples that deviate from the corresponding prototype, relying on a single prototype may lead to error-prone representation learning. Additionally, for the catastrophic forgetting challenge, existing methods employ memory to store past samples but lack an effective selection of samples to be stored, merely updating the memory through random sampling. In this paper, we propose ProtoGroup, a multi-prototype grouping continual learning framework for VQA, which groups prototypes based on their similarity to obtain more accurate and stable sample-invariant features. We also devise a memory sample selection method based on the correlation with prototype groups from old tasks. Experimental results demonstrate our framework outperforms previous methods significantly across multiple datasets and settings.

关键词： Continuing education Representation learning Visualization Accuracy Semantics Prototypes signal processing Data aggregation Question answering (information retrieval) Speech processing

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Data Analysis Methods for Health Monitoring Sensors: A survey 45

Data Analysis Methods for Health Monitoring Sensors: A surve...

引用

45th Annual international ieee-Computer-Society Computers, Software, and Applications conference (COMPSAC)

作者： Sobhan, Shahriar Islam, Saiful Valero, Maria Shahriar, Hossain Ahamed, Sheikh, I Kennesaw State Univ CyberSecur Inst Kennesaw GA 30144 USA Kennesaw State Univ Dept Informat Technol Kennesaw GA 30144 USA Marquette Univ Dept Comp Sci Milwaukee WI 53233 USA

ISBN: (纸本)9781665424639

Innovations in health monitoring systems are fundamental for the continuous improvement of remote healthcare. With the current presence of SARS-CoV-2, better known as COVID-19, in people's daily lives, solutions for monitoring heart and especially respiration and pulmonary functions are more needed than ever. In this paper, we survey the current approaches that utilize the advantages of sensor technologies to sense, analyze, and estimate health data related to respiration, heart, and sleep monitoring. We focus on illustrating the signal processing and machine learning techniques used on each approach to facilitate researchers' understanding of how data is processed nowadays. We have classified the reviewed papers into two main categories: contact and contactless sensors. In each category, we discuss the different types of used sensors, the data analysis technique, and the accuracy of those techniques.

关键词： signal processing (SP) machine learning (ML) remote monitoring internet of things (IoT) respiration and pulmonary function

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Underwater image Restoration via Polymorphic Large Kernel CNNs

Underwater Image Restoration via Polymorphic Large Kernel CN...

引用

international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Xiaojiao Guo Yihang Dong Xuhang Chen Weiwen Chen Zimeng Li FuChen Zheng Chi-Man Pun University of Macau Baoshan Univeristy Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Huizhou Univeristy The Hong Kong University of Science and Technology (Guangzhou) Shenzhen Polytechnic University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Underwater image Restoration (UIR) remains a challenging task in computer vision due to the complex degradation of images in underwater environments. While recent approaches have leveraged various deep learning techniques, including Transformers and complex, parameter-heavy models to achieve significant improvements in restoration effects, we demonstrate that pure CNN architectures with lightweight parameters can achieve comparable results. In this paper, we introduce UIR-PolyKernel, a novel method for underwater image restoration that leverages Polymorphic Large Kernel CNNs. Our approach uniquely combines large kernel convolutions of diverse sizes and shapes to effectively capture long-range dependencies within underwater imagery. Additionally, we introduce a Hybrid Domain Attention module that integrates frequency and spatial domain attention mechanisms to enhance feature importance. By leveraging the frequency domain, we can capture hidden features that may not be perceptible to humans but are crucial for identifying patterns in both underwater and on-air images. This approach enhances the generalization and robustness of our UIR model. Extensive experiments on benchmark datasets demonstrate that UIR-PolyKernel achieves state-of-the-art performance in underwater image restoration tasks, both quantitatively and qualitatively. Our results show that well-designed pure CNN architectures can effectively compete with more complex models, offering a balance between performance and computational efficiency. This work provides new insights into the potential of CNN-based approaches for challenging image restoration tasks in underwater environments. The code is available at https://***/CXH-Research/UIR-PolyKernel.

关键词： Convolutional codes Convolution Computational modeling Frequency-domain analysis Computer architecture Benchmark testing Transformers image restoration Computational efficiency Kernel

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Tobacco Impurities Detection with Deep image Segmentation Method on Hyperspectral Imaging

Tobacco Impurities Detection with Deep Image Segmentation Me...

引用

ieee international conference on signal Processing, Communications and Computing (ICSPCC)

作者： Linruize Tang Min Zhao Shuaikai Shi Jie Chen Jinwei Li Qiang Li Rengang Li Center of Intelligent Acoustics and Immersive Communications School of Marine Science and Technology Northwestern Polytechnical University Xi'an China Luxi Tobacco Redrying Factory Yunnan tobacco redrying Co. Ltd. Yunnan China

The detection of impurities in tobacco raw materials is an important task, and the performance of detection algorithms directly affects the quality of tobacco products. The hyperspectral imaging system simultaneously captures the visible and near-infrared spectra containing rich spectral information that can accurately reflect material categories. In this paper, we propose a novel deep learning-based tobacco impurities detection method on hyperspectral data. It is a U-net like structure that progressively extracts multi-scale discriminant features and fuses them to find the impurities in the scene. Experiments conducted on the collected dataset show that the proposed segmentation model yields superior performance compared to other detection methods.

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Using Asymmetric Attention Mechanism Access Class Barring with Gated Recurrent Unit for an End-to-end image Compression

Using Asymmetric Attention Mechanism Access Class Barring wi...

引用

international conference on Humanoid, Nanotechnology, Information technology, Communication and Control, Environment and Management (HNICEM)

作者： Mary Jane C. Samonte Suping Liu Yanan Li School of Information Technology Mapúa University Makati City Philippines

ISBN: (数字)9798350381177

ISBN: (纸本)9798350381184

The exponential expansion of the internet has resulted in an overwhelming abundance of multimedia data. However, image and video compression technology faces challenges such as insufficient feature extraction, inaccurate bit rate estimation, and quantization errors. Within this study, we introduce optimization methods to address these issues. The AT-ACB module is introduced to enhance compact feature extraction using an Asymmetrical Convolution Block and hybrid attention module. Besides, the parallel conditional context module captures global relevance and reduces spatial redundancy. Then, the Tiki-Taka training method mitigates quantizer mismatch. This paper mainly researches the end-to-end image compression optimization scheme built upon deep learning. Finally, the experimental results demonstrate that the proposed optimization method achieves BD-Rate savings of 3.95%, 0.97%, and 3.18% on the Kodak dataset, resulting in a total BD-Rate saving of 6.89%.

关键词： Training image coding Attention mechanisms Quantization (signal) Redundancy Optimization methods Logic gates

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Voltage Differential Control of Multi-terminal DC Distribution Network based on Active Power Balance 4

Voltage Differential Control of Multi-terminal DC Distributi...

引用

4th ieee China international Electrical and Energy conference, CIEEC 2021

作者： Yu, Jiangsheng Huang, Yunfei Yuan, Chanpei Jiang, Keteng Li, Haibo Lei, Yi Huang, Yanwan Geng, Shengjie Dongguan Power Supply Bureau Dongguan China Tsinghua Sichuan Energy Internet Research Institute Chengdu China

ISBN: (纸本)9781728171494

With the rise of renewable energy and the development of power electronics, the DC distribution network will play an increasingly important role. Firstly, this paper described the typical topology and operation mode of multi- terminal flexible DC distribution network. Secondly, in order to solve the problems of voltage control deviation and strong communication dependence in the traditional control strategy of multi-terminal flexible DC distribution network, an adaptive voltage differential control (AVDC) was proposed, which enabled the converter stations to adjust voltage and maintain power balance instead of the master station. By detecting the bus voltage signal, the converter station can switch mode independent of the communication system. Finally, a simulation model was built based on PSCAD / EMTDC platform, and the effectiveness and superiority of the proposed control strategy were verified by simulating the master station exit and load removal conditions. © 2021 ieee.

关键词： Adaptive control systems

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Ultrasound image Quality Control Using Speech-Assisted Switchable CycleGAN

Ultrasound Image Quality Control Using Speech-Assisted Switc...

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Jaeyoung Huh Shujaat Khan Eun Sun Lee Jong Chul Ye Dept of Bio and Brain Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea Department of Radiology Chung-Ang University Hospital Seoul Korea Kim Jaechul Graduate School of AI Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea

Unlike computed tomography (CT) and magnetic resonance imaging (MRI) in which the image quality (IQ) is controlled by predefined acquisition setups, the IQ of ultrasound (US) is heavily dependent upon operators. In particular, an operator often adjusts the system parameters in a real-time manner based on his/her preference. Unfortunately, there are many cases where such real-time control of IQ is difficult, especially in the intensive care unit (ICU) or operating room (OR), since the operator should simultaneously treat patients in sterile status and adjust the system parameters. To address this, inspired by the recent success of Switchable CycleGAN using Adaptive Instance Normalization (AdaIN) layers, here we propose a novel speech-assisted Switchable CycleGAN architecture that can be controlled by operator’s verbal commands. Specifically, we employ a Speech Recognition Module (SRM) to generate AdaIN codes for Switchable CycleGAN. In particular, our SRM is based on the pre-trained model so that it can be less effected by the operator’s voice and robustly generate AdaIN codes. Various experimental results confirm that the proposed method can successfully control IQ of US by speech.

关键词： image quality Ultrasonic imaging Codes Text recognition Magnetic resonance imaging Computed tomography Switches

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Boosting Semi-Supervised Federated Learning with Model Personalization and Client-Variance-Reduction

Boosting Semi-Supervised Federated Learning with Model Perso...

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Shuai Wang Yanqing Xu Yanli Yuan Xiuhua Wang Tony Q. S. Quek Singapore University of Technology and Design Singapore The Chinese University of Hong Kong Shenzhen China Beijing Institute of Technology Beijing China Huazhong University of Science and Technology Wuhan China

Recently, federated learning (FL) has been increasingly appealing in distributed signal processing and machine learning. Nevertheless, the practical challenges of label deficiency and client heterogeneity form a bottleneck to its wide adoption. Although numerous efforts have been devoted to semi- supervised FL, most of the adopted algorithms follow the same spirit as FedAvg, thus heavily suffering from the adverse effects caused by client heterogeneity. In this paper, we boost the semi-supervised FL by addressing the issue using model personalization and client-variance-reduction. In particular, we propose a novel and unified problem formulation based on pseudo-labeling and model interpolation. We then propose an effective algorithm, named FedCPSL, which judiciously adopts the schemes of a novel momentum-based client- variance-reduction and normalized averaging. Convergence property of FedCPSL is analyzed and shows that FedCPSL is resilient to client heterogeneity and obtains a sublinear convergence rate. Experimental results on image classification tasks are also presented to demonstrate the efficacy of FedCPSL over the benchmark algorithms.

关键词： Interpolation Machine learning algorithms Federated learning signal processing algorithms signal processing Classification algorithms Task analysis

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Underwater image Restoration with Light-Aware Progressive Network

Underwater Image Restoration with Light-Aware Progressive Ne...

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Jian Yang Chen Li Xuelong Li School of Computer Science Northwestern Polytechnical University Xi’an P.R. China School of Artificial Intelligence OPtics and ElectroNics (iOPEN) Northwestern Polytechnical University Xi’an P.R. China Ministry of Industry and Information Technology Key Laboratory of Intelligent Interaction and Applications(Northwestern Polytechnical University) Xi’an P.R. China

Underwater images suffer from different types of degradation such as blur and color cast. It is difficult to obtain images with both color equalization and rich texture in various underwater scenes. To alleviate these issues, this paper proposes a reflected light-aware multi-scale progressive restoration network. A restoration framework is designed based on physical imaging model. It can accurately remove scattering blur of various underwater images and output a preliminary restoration result. A multi-scale progressive enhancement module is proposed to further enrich the image details and improve the generalization ability of the model by fully extracting detailed and spatial features. For the issues of color cast and low contrast, a new chromatic aberration correction mechanism is introduced, which makes the image color balanced. The experimental results show that our method can obtain underwater images with enriching texture and harmonious color distribution as compared with other state-of-the-art methods.

关键词： Degradation Visualization image texture image color analysis Scattering Imaging signal processing

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