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检索条件"任意字段=International Congress on Image and Signal Processing, BioMedical Engineering and Informatics"

共 2036 条记录，以下是81-90 订阅

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Prediction System for Wind Power Generation Based on Machine Learning Models 16

Prediction System for Wind Power Generation Based on Machine...

引用

16th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2023

作者： Liu, Zhipeng Zhao, Huaitian Xie, Junjie Fang, Zhewei Chen, Ruiyuan Li, Yongfeng Taizhou University School of Electronic and Information Engineering Zhejiang Taizhou China

ISBN: (纸本)9798350330755

Based on 20 wind power datasets from different regions, this article uses a series of feature engineering, data normalization, construction of training and validation sets, and five models including TCN, MLP, RNN, Transformer, and LSTM for training and testing. The wind power prediction system is established based on these five models. Finally, the web interface is implemented using Flask and Bootstrap frameworks, demonstrating data classification and test results. © 2023 IEEE.

关键词： Data visualization

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Enhancing Plant Recognition in Intelligent Agricultural Irrigation Systems Using Stable Diffusion and Lora Models with CFloss 17

Enhancing Plant Recognition in Intelligent Agricultural Irri...

引用

17th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2024

作者： Lu, Yingzhu Tang, Chengyu Fan, Yawen Zhou, Qin Nanjing University of Posts and Telecommunications Department of Computer Science and Technology Nanjing China Nanjing Forestry University Department of Computer Science and Technology Nanjing China School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China School of Information Science and Engineering East China University of Science and Technology Shanghai China

ISBN: (纸本)9798331507398

This paper proposes a novel approach to optimize the plant recognition function of intelligent agricultural irrigation system, utilizing Stable Diffusion model and Lora (Low-Rank Adaptation) model. Stable Diffusion model is a latent diffusion model that converts text into images, while Lora is a method of fine-tuning large models by reducing trainable parameters while maintaining model performance as much as possible. By combining the Stable Diffusion model and Lora model, this paper trains custom models for each plant species using a small amount of data and generates corresponding images of each variety to assist training, thus optimizing classification results. In addition, to address the issue of imbalanced data, this study also introduces Focal Loss to mitigate the impact of both class imbalance and difficulty in classification. Building upon Focal Loss, CFloss is created as the model's loss function. This method was tested on the OxfordFlower102 dataset used by the original model, and the results demonstrate that it effectively improves classification accuracy and significantly enhances the plant recognition function of the irrigation system. © 2024 IEEE.

关键词： image classification

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A Rigid Registration Pipeline for TMJ CBCT Time-Series images with Removal of Remodel Structures 17

A Rigid Registration Pipeline for TMJ CBCT Time-Series Image...

引用

17th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2024

作者： Zhong, Chen Li, Jupeng Feng, Jiling Ma, Ruohan Li, Gang Beijing Jiaotong University Beijing China Peking University School and Hospital of Stomatology Beijing China

ISBN: (纸本)9798331507398

The accurate estimation of structure and position in relation to Temporomandibular Joint (TMJ) is crucial for Temporomandibular Joint Disorder (TMD) diagnosis and treatment. However, existing automatic rigid registration methods, such as Scale Invariant Feature Transform (SIFT), often have some limitations, including reliance on artificial external features, requiring high computational effort, or ignoring the deformation and physiological changes in anatomical structures. To address these problems, we propose a pipeline for TMJ CBCT time-series images to predict the matrix of translations and rotations in rigid registration task. In our proposed pipeline, a segmentation network is employed to eliminate the effects of complex anatomical structures, and a landmark-based 3D rigid registration is adopted for preliminary rigid registration for avoid poor performance on images with large positional variations. Finally, a self-supervised neural network trained on real medial images is used to predict the rotation and translation parameters. Our network is evaluated on 44 pairs of clinical images. SIRS-Net+ achieves an average Dice coefficient of 0.8020 (approximately 8% higher than SIFT) and an average NCC of 0.7264 (about 10% higher than SIFT). © 2024 IEEE.

关键词： image registration

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Robust image Encryption Based on Improved Logistic Chaos and Cross Chaotic Diffusion 16

Robust Image Encryption Based on Improved Logistic Chaos and...

引用

16th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2023

作者： Guo, Yuan Zhai, Ping Wang, Chong Li, Haibo Liu, Ziqi Heilongjiang University Department of Computer Science and Technology Harbin150080 China Qiqihar University Department of Computer and Control Engineering Qiqihar161006 China

ISBN: (纸本)9798350330755

To address the problem of weak robustness of some current image encryption algorithms, this paper proposes a strong robust encryption algorithm. The Logistic chaotic system is improved to produce more random sequence distribution. The plaintext hash is used to construct the initial key of chaotic system and the spiral matrix transformation is used for the first scrambling. A novel chaotic sequence cross-diffusion method is constructed by introducing two values of plaintext correlation val1 and val2. Through plaintext transformation of chaotic sequences, it improves the plaintext sensitivity, which can resist the selective plaintext attack, and has strong robustness. In order to avoid the position of some pixels not changed in the first spiral matrix transformation, cross-scrambling is carried out again after cross-diffusion. Therefore a scrambling - diffusing - scrambling encryption framework is built. The experimental results show that the proposed algorithm can resist brute force attacks and selective plaintext attack. It has strong robustness and high security. Especially it has perfect performance in anti-shear attacks, which has significance for image security transmission and is very suitable for image encryption. © 2023 IEEE.

关键词： Chaotic systems

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signal Reconstruction Method Based CNN for Atrial Fibrillation Detection 17

Signal Reconstruction Method Based CNN for Atrial Fibrillati...

引用

17th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2024

作者： Gao, Jiahui Li, Yongjian Chen, Meng Zhang, Xiuxin Sun, Yiheng Jiang, Xinge Wei, Shoushui School of Control Science and Engineering Shandong University Jinan250100 China School of Information Science and Electrical Engineering Shandong Jiaotong University Jinan250357 China

ISBN: (纸本)9798331507398

Wearable devices can monitor patients' heart activity in real-time, supporting early detection and treatment. However, ensuring high performance while maintaining low computational requirements is crucial for wearable devices. To enhance disease identification by providing more dimensions and features, and to improve accuracy and interpretability of data analysis, this study explores an innovative signal reconstruction method. By converting one-dimensional (I-D) time series into two-dimensional (2-D) signal matrices, this method better captures important information in the time series. Subsequently, by employing multi-scale convolutional kernels, the network structure is deepened, enabling the capture of spatial information across different ranges. This approach helps learn more complex features while effectively reducing the number of parameters. On the MIT-BIH Atrial Fibrillation Database, this method achieved an accuracy of 99.30%. Furthermore, validation on a clinical dataset yielded an accuracy of 99.04%. Compared to one-dimensional time series, this method improved accuracy by 1.17% in atrial fibrillation identification tasks. Additionally, compared to other common types of 2-D images, the new input format significantly reduces the computational burden during model training, making it more suitable for practical applications in wearable devices. © 2024 IEEE.

关键词： Convolutional neural networks

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An Improved Multiplicative Fusion Method for High-resolution SAR and Optical images 16

An Improved Multiplicative Fusion Method for High-resolution...

引用

16th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2023

作者： Li, Hao Liang, Dongxing China Aerospace Standardization Institute Electronic Components Assurance Department Beijing China Xidian University National Laboratory of Radar Signal Processing Shaanxi China

ISBN: (纸本)9798350330755

The fusion of high-resolution SAR and optical images, aiming to produce an enhanced image which benefits from both the spectral information of the optical image and the spatial details of the SAR image, is not an easy task in practice. In this paper, we address a problem of spectral distortion in high-resolution SAR-optical fusion and propose an improved multiplicative fusion method, which is simple and effective. The proposed method leverages a power mapping function applied on a SAR image to preserve the details of the SAR image while keeping the chromatics of the optical image during the multiplicative fusion of the optical image and the SAR image. The real-data experiments illustrate the strong preservation of spectral information and spatial details with our proposed method. In addition, the proposed method can control the amount of spatial gain or spectral content loss in the fused result by adjusting the parameter of the mapping function. © 2023 IEEE.

关键词： image fusion

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Hash-Based Food image Retrieval with Enhanced Feature Correlation Vision Transformeraper 17

Hash-Based Food Image Retrieval with Enhanced Feature Correl...

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17th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2024

作者： Yuan, Xin Chen, YongHui Wang, FuPan School of Computer Science and Technology Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9798331507398

image retrieval is a crucial research task in the field of computer vision, and food image retrieval is an application of fine-grained image retrieval in food computation tasks. The feature extraction of food image visual spatial information is constrained by the subtle differences among various types of food. The effective combination of contextual and texture information also affects the final outcome of food image retrieval. Furthermore, differences in food processing methods introduce information errors, posing greater challenges for food image retrieval tasks. This paper is based on the pretrained Vision Transformer (ViT) model and introduces an enhanced image feature association ViT hashing network (IFCVH-Net). Targeting the associations between subcategories of food information, IFCVH-Net incorporates a weight feature integration module that uses a self-attention mechanism to assign different weights to encoded feature vectors, thereby enhancing the extraction of global features from food images. Additionally, to make full use of visual spatial information effectively, a dual-channel feature association module is designed. The IFCVH-Net model is evaluated using three popular hashing image retrieval frameworks: Greedy Hash (GreedyHash), Central Similarity Quantization (CSQ), and Deep Polarized Network (DPN). Experiments comparing the IFCVH-Net model with mainstream network models such as AlexNet, ResNet50, ViT-B_32, ViT-B_16, EVHNet-16, and EVHNet-32 on the Food-101 and Vireo Food-172 datasets show that the IFCVH-Net model outperforms the other models in terms of retrieval accuracy. © 2024 IEEE.

关键词： CBAM Hybrid Attention Food image Retrieval Hash Retrieval Self-Attention Mechanism Vision Transformer Model

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Revealing the Fairness Issues of Text-to-image Generation Models Based on Metamorphic Testing 17

Revealing the Fairness Issues of Text-to-Image Generation Mo...

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17th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2024

作者： Ruan, MengYao Pan, Ya Fan, Yong School of Computer Science and Technology Southwest University of Science and Technology Mian Yang China

ISBN: (纸本)9798331507398

The Text-to-image Generation(T2I) Models acquires implicit social biases during the training process, which can easily cause social disputes and negative impacts in sensitive fields such as news broadcasting, educational illustrations, and so on. There are many factors contributing to this situation. Most of the existing studies only focus on the combed biases of gender and skin color. Therefore, this paper proposes a method based on metamorphosis test to reveal the fairness of T2I models in terms of gender, skin color, and region. The part of speech and influential positions are divided in the input text, and the prompt text is modified by using names with regional characteristics in combination with three mutation operators: entity replacement, entity attribute enhancement, and multilingual description. The large language model is used for optimization to expand the test cases for generating images. To alleviate the test oracle problem of the T2I models, three metamorphic relations are constructed for verification in terms of gender, skin color, and image-text consistency. This paper has conducted a large number of experiments on T2I models: Kolors, Wan Xiang, and Stable Diffusion. The experimental results show that after variation using the variation operator, the proportion of female entities increases by at least 11.4%, and the proportion of dark-skinned objects increases by at least 15.7%. © 2024 IEEE.

关键词： Economic and social effects

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CBCTformer: Transformer Model for Sparse-View CBCT Artifact Reduction in image-Guided Radiotherapy 17

CBCTformer: Transformer Model for Sparse-View CBCT Artifact ...

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17th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2024

作者： Zhao, Xuzhi Chen, Keming Du, Yi Li, Jupeng Sui, Lu Peng, Yahui School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Peking University Cancer Hospital & Institute Key Laboratory of Carcinogenesis and Translational Research Ministry of Education/Beijing Department of Radiation Oncology Beijing China Institute of Medical Technology Peking University Health Science Center Beijing China

ISBN: (纸本)9798331507398

This study proposes a Transformer-based image domain model, named CBCTformer, for artifact reduction in sparse-view cone-beam computed tomography (CBCT) used in image-guided radiotherapy (IGRT). The CBCTformer model is built on a Swin Transformer U-Net architecture, comprising both encoder and decoder components. The model was trained, validated, and tested on CBCT data from 163, 30, and 30 real patients, respectively. Quantitative metrics including root-mean-square error (RMSE), peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and feature similarity (FSIM) were used to evaluate the model's performance. The CBCTformer model was compared with four state-of-art (SOTA) convolutional neural network (CNN)-based models on the 1/8 sparse-view reconstruction task. On the test set, the CBCTformer model achieved averaged RMSE, PSNR, SSIM and FSIM values of 0.001098, 35.67 dB, 0.8714 and 0.97, respectively, outperforming all the four SOTA CNN models across all metrics. In conclusion, the proposed CBCTformer model effectively reduces artifacts in sparse-view CBCT images, thereby improving CBCT image quality in IGRT. © 2024 IEEE.

关键词： Convolutional neural networks

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Fault Detection and Diagnosis Model for Photovoltaic Array Based on Multi-Scale Transformer 17

Fault Detection and Diagnosis Model for Photovoltaic Array B...

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17th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2024

作者： Liao, Haibing Wang, Wang Li, Yinquan Liu, Shaoping Tang, Chaoyang Yuan, Ye College of Engineering Shantou University China Guangdong Botong New Energy Technology Co. Ltd China

ISBN: (纸本)9798331507398

This paper presents an advanced automatic fault detection and diagnosis method for photovoltaic (PV) arrays aimed at enhancing the efficiency, reliability, and safety of PV systems. Traditional artificial intelligence methods, such as machine learning, although useful, are typically applied to structured, feature-rich two-dimensional data, thus lacking the capability to effectively classify samples represented as arrays of sampling points with only curve characteristics. This limitation may hinder the model's effectiveness and lead to local optimization issues. Existing deep learning techniques have limited feature extraction capabilities and often iterate repetitively over modules without extracting richer features from multiple levels and perspectives. To address these challenges, we propose a novel intelligent fault detection and diagnosis method utilizing a specifically designed multi-scale Transformer network. This network analyzes I-V curves through bilinear interpolation and incorporates irradiance and temperature data, ensuring a comprehensive input basis. We conducted thorough fault simulation experiments on the Simulink platform to evaluate the performance of our proposed method. The results highlight significant achievements in accuracy, generalization capability, reliability, and training efficiency. © 2024 IEEE.

关键词： Deep learning

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