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arXiv 2023年

作者： Jiang, Chengjia Wang, Tao Li, Sien Wang, Jinyang Wang, Shirui Antoniou, Antonios Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan China College of Computer and Data Science Fuzhou University Fuzhou China Department of Computer Science and Engineering European University Cyprus Nicosia Cyprus

We address the problem of learning new classes for semantic segmentation models from few examples, which is challenging because of the following two reasons. Firstly, it is difficult to learn from limited novel data to capture the underlying class distribution. Secondly, it is challenging to retain knowledge for existing classes and to avoid catastrophic forgetting. For learning from limited data, we propose a pseudo-labeling strategy to augment the few-shot training annotations in order to learn novel classes more effectively. Given only one or a few images labeled with the novel classes and a much larger set of unlabeled images, we transfer the knowledge from labeled images to unlabeled images with a coarse-to-fine pseudo-labeling approach in two steps. Specifically, we first match each labeled image to its nearest neighbors in the unlabeled image set at the scene level, in order to obtain images with a similar scene layout. This is followed by obtaining pseudo-labels within this neighborhood by applying classifiers learned on the few-shot annotations. In addition, we use knowledge distillation on both labeled and unlabeled data to retain knowledge on existing classes. We integrate the above steps into a single convolutional neural network with a unified learning objective. Extensive experiments on the Cityscapes and KITTI datasets validate the efficacy of the proposed approach in the self-driving domain. Code is available from https://***/ChasonJiang/FSCILSS. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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Image Matching Algorithm Under Adverse Conditions Based on SIFT

Image Matching Algorithm Under Adverse Conditions Based on S...

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IEEE International Symposium on information (IT) in Medicine and Education, ITME

作者： Hao Chen Xinrong Cao Zuoyong Li Lihui Lin College of Computer and Big Data Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou China Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry School of Mathematics and Computer Science Wuyi University Fujian China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

Image matching technology is crucial in computer vision applications. However, the traditional SIFT (Scale-Invariant Feature Transform) algorithm often faces challenges under adverse conditions, such as a high number of mismatched points and few accurately matched points. Additionally, in practical scenarios, environmental factors like lighting changes and image blurring further reduce image similarity. Therefore, this paper proposes an improved SIFT-based image matching algorithm to address these challenges. First, SIFT is used to extract image features, and a bidirectional FLANN (Fast Library for Approximate Nearest Neighbors) matching strategy is employed to initially filter out incorrect features. Then, the RANSAC (Random Sample Consensus) algorithm is used to screen out unreliable matching point pairs. Finally, the matching accuracy is evaluated based on the remaining pure matching point pairs. Experimental results demonstrate that, compared to the traditional SIFT algorithm, this improved algorithm achieves higher matching accuracy and robustness under adverse conditions, exhibiting superior performance.

关键词： Location awareness Matched filters Accuracy Image matching Lighting Transforms Feature extraction Robustness Libraries information technology

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Mitigating Low-Frequency Bias: Feature Recalibration and Frequency Attention Regularization for Adversarial Robustness

arXiv

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arXiv 2024年

作者： Zhang, Kejia Weng, Juanjuan Cai, Yuanzheng Luo, Zhiming Li, Shaozi Department of Artificial Intelligence Xiamen University Xiamen361005 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350108 China College of Information Science and Technology Jinan University Guangzhou510632 China

Ensuring the robustness of deep neural networks against adversarial attacks remains a fundamental challenge in computer vision. While adversarial training (AT) has emerged as a promising defense strategy, our analysis reveals a critical limitation: AT-trained models exhibit a bias toward low-frequency features while neglecting high-frequency components. This bias is particularly concerning as each frequency component carries distinct and crucial information: low-frequency features encode fundamental structural patterns, while high-frequency features capture intricate details and textures. To address this limitation, we propose High-Frequency Feature Disentanglement and Recalibration (HFDR), a novel module that strategically separates and recalibrates frequency-specific features to capture latent semantic cues. We further introduce frequency attention regularization to harmonize feature extraction across the frequency spectrum and mitigate the inherent low-frequency bias of AT. Extensive experiments demonstrate our method’s superior performance against white-box attacks and transfer attacks, while exhibiting strong generalization capabilities across diverse scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Attention Residual Network for White Blood Cell Classification with WGAN Data Augmentation 11

Attention Residual Network for White Blood Cell Classificati...

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11th International Conference on information Technology in Medicine and Education, ITME 2021

作者： Zhao, Meng Jin, Lingmin Teng, Shenghua Li, Zuoyong College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China

ISBN: (纸本)9781665406796

In medicine, white blood cell (WBC) classification plays an important role in clinical diagnosis and treatment. Due to the similarity between classes and lack of training data, the precise classification of WBC is still challenging. To alleviate this problem, we propose an attention residual network for WBC image classification on the basis of data augmentation. Specifically, the attention residual network is composed of multiple attention residual blocks, an adaptive average pooling layer, and a full connection layer. The channel attention mechanism is introduced in each residual block to use the feature maps of WBC learned by a high layer to generate the attention map for a low layer. Each attention residual block also introduces depth separable convolution to extract the feature of WBC and decrease the training costs. The Wasserstein Generative adversarial network (WGAN) is used to create synthetic instances to enhance the size of training data. Experiments on two image datasets show the superiority of the proposed method over several state-of-the-art methods. © 2021 IEEE.

关键词： Generative adversarial networks

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Few-Shot Class-Incremental Semantic Segmentation via Pseudo-Labeling and Knowledge Distillation

Few-Shot Class-Incremental Semantic Segmentation via Pseudo-...

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information Science, Parallel and Distributed Systems (ISPDS), International Conference on

作者： Chengjia Jiang Tao Wang Sien Li Jinyang Wang Shirui Wang Antonios Antoniou Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China The Key Laboratory of Cognitive Computing and Intelligent Information Processing Fujian Education Institutions Wuyi University Wuyishan China College of Computer and Data Science Fuzhou University Fuzhou China Department of Computer Science and Engineering European University Cyprus Nicosia Cyprus

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Concentration control of SMB System Based on Hierarchical Fuzzy controller 6th

Concentration Control of SMB System Based on Hierarchical Fu...

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6th Euro-China Conference on intelligent Data Analysis and Applications, ECC 2019

作者： Xie, Chao-Fan Xu, Lu-Xiong Wang, Ruobin Zhang, Fuquan Department of Electrical Engineering I-Shou University Kaohsiung84001 Taiwan Electronic Information and Engineering Institute Fuqing Branch of Fujian Normal University Fuqing350300 China Key Laboratory of Nondestructive Testing Fuqing Branch of Fujian Normal University Fuqing350300 China School of Information Science and Technology North China University of Technology Beijing100144 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China

ISBN: (纸本)9789811650352

Chromatographic separation is an indispensable and efficiency technology in the manufacturing process of chemical products and biomedicine. Simulated moving bed (SMB) is a kind of process that can increase the efficiency of adsorbents in the adsorbent bed. SMB system contains several sectors of flow rate, the switching time of valves and other possible influencing variables, Because of its complexity;it is very difficult to achieve precise prediction and control. The software on the market is basically used for specific materials, and the parameters are set manually, so it is impossible to control and adjust the process. This paper firstly digitalization the simulation process, then analysis sensitivity of regional flow velocity on export concentration, on this basis, the rule of fuzzy control is established. The Hierarchical fuzzy controller is applied to the SMB system to observe the control of the final concentration of the controller. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fuzzy control

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MEFA-Net: A mask enhanced feature aggregation network for polyp segmentation

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Computers in Biology and Medicine 2025年 186卷 109601-109601页

作者： Ke, Xiao Chen, Guanhong Liu, Hao Guo, Wenzhong Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350116 China

Accurate polyp segmentation is crucial for early diagnosis and treatment of colorectal cancer. This is a challenging task for three main reasons: (i) the problem of model overfitting and weak generalization due to the multi-center distribution of data;(ii) the problem of interclass ambiguity caused by motion blur and overexposure to endoscopic light;and (iii) the problem of intraclass inconsistency caused by the variety of morphologies and sizes of the same type of polyps. To address these challenges, we propose a new high-precision polyp segmentation framework, MEFA-Net, which consists of three modules, including the plug-and-play Mask Enhancement Module (MEG), Separable Path Attention Enhancement Module (SPAE), and Dynamic Global Attention Pool Module (DGAP). Specifically, firstly, the MEG module regionally masks the high-energy regions of the environment and polyps through a mask, which guides the model to rely on only a small amount of information to distinguish between polyps and background features, avoiding the model from overfitting the environmental information, and improving the robustness of the model. At the same time, this module can effectively counteract the "dark corner phenomenon" in the dataset and further improve the generalization performance of the model. Next, the SPAE module can effectively alleviate the inter-class fuzzy problem by strengthening the feature expression. Then, the DGAP module solves the intra-class inconsistency problem by extracting the invariance of scale, shape and position. Finally, we propose a new evaluation metric, MultiColoScore, for comprehensively evaluating the segmentation performance of the model on five datasets with different domains. We evaluated the new method quantitatively and qualitatively on five datasets using four metrics. Experimental results show that MEFA-Net significantly improves the accuracy of polyp segmentation and outperforms current state-of-the-art algorithms. Code posted on https://***/

关键词： Endoscopy

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Theme-Based Literature Analysis in the Field of Transportation 16th

Theme-Based Literature Analysis in the Field of Transportati...

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16th International Conference on intelligent information Hiding and Multimedia Signal processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of information Technology, Applications and Tools, FITAT 2020

作者： Zhang, Sifan Lu, Hao Zhang, Fuquan Zhu, Yifan Wang, Rongrong Computer School Beijing Institute of Technology Beijing10089 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Min-Jiang University Fuzhou350121 China

ISBN: (纸本)9789813367562

Transportation is an important research field of engineering science, and its related research covers multiple fields and links in policies, methods, technologies, and applications. In order to sort out the research context and development status of the transportation field and provide scientific and technological information support for scientific researchers in the field, this article analyzes the literature published in SCI journals in the transportation field from 2008 to 2018 through the topic analysis method. The analysis results show that the current main research results in the field of transportation have formed different clusters around keywords such as cognitive radio, electric vehicles, optimization and simulation, and intelligent transportation systems, and the research development process of different countries is different. For example, the research results of China in recent years have gone from energy efficiency, automobile emissions to intelligent transportation, while the United States has been focusing on the research of intelligent transportation systems. Over time, China has gradually focused on the cutting-edge technology in the field of transportation, and its research results have greatly promoted the development of research in the field of transportation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Energy efficiency

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Polyp Segmentation Neural Network with Simple Encoding Structure and Reverse Attention Module

Polyp Segmentation Neural Network with Simple Encoding Struc...

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IEEE International Symposium on information (IT) in Medicine and Education, ITME

作者： Jie Wang Zuoyong Li Haiping Xu Xuesong Cheng Shenghua Teng College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China Fujian Key Laboratory of Medical Big Data Engineering Fujian Provincial Hospital Fuzhou China College of Mathematics and Data Science Minjiang University Fuzhou China

Colorectal cancer is a common malignancy. In colonoscopy images, computer-assisted polyp segmentation helps doctors diagnose and treat disorders more precisely. In recent years, some methods based on deep convolutional neural networks have made significant breakthroughs in this task. However, only a few algorithms explicitly consider the effect of light intensity on segmentation performance, which results in algorithms needing to be more robust for complex samples. This paper proposes a neural network combining a simple encoding structure with a reverse attention module. Specifically, the model uses HarDNet68 as the backbone network, adds a camouflage identification module (CIM) to enhance the capture of polyp details on the underlying features, and finally utilizes adaptive feature fusion with the reverse attention module (RAM). The proposed model effectively addresses the challenge of poor segmentation quality in colonoscopy images caused by inconsistent illumination. Extensive experiments on five datasets show that the proposed model performs well in polyp segmentation.

关键词：

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Fall Detection Based on Person Detection and Multi-target Tracking 11

Fall Detection Based on Person Detection and Multi-target Tr...

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11th International Conference on information Technology in Medicine and Education, ITME 2021

作者： Xu, Teng Chen, Jian Li, Zuoyong Cai, Yuanzheng School of Electronic Electrical Engineering and Physics Fujian University of Technology Fuzhou350118 China College of Computer and Control Engineering Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China

ISBN: (纸本)9781665406796

Recently, official statistics reported that the Chinese population aged 60 and above has been 26.402 million, which accounts for 18.70% of total population. It is urgent to develop fall detection technologies for alleviating the risk causing by falling of elder person. In this paper, we propose a real-time, high-precision, and deep learning-based fall detection method with automatic person detection and tracking. Specifically, the proposed method first improves the YOLOv3 network to more efficiently detect person and extract feature maps of the object. Then, it inputs the extracted feature maps from the YOLOv3 into a multi-target tracking network for cascade matching and IOU matching in a Deep SORT algorithm, respectively. Next, it improves YOLOv5 network to detect posture anomalies. Finally, it refines the detected posture anomalies for obtaining the final fall detection result. Experimental results show that the proposed method simultaneously improves accuracy and efficiency of the fall detection. © 2021 IEEE.

关键词： Fall detection

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