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13th International Conference on information Technology in Medicine and Education, ITME 2023

作者： Wang, Jie Li, Zuoyong Xu, Haiping Cheng, Xuesong Teng, Shenghua College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China Fujian Key Laboratory of Medical Big Data Engineering Fujian Provincial Hospital Fuzhou350001 China College of Mathematics and Data Science Minjiang University Fuzhou350121 China

ISBN: (纸本)9798350319156

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. © 2023 IEEE.

关键词： Diseases

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Manifold Learning Inspired Mating Restriction for Evolutionary Constrained Multiobjective Optimization 11th

Manifold Learning Inspired Mating Restriction for Evolutiona...

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11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021

作者： Li, Lianghao He, Cheng Cheng, Ran Pan, Linqiang Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9783030720612

Mating restriction strategies are capable of restricting the distribution of parent solutions for effective offspring generation in evolutionary algorithms (EAs). Studies have shown the importance of these strategies in improving the performance of EAs for multiobjective optimization. Our previous study proposed a specific manifold learning inspired mating restriction (MLMR) strategy. It has shown promising capability of solving multiobjective optimization problems (MOPs) with complicated Pareto set shapes. However, the effect of mating restriction strategies in solving constrained MOPs is yet to be well studied. Here, we investigate the effectiveness of MLMR for solving constrained MOPs. The MLMR strategy is embedded into some representative multiobjective EAs and tested on various benchmark constrained MOPs. Experimental results indicate the encouraging performance of MLMR in constrained multiobjective optimization. © 2021, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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VRAS: A virtual rehearsal assistant system for live performance 2nd

VRAS: A virtual rehearsal assistant system for live performa...

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2nd International Conference on Smart Vehicular Technology, Transportation, Communication and Applications, VTCA 2018

作者： Wu, Yufeng Ding, Gangyi Li, Hongsong Xue, Tong Jiao, Di Huang, Tianyu Zhang, Longfei Zhang, Fuquan Xu, Lin Digital Performance and Simulation Key Laboratory School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China Key Laboratory of Nondestructive Testing Fuqing Branch of Fujian Normal University Fuzhou350300 China

ISBN: (纸本)9783030045845

The VR technologies are widely adopted for training purposes by providing the users with educational virtual experience. In this work, we propose an immersive VR system that help the choreographers and the dancers to facilitate their dance rehearsal experience. The system integrates motion capture devices and head-mounted displays (HMDs). The motions of the dancers, their partners, and the choreographers are captured and projected into a virtual dancing scene in an interactive frame rate. The dancers who are wearing the HMDs, are allowed to observe the synthesized virtual performances within a virtual stage space from several selected third-person views. These are the audience’s view, the dancing partner’s view, and the choreographer’s view. Such synthesized external self-images augment dancer’s perception of their dance performance and their understanding of the choreography. Feedbacks from the participants indicate the proposed system is effective and the preliminary experimental results agree with our observations. © Springer Nature Switzerland AG 2019.

关键词： Helmet mounted displays

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Spatio-Temporal Context Modeling for Road Obstacle Detection 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Wu, Xiuen Wang, Tao Liang, Lingyu Li, Zuoyong Ching, Fum Yew College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China Ministry of Education Key Laboratory of Computer Network and Information Integration Southeast University Nanjing211189 China Guangzhou510320 China Penang11800 Malaysia

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

Road obstacle detection is an important problem for vehicle driving safety. In this paper, we aim to obtain robust road obstacle detection based on spatio-temporal context modeling. Firstly, a data-driven spatial context model of the driving scene is constructed with the layouts of the training data. Then, obstacles in the input image are detected via the state-of-the-art object detection algorithms, and the results are combined with the generated scene layout. In addition, to further improve the performance and robustness, temporal information in the image sequence is taken into consideration, and the optical flow is obtained in the vicinity of the detected objects to track the obstacles across neighboring frames. Qualitative and quantitative experiments were conducted on the Small Obstacle Detection (SOD) dataset and the Lost and Found dataset. The results indicate that our method with spatio-temporal context modeling is superior to existing methods for road obstacle detection. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Adaptive Histogram Thresholding-Based Leukocyte Image Segmentation 15th

Adaptive Histogram Thresholding-Based Leukocyte Image Segmen...

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15th International Conference on intelligent information Hiding and Multimedia Signal processing, IIH-MSP 2019, held in conjunction with the 12th International Conference on Frontiers of information Technology, Applications and Tools, FITAT 2019

作者： Zhou, Xiaogen Wang, Chuansheng Li, Zuoyong Zhang, Fuquan College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China School of Computer Science and Technology Harbin University of Science and Technology Harbin China

ISBN: (纸本)9789811397097

To improve the accuracy of leukocyte segmentation, this paper presents a novel method based on adaptive histogram thresholding (AHT). The proposed method first employs color component combination and AHT to extract the nucleus of leukocyte and utilizes image color features to remove the complex backgrounds such as red blood cells (RBCs) and substantial dyeing impurities. Then, Canny edge detection is performed to extract the entire leukocyte. Finally, the cytoplasm of the leukocyte is obtained by subtracting the nucleus with the entire leukocyte. Experimental results on an image dataset containing 60 leukocyte images show that the proposed method generates more accurate segmentation results than the counterparts. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Image segmentation

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Color space volume and superpixel based leukocyte image segmentation 10

Color space volume and superpixel based leukocyte image segm...

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10th International Conference on information Technology in Medicine and Education, ITME 2019

作者： Zhou, Xiaogen Li, Zuoyong Wang, Chuansheng Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of Mathematics and Computer Science Fuzhou University Fuzhou China School of Computer Science and Technology Harbin University of Science and Technology Harbin China

ISBN: (纸本)9781728139173

In this paper, we propose a novel method based on color space volume and multimodal histogram thresholding (MHT) for leukocyte segmentation in human blood smear images under the rapid staining condition. The proposed method first defines a new color space volume to highlight the leukocyte's nucleus region, and it then uses the MHT method to extract the nucleus. Then, the proposed method takes the central region in the superpixel segmentation result as an initial leukocyte region, and it then uses the MHT method to extract the leukocyte region. Finally, the cytoplasm is obtained by subtracting the nucleus region from the leukocyte region. Experimental results on a dataset containing 300 leukocyte images under rapid staining condition show that the proposed method provides a higher segmentation accuracy than the counterparts. © 2019 IEEE.

关键词： Superpixels

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Kernel-Guided Texture-Aware Blind Super-Resolution via Uncertainty Learning 13

Kernel-Guided Texture-Aware Blind Super-Resolution via Uncer...

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13th International Conference on information Technology in Medicine and Education, ITME 2023

作者： Zhao, Tingting Li, Zuoyong Quan, Li Zeng, Kun Teng, Shenghua College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China College of Computer Science and Big Data Fuzhou University Fuzhou350121 China

ISBN: (纸本)9798350319156

The accuracy of kernel estimation is critical to the performance of blind super-resolution (SR). Traditional kernel estimation methods usually use L1 or L2 loss function to minimize the difference between the estimated kernel and the ground-truth kernel. This tends to make the estimated kernel converge towards the average of all possible kernels, deviating from the ground-truth kernel. To improve the performance of kernel estimation, this paper proposes an uncertainty loss for training a kernel estimation network, focusing on regions with high uncertainty (variance) in the kernel. In addition, a texture-aware SR network is proposed that utilizes the Gumbel Softmax trick to pay more attention to the complex regions of the image texture, thus improving the SR performance. Extensive experiments on synthetic datasets show that our approach achieves promising performance. © 2023 IEEE.

关键词： Textures

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Distract Your Attention: Multi-head Cross Attention Network for Facial Expression Recognition

arXiv

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arXiv 2021年

作者： Wen, Zhengyao Lin, Wenzhong Wang, Tao Xu, Ge Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China

This paper presents a novel facial expression recognition network, called Distract your Attention Network (DAN). Our method is based on two key observations in biological visual perception. Firstly, multiple facial expression classes share inherently similar underlying facial appearance, and their differences could be subtle. Secondly, facial expressions simultaneously exhibit themselves through multiple facial regions, and for recognition, a holistic approach by encoding high-order interactions among local features is required. To address these issues, this work proposes DAN with three key components: Feature Clustering Network (FCN), Multi-head Attention Network (MAN), and Attention Fusion Network (AFN). Specifically, FCN extracts robust features by adopting a large-margin learning objective to maximize class separability. In addition, MAN instantiates a number of attention heads to simultaneously attend to multiple facial areas and build attention maps on these regions. Further, AFN distracts these attentions to multiple locations before fusing the feature maps to a comprehensive one. Extensive experiments on three public datasets (including AffectNet, RAF-DB, and SFEW 2.0) verified that the proposed method consistently achieves state-of-the-art facial expression recognition performance. The DAN code is publicly available. Copyright © 2021, The Authors. All rights reserved.

关键词： Face recognition

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Automatic Identification for Projector Brand and Model Number 1

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6th International Conference on Data Mining and Big Data, DMBD 2021

作者： Li, Zuoyong Wang, Weice Zhang, Fuquan Fan, Haoyi Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou350002 China School of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China

ISBN: (数字)9789811674761

ISBN: (纸本)9789811674754

The projector production process needs to pack manufactured projectors. The key step of projector packing is to check whether the brand and model number of the projector is correct or not, for avoiding the projector’s packing error. To achieve automation of identifying projector brand and model number for improving the efficiency of the projector packing, we proposed an automatic identification method by using image processing, character recognition, and string matching. Specifically, image grayscale processing, image gradient calculation, morphological operations, and image thresholding are sequentially performed to determine candidate target regions located by a projector's brand and model number. Then, two shape prior-based image features are designed to perform target region screening, i.e., excluding fake target regions as far as possible. Next, the character recognition technology is used to extract the texts in the target regions. Finally, string matching is performed to obtain identification results of the projector's brand and model number. Experimental results on several projectors demonstrate the effectiveness of the proposed method on automatic identification of projector brand and model number. The proposed method provides a general framework for identifying the brand and model number of other similar electronic products. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Character recognition

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An Anomalous Traffic Detection Approach for the Private Network Based on Self-learning Model 1

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3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020

作者： Han, Weijie Xue, Jingfeng Zhang, Fuquan Zhang, Yingfeng School of Computer Science and Technology Beijing Institute of Technology Beijing China School of Space Information Space Engineering University Beijing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China Equipment Development Department Beijing100071 China

ISBN: (数字)9783030622237

ISBN: (纸本)9783030622220

Although being isolated from the external network, the private network is still faced with some security threats, such as violations communications, malware attacks, and illegal operations. It is an attractive approach to recognize these security threats by discovering the underlying anomalous traffic. By studying the anomalous traffic detection technologies, an anomalous traffic detection approach is developed by capturing and analyzing the network packets, detecting the anomaly traffic that occurs in the network, and then detects anomalous behaviors of the network timely. In order to enhance its effectiveness and efficiency, a self-learning model is proposed and deployed in the detection approach. Finally, we conduct necessary evaluations about the proposed approach. The test results show that the approach can reach a good effect for detecting the unknown anomalous traffic. © 2020, Springer Nature Switzerland AG.

关键词： Learning systems

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