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8th International Conference on Image, Vision and Computing, ICIVC 2023

作者： Gao, Yujie Li, Yadong Liu, Bin Tian, Yifan Zhu, Minfeng Luo, Jian School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Applications in Complicated System Tianjin China

ISBN: (纸本)9798350335231

Sign language plays an important role in information transmission and emotional communication between the hearing-impaired and the outside world. It is expressed through a rapid and complex combination of gestures, body postures and facial expressions. In this paper, Google's MediaPipe framework is used to extract and process key point information of hands. Depth features are used to supplement the RBG diagram. A framework of skeleton sensing multi-modal recognition suitable for complex scenes is proposed. This can be used to deal with the problem of sign language recognition of multi-modal information in different scenes. However, most of the existing sign language recognition methods are traditional convolutional networks and LSTM. Although they have excellent achievements in image recognition and video recognition, their application in sign language recognition does not conform to the characteristics of sign language. In this paper, a Swin Transformer model is used to process both depth information and RGB information. Finally, statistical data fusion method was used to fuse the depth prediction results with RGB prediction results. Experimental analysis shows that this method performs well in sign language recognition in complex scenes compared with existing methods. © 2023 IEEE.

关键词： Image recognition

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Hyperbolic cosine transformer for LiDAR 3D object detection

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Optoelectronics Letters 2025年

作者： Tong Jigang Yang Fanhang Yang Sen Du Shengzhi Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems and Intelligent Robot Laboratory， Tianjin University of Technology Department of Electrical Engineering， Tshwane University of Technology

Recently， Transformer has achieved great success in computer vision. However， it is constrained because the spatial and temporal complexity grows quadratically with the number of large points in 3D object detection applications from point clouds. Previous point-wise methods are suffering from time consumption and limited receptive fields to capture information among points. To address these limitations， we propose the cosh-attention， which reduces the computation complexity of space and time from the quadratic order to linear order with respect to the number of points. In the cosh-attention， the traditional softmax operator is replaced by non-negative ReLU activation and hyperbolic-cosine-based operator with re-weighting mechanism. Then based on the key component， cosh-attention， we present a two-stage hyperbolic cosine transformer (ChTR3D) for 3D object detection from point clouds. It refines proposals by applying cosh-attention in linear computation complexity to encode rich contextual relationships among points. Extensive experiments on the widely used KITTI dataset and Waymo Open Dataset demonstrate that， compared with vanilla attention， the cosh-attention significantly improves the inference speed with competitive performance. Experiment results show that， among two-stage state-of-the-art methods using point-level features to refine proposals， the proposed ChTR3D is the fastest one.

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Research on hearing-impaired EEG Emotion Recognition Based on Deep Residual Shrinkage Networks

Research on hearing-impaired EEG Emotion Recognition Based o...

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第42届中国控制会议

作者： Meiyi Cheng Qiang Gao Zhongli Bai Yirui Chen Yang Liu Yu Song Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems

With the development of affective computing and computer science,electroencephalogram(EEG) based emotion recognition has attracted much more *** this paper,we collected EEG signals from fifteen healthy people and fifteen hearing-impaired people when they were watching five kinds of emotional pictures(fear,anger,sadness,happiness and neutral).The collected EEG signals are preprocessed to remove artifacts by independent component analysis(ICA).Then the differential power spectral density,entropy,and wavelet entropy features were extracted(PSD,DE,WE).The Deep Residual Shrinkage Networks(DRSNs) were composed of residual building units(RBUs) and an attention mechanism was used to capture the representative features,and then made a *** classification results prove that using DE as a feature for classification is better than other features,with an average accuracy being 76.35% and 80.47% for the hearing-impaired group and the normal group,***,from the brain topography,we found that there are certain differences in brain function between hearing-impaired and healthy people.

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Multi-view stereo network with regional consistency and discrepancy cost volume

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Optoelectronics Letters 2025年

作者： XIA GUANGZHI ZHANG JINGLEI JIA XIN Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems， School of Electrical Engineering and Automation， Tianjin University of Technology Engineering Training Center， Tianjin University of Technology

The goal of Multi-View Stereo (MVS) is to robustly recover an accurate 3D point cloud from multiple views. In this paper， we propose a novel Multi-View Stereo network with Regional consistency and Discrepancy cost volume， denoted as MVSRD. Firstly， a full-feature interaction transformer is presented， which learns the regional consistency between the reference and source views， improving the robustness of reconstruction. Secondly， a discrepancy cost volume is designed to emphasize pixel-level differences between feature volumes. It facilitates the construction of high-quality cost volume to enhance the accuracy of reconstruction. Extensive experiments on the DTU and Tanks & Temples datasets demonstrate that our MVSRD achieves state-of-the-art performance.

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Differential Enhancement and Commonality Fusion for RGBT Tracking 20

Differential Enhancement and Commonality Fusion for RGBT Tra...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Yang, Jianrong Dong, Enzeng Tong, Jigang Yang, Sen Zhang, Zufeng Li, Wenyu Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Intelligent Robot Laboratory 391 Binshui West Road Tianjin China Nankai University College of Artificial Intelligence 94 Weijin Road Tianjin China

ISBN: (纸本)9798350320831

Target tracking is a fundamental task and an important part in the field of computer vision. The image information contained in the visible (RGB) modality and thermal infrared (T) modality has differences. RGBT tracking utilizes this difference to fuse the information of the two modalities, fully combining the uniqueness and complementarity of the information of the two modalities. How to maximize the uniqueness of individual modalities in RGBT tracking and how to effectively combine the complementarity of the two modalities is the key issue in RGBT tracking. For the combination of the uniqueness and complementarity of the two modalities, a novel Differential Enhancement and Commonality Fusion Network (DECFNet) is designed in this paper, in which the main components include a feature enhancement module for the visible modality and a feature enhancement module for the thermal infrared modality, as well as a fusion module that effectively fuses the information of both. The results on three RGB T tracking benchmark datasets demonstrate the effectiveness of the proposed method. © 2023 IEEE.

关键词： Target tracking

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A Global Perception Attention-based Network for Point Cloud Completion 20

A Global Perception Attention-based Network for Point Cloud ...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Wu, Fengyong Dong, Enzeng Tong, Jigang Yang, Sen Li, Wenyu Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Intelligent Robot Laboratory 391 Binshui West Road Tianjin China Nankai University College of Artificial Intelligence 94 Weijin Road Tianjin China

ISBN: (纸本)9798350320831

The purpose of point cloud completion is designed to high-precision complete the complete shape from local observations. However, the previous approaches have tended to focus on a good perception of local details and lack a better overall representation of the point cloud. To solve this problem, we propose a global attention point cloud reconstruction method based on SnowflakeNet. We designed a global focus module to integrate the global structure information of the original point cloud on the basis of the local shape context features, so as to enhance the perception ability of the network to the local fine structure and the overall shape of the point cloud. At the same time, we improved the point cloud generation part of the network and added SPD that can re-optimize the position of the completion point cloud, so as to achieve more accurate completion results. We conducted a series of experiments on the Completion3D dataset and confirmed that our network outperformed the original algorithm. © 2023 IEEE.

关键词： Computer vision

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Deep Neural Network Based Object Detection Algorithm With optimized Detection Head for Small Targets 20

Deep Neural Network Based Object Detection Algorithm With op...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Zhu, Yongchang Dong, Enzeng Tong, Jigang Yang, Sen Zhang, Zufeng Li, Wenyu Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Intelligent Robot Laboratory 391 Binshui West Road Tianjin China Nankai University College of Artificial Intelligence 94 Weijin Road Tianjin China

ISBN: (纸本)9798350320831

Target detection is a significant research direction in the realm of computer vision, aimed at analyzing and localizing targets in input images to obtain their categories and locations. However, this task is faced with several challenges in complex scenarios, such as large variations in target scales, similarity to the background, dense number, and overlapping with each other, making it difficult for the target detection model to extract different scales and discriminative target features adaptively, leading to low detection accuracy and weak generalization ability. To tackle the aforementioned problems, this study proposes a novel approach by optimizing the detection head of the model, invoking decoupled detection heads, dividing the classification and localization tasks, and optimizing for small targets. This approach effectively reduces the false detection rate and the missed detection rate, enabling the network to better discriminate and recognize distant objects as well as objects of relatively small size. Moreover, the proposed approach outperforms the yolov7 model by 0.8% in the COCO dataset, not only improving the accuracy of detecting distant and small targets but also exhibiting better performance in conventional target detection. Additionally, this approach is capable of handling small targets, overlapping targets, or relatively dense targets, significantly expanding its practical use range. © 2023 IEEE.

关键词： Object detection

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A Single-Frame Face Anti-Spoofing Algorithm With Circular CDC and Multi-Scale Spatial Attention 20

A Single-Frame Face Anti-Spoofing Algorithm With Circular CD...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： He, Shan Dong, Enzeng Tong, Jigang Yang, Sen Liang, Xuehui Li, Wenyu Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Intelligent Robot Laboratory 391 Binshui West Road Tianjin China Nankai University College of Artificial Intelligence 94 Weijin Road Tianjin China

ISBN: (纸本)9798350320831

Face anti-spoofing is to prevent face images with attack properties from entering face recognition and causing confusion or spoofing of the face recognition function. Since most face presentation attacks do not possess true deep information, some recent works use this information as supervisory signal. In this paper, we continue such a pattern by proposing a circular CDC (Central Difference Convolution) with larger sensory field and contextual semantic awareness, and use it in a backbone network for improving the network model's ability to characterize texture features and multi-scale information. Combined with the spatial attention method concat multiple scale feature maps are used to fuse the feature map information from multiple scales. We conduct a series of experiments on OULU-NPU and demonstrate that our network accuracy is better than the original algorithm, especially in terms of generalizability across devices and different environments. © 2023 IEEE.

关键词： Face recognition

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Multilevel Decomposition Time Aggregation Graph Convolution Networks for Skeleton-Based Action Recognition 20

Multilevel Decomposition Time Aggregation Graph Convolution ...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Li, Wenhua Dong, Enzeng Tong, Jigang Yang, Sen Zhang, Zufeng Li, Wenyu Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Intelligent Robot Laboratory 391 Binshui West Road Tianjin China Nankai University College of Artificial Intelligence 94 Weijin Road Tianjin China

ISBN: (纸本)9798350320831

Skeleton-based human action recognition has become a popular topic among researchers. This is because using skeletal data provides a robust solution to problems encountered in complex environments, such as changes in perspective and background interference. The robustness of skeletal data enables recognition methods to focus on more specific and relevant features. We propose a model called multilevel decomposition time aggregation graph convolution network (MDT-GCN), which utilizes a multilevel graph convolution kernel to capture higher-order spatial dependence relationships between joints. This is achieved by decomposing a human topology graph into smaller graphs, each of which has its own graph convolution kernel. To further enhance the performance of our model, we employ a two-flow framework and channel topology refinement strategy. Our experiments on the NTU-RGB+D60 and NTU-RGB+D120 datasets demonstrate that our MDT-GCN network outperforms the previous algorithm and significantly improves the accuracy of action recognition. © 2023 IEEE.

关键词： Convolution

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Liquid refractive index measurement method based on orthogonal polarization laser self-mixing interference

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Optoelectronics Letters 2025年

作者： ZHAO Yan WANG Haipeng DU Shengzhi TONG Jigang KANG Jun School of Electrical Engineering and Automation， Tianjin University of Technology Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Department of Electrical Engineering， Tshwane University of Technology

In this paper， a novel method combining orthogonal polarization laser self-mixing interference is proposed. The method utilizes a rotating cuvette to measure the refractive index of liquids at different concentrations. The cuvette is filled with the liquid to be measured and rotated by a certain angle. The change in the number of interference fringes， caused by comparing an empty cuvette with a liquid-filled cuvette， is used to calculate the refractive index of the liquid. A four-fold logic subdivision algorithm is then used to improve measurement resolution. The experimental results show that for pure water and different NaCl and glucose solutions concentrations， the average relative errors are 0.47%， 0.59%， and 2.17%， respectively， with the maximum relative error within ±2.54%. The standard deviation of all solutions is less than 3.4%.

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