检索结果-内蒙古大学图书馆

IEEE Transactions on Artificial Intelligence 2022年第2期3卷 254-264页

作者： Zhang, Hong-Bo Zhou, Yi-Zhong Dong, Li-Jia Lei, Qing Du, Ji-Xiang Department of Computer Science and Technology Huaqiao University Xiamen361000 China Xiamen Key Laboratory of Computer Vision and Pattern Recognition The Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Xiamen361000 China

In this article, a new vision- and grating-sensor-based intelligent unmanned settlement (IUS) system is proposed for convenience stores to automatically recognize the shopping behavior of customers, record their identities, and generate invoices. First, we design a new IUS architecture, which includes a shelf module and exit module. To achieve automatic settlement for each customer, a shopping event detection method is proposed. In this method, a vision-based human pose estimation algorithm is used to detect a human form standing in front of a shelf. The hand actions of each customer are detected by a grating sensor, and an image recognition method based on a convolutional neural network (CNN) is applied to recognize the items in the hands of customers. To reduce the image annotation workload, we propose a semisupervised training method for the recognition network. Based on hand action detection and item recognition, a shopping event recognition method is designed for the system, and a facial image of the customer corresponding to each shopping behavior is captured. Finally, each detected shopping event is added to the invoice of the corresponding customer via a facial recognition method. To verify the effectiveness of the proposed IUS system, we have built a handheld item image dataset and a shopping event dataset for an unmanned convenience store. The experimental results show that the proposed system can accurately recognize shopping behaviors and generate invoices. © 2020 IEEE.

关键词： Sales

来源：评论

学校读者我要写书评

暂无评论

Tile selection method based on error minimization for photomosaic image creation

引用

Frontiers of computer Science 2021年第3期15卷 165-172页

作者： Hongbo ZHANG Xin GAO Jixiang DU Qing LEI Lijie YANG Department of Computer Science and Technology Huaqiao UniversityXiamen 361021China Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao UniversityXiamen 361021China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao UniversityXiamen 361021China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen 518055China

Photomosaic images are composite images composed of many small images called *** its overall visual effect,a photomosaic image is similar to the target image,and photomosaics are also called“montage art”.Noisy blocks and the loss of local information are the major obstacles in most methods or programs that create photomosaic *** solve these problems and generate a photomosaic image in this study,we propose a tile selection method based on error minimization.A photomosaic image can be generated by partitioning the target image in a rectangular pattern,selecting appropriate tile images,and then adding them with a weight *** on the principles of montage art,the quality of the generated photomosaic image can be evaluated by both global and local *** the proposed framework,via an error function analysis,the results show that selecting a tile image using a global minimum distance minimizes both the global error and the local error ***,the weight coefficient of the image superposition can be used to adjust the ratio of the global and local ***,to verify the proposed method,we built a new photomosaic creation dataset during this *** experimental results show that the proposed method achieves a low mean absolute error and that the generated photomosaic images have a more artistic effect than do the existing approaches.

关键词： photomosaic image tile image target image error minimization mean absolute error

来源：评论

学校读者我要写书评

暂无评论

AMST: Object tracking based on collaborative framework with adaptive multi-strategy

引用

Information Sciences 2025年 718卷

作者： Rui Xu Si Chen Yan Yan Da-Han Wang Shunzhi Zhu Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology Xiamen 361024 China Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen 361005 China

Deep object tracking can be modeled by online learning to adapt to the appearance changes, or offline learning to achieve fast tracking speed. However, either online or offline learning trackers are still difficult to continuously cope with challenging tracking scenes, due to the cumulative errors easily caused by online learning trackers and the object appearance changes often neglected by offline learning trackers. To overcome this problem, we propose a novel object tracking method based on collaborative framework with adaptive multi-strategy, enabling automatic switch between the online and offline learning trackers. In this framework, we first design the variable action set (VAS) module to adaptively choose the different trackers, tracking strategies, and template strategies with multiple action labels. Moreover, we incorporate the template update and selection (TUS) module, which dynamically updates and selects templates from a memory unit to adapt to object appearance changes. To update the action set and the memory unit, we further devise the online reliability evaluation (ORE) module that not only evaluates the tracking result and the tracker itself, but also estimates the quality and quantity of templates. Comprehensive experiments on challenging short-term and long-term tracking benchmarks demonstrate the remarkable performance of the proposed method.

关键词：

来源：评论

学校读者我要写书评

暂无评论

LVAgent: Long Video Understanding by Multi-Round Dynamical Collaboration of MLLM Agents

arXiv

引用

arXiv 2025年

作者： Chen, Boyu Yue, Zhengrong Chen, Siran Wang, Zikang Liu, Yang Li, Peng Wang, Yali Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Tsinghua University Beijing China Dept. of Comp. Sci. & Tech. Institute for AI Tsinghua University Beijing China Shanghai Artificial Intelligence Laboratory China Shanghai Jiao Tong University China

Existing Multimodal Large Language Models (MLLMs) encounter significant challenges in modeling the temporal context within long videos. Currently, mainstream Agent-based methods use external tools (e.g., search engine, memory banks, OCR, retrieval models) to assist a single MLLM in answering long video questions. Despite such tool-based support, a solitary MLLM still offers only a partial understanding of long videos, resulting in limited performance. In order to better address long video tasks, we introduce LVAgent, the first framework enabling multi-round dynamic collaboration of MLLM agents in long video understanding. Our methodology consists of four key steps: 1) Selection: We pre-select appropriate agents from the model library to form optimal agent teams based on different tasks. 2) Perception: We design an effective retrieval scheme for long videos, improving the coverage of critical temporal segments while maintaining computational efficiency. 3) Action: Agents answer long video-related questions and exchange reasons. 4) Reflection: We evaluate each agent’s performance in each round of discussion and optimize the agent team for dynamic collaboration. The agents iteratively refine their answers by multi-round dynamical collaboration of MLLM agents. LVAgent is the first agent system method that outperforms all closed-source models (including GPT-4o) and open-source models (including InternVL-2.5 and Qwen2-VL) in the long video understanding tasks. Our LVAgent achieves an accuracy of 80% on four mainstream long video understanding tasks. Notably, on the LongVideoBench dataset, LVAgent improves accuracy by up to 14.3% compared with SOTA. © 2025, CC BY-NC-SA.

关键词： Open systems

来源：评论

学校读者我要写书评

暂无评论

Graph convolutional network combined with random walks and graph attention network for node classification

引用

The Journal of China Universities of Posts and Telecommunications 2024年第3期31卷 1-14页

作者： Chen Yong Xie Xiaozhu Weng Wei College of Computer and Information Engineering Xiamen University of TechnologyXiamen 361024China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of TechnologyXiamen 361024China

Graph conjoint attention(CAT)network is one of the best graph convolutional networks(GCNs)frameworks,which uses a weighting mechanism to identify important neighbor ***,this weighting mechanism is learned based on static information,which means it is susceptible to noisy nodes and edges,resulting in significant *** this paper,a method is proposed to obtain context dynamically based on random walk,which allows the context-based weighting mechanism to better avoid noise ***,the proposed context-based weighting mechanism is combined with the node content-based weighting mechanism of the graph attention(GAT)network to form a model based on a mixed weighting *** model is named as the context-based and content-based graph convolutional network(CCGCN).CCGCN can better discover important neighbors,eliminate noise edges,and learn node embedding by message *** show that CCGCN achieves state-of-the-art performance on node classification tasks in multiple datasets.

关键词： graph neural network(GNN) graph convolutional network(GCN) semi-supervised classification graph analysis

来源：评论

学校读者我要写书评

暂无评论

Periodic Action Temporal Localization Method Based on Two-Path Architecture for Product Counting in Sewing Video 15th

Periodic Action Temporal Localization Method Based on Two-Pa...

引用

15th International Conference on Intelligent Computing, ICIC 2019

作者： Huang, Jin-Long Zhang, Hong-Bo Du, Ji-Xiang Zheng, Jian-Feng Peng, Xiao-Xiao Department of Computer Science and Technology Huaqiao University Xiamen China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Xiamen China

ISBN: (纸本)9783030267650

Automatically product counting in the handmade process plays a vital role in the manufacturing industry, especially at the sewing industry. Nevertheless, there is currently a few methods to count the product number in the hand sewing process from surveillance video automatically. Due to the sewing procedure is a cyclical action, the product counting in hand sewing process is regarded as periodic action temporal localization and counting problem. In this paper, in order to solve this problem, we propose a novel two-path method, based on pose estimation and region-based convolutional neural network. The pose estimation method is used to obtain the trajectory information of human joint points, and the periodic action is located by detecting the periodic changes in joint trajectory. An effective two-threshold method is proposed to locate each action and count the number of periodic action from the trajectory information. To more accurately localization, we use a convolutional neural network to predict whether the workbench is empty or not. We fuse the results of joint trajectory and the status of the workbench to adjust the final periodic action localization and counting the number. To verify the proposed method, we built a new video database collected in the real sewing industry. The experimental results show that the proposed method is effective and constructive at periodic action localization and counting in the video for the sewing industry. © 2019, Springer Nature Switzerland AG.

关键词： Neural networks

来源：评论

学校读者我要写书评

暂无评论

Dual Branch PnP Based Network for Monocular 6D Pose Estimation

引用

Intelligent Automation & Soft Computing 2023年第6期36卷 3243-3256页

作者： Jia-Yu Liang Hong-Bo Zhang Qing Lei Ji-Xiang Du Tian-Liang Lin Department of Computer Science and Technology Huaqiao UniversityXiamen361000China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao UniversityXiamen361000China Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao UniversityXiamen361000China College of Mechanical Engineering and Automation Xiamen361000China

Monocular 6D pose estimation is a functional task in the field of com-puter vision and *** recent years,2D-3D correspondence-based methods have achieved improved performance in multiview and depth data-based ***,for monocular 6D pose estimation,these methods are affected by the prediction results of the 2D-3D correspondences and the robustness of the per-spective-n-point(PnP)*** is still a difference in the distance from the expected estimation *** obtain a more effective feature representation result,edge enhancement is proposed to increase the shape information of the object by analyzing the influence of inaccurate 2D-3D matching on 6D pose regression and comparing the effectiveness of the intermediate ***,although the transformation matrix is composed of rotation and translation matrices from 3D model points to 2D pixel points,the two variables are essentially different and the same network cannot be used for both variables in the regression ***,to improve the effectiveness of the PnP algo-rithm,this paper designs a dual-branch PnP network to predict rotation and trans-lation ***,the proposed method is verified on the public LM,LM-O and YCB-Video *** ADD(S)values of the proposed method are 94.2 and 62.84 on the LM and LM-O datasets,*** AUC of ADD(-S)value on YCB-Video is *** experimental results show that the performance of the proposed method is superior to that of similar methods.

关键词： 6D pose monocular RGB edge enhancement dual-branch PnP 2D-3D correspondence

来源：评论

学校读者我要写书评

暂无评论

Efficient Audio-Visual Speaker recognition via Deep Heterogeneous Feature Fusion 12th

Efficient Audio-Visual Speaker Recognition via Deep Heteroge...

引用

12th Chinese Conference on Biometric recognition, CCBR 2017

作者： Liu, Yu-Hang Liu, Xin Fan, Wentao Zhong, Bineng Du, Ji-Xiang Department of Computer Science Huaqiao University Xiamen361021 China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Xiamen361021 China

ISBN: (纸本)9783319699226

Audio-visual speaker recognition (AVSR) has long been an active research area primarily due to its complementary information for reliable access control in biometric system, and it is a challenging problem mainly attributes to its multimodal nature. In this paper, we present an efficient audio-visual speaker recognition approach via deep heterogeneous feature fusion. First, we exploit a dual-branch deep convolutional neural networks (CNN) learning framework to extract and fuse the high-level semantic features of face and audio data. Further, by considering the temporal dependency of audio-visual data, we embed the fused features into a bidirectional Long Short-Term Memory (LSTM) networks to produce the recognition result, though which the speakers acquired under different challenging conditions can be well identified. The experimental results have demonstrated the efficiency of our proposed approach in both audio-visual feature fusion and speaker recognition. © 2017, Springer International Publishing AG.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

WDA-Net: Weakly-Supervised Domain Adaptive Segmentation of Electron Microscopy

WDA-Net: Weakly-Supervised Domain Adaptive Segmentation of E...

引用

2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Qiu, Dafei Yi, Jiajin Peng, Jialin Huaqiao University Xiamen Key Laboratory of Computer Vision and Pattern Recognition China Meitu Inc Xiamen China Huaqiao University College of Computer Science and Technology China

ISBN: (纸本)9781665468190

Accurate segmentation of organelle instances is essential for electron microscopy analysis. Despite the outstanding performance of fully supervised methods, they highly rely on sufficient per-pixel annotated data and are sensitive to domain shift. Aiming to develop a highly annotation-efficient approach with competitive performance, we focus on weakly-supervised domain adaptation (WDA) with a type of extremely sparse and weak annotation demanding minimal annotation efforts, i.e., sparse point annotations on only a small subset of object instances. To reduce performance degradation arising from domain shift, we explore multi-level transferable knowledge through conducting three complementary tasks, i.e., counting, detection, and segmentation, constituting a task pyramid with different levels of domain invariance. The intuition behind this is that after investigating a related source domain, it is much easier to spot similar objects in the target domain than to delineate their fine boundaries. Specifically, we enforce counting estimation as a global constraint to the detection with sparse supervision, which further guides the segmentation. A cross-position cut-and-paste augmentation is introduced to further compensate for the annotation sparsity. Extensive validations show that our model with only 15% point annotations can achieve comparable performance as supervised models and shows robustness to annotation selection. © 2022 IEEE.

关键词： Electron microscopy

来源：评论

学校读者我要写书评

暂无评论

Human motion correction and representation method from motion camera

引用

Journal of Engineering 2017年第1期1卷 370-375页

作者： Zhang, Hong-Bo Guo, Feng Zhang, Miaohui Lin, Ying Hsiao, Tsung-Chih Department of Computer Science and Technology Huaqiao University Xiamen China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Xiamen China School of Information Science and Engineering Xiamen University Xiamen China Institute of Energy Jiangxi Academy of Sciences Jiangxi Province China

Motion estimation is a basic issue for many computer vision tasks, such as human-computer interaction, motion objection detection and intelligent robot. In many practical scenes, the object movement goes with camera motion. Generally, motion descriptors directly based on optical flow are inaccurate and have low discrimination power. To this end, a novel motion correction method is proposed and a novel motion feature descriptor called the motion difference histogram (MDH) for recognising human action is proposed in this study. Motion estimation results are corrected by background motion estimation and MDH encodes the motion difference between the background and the objects. Experimental results on video shot with camera motion show that the proposed motion correction method is effective and the recognition accuracy of MDH is better than that of the state-of-the-art motion descriptor.

关键词： Motion estimation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：