检索结果-内蒙古大学图书馆

2024 16th International Conference on Graphics and Image Processing, ICGIP 2024

作者： Sun, Liliang He, Yuanlie Li, Wensheng Feng, Fujian Liang, Yihui School of Computer Guangdong University of Technology Guangzhou510000 China School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan528400 China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang550025 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

This paper focuses on the image composition of transparent objects, where existing image matting methods suffer from composition errors due to the lack of accurate foreground during the composition process. We propose a foreground prediction model named ALGM, which leverages the local feature extraction capabilities of Convolutional Neural Networks (CNNs) and incorporates an attention mechanism for global information modeling. The proposed alpha-assisted foreground prediction module extracts foreground information from the original image and conveys it. The extracted foreground color information is combined with the deep structural features of the encoder and used for foreground color prediction. ALGM reduces image composition errors in the quantitative data from the Composition-1k dataset and improves the visual quality of composed images on the AIM-500 and Transparent-460 datasets. © 2025 SPIE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Fire Detection Based on Flame Enhancement for Weak Fires 19th

Fire Detection Based on Flame Enhancement for Weak Fires

引用

19th Chinese Conference on Image and Graphics Technologies and Applications, IGTA 2024

作者： Chen, Kuan Wen, Wen Feng, Fujian Xu, Xiang Liang, Yihui School of Computer Guangdong University of Technology Guangzhou510000 China School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan528400 China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang550025 China

ISBN: (纸本)9789819799183

Detecting weak fire, such as overexposed and highly transparent flames, remains a significant challenge in vision-based fire detection. Convolutional Neural Network (CNN) based methods are widely used for automatic fire feature extraction, but they struggle to accurately recognize overexposed flames similar to the background and highly transparent flames that blend with the background, leading to false negative fire detection results. To address this issue, we have established a large-scale fire detection dataset with weak flames and introduce a fire detection method based on flame context enhancement. While employing a flame feature extraction module to identify and delineate the potential flame area, the proposed method primarily introduces a flame context module to capture the effective features of weak flames from this area, significantly enhancing the understanding of fires. Experimental results show that the proposed method can effectively reduce false negatives and improves the recall rate for detecting weak flames. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Premixed flames

来源：评论

学校读者我要写书评

暂无评论

Adaptive Pixel Pair Generation Strategy for Image Matting Methods Based on Pixel Pair Optimization 19th

Adaptive Pixel Pair Generation Strategy for Image Matting Me...

引用

19th Chinese Conference on Image and Graphics Technologies and Applications, IGTA 2024

作者： Zheng, Jiamin Wen, Wen Liang, Yihui Feng, Fujian Xu, Xiang School of Computer Guangdong University of Technology Guangzhou510000 China School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan528400 China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang550025 China

ISBN: (纸本)9789819799183

Natural image matting plays a crucial role in numerous real-world applications. Image matting methods based on pixel pair optimization is a type of matting algorithm, which has significant advantages in parallel computation, handling images with similar foreground and background, and operating under limited computing time. However, these methods cannot provide high-quality alpha mattes for high-resolution images within limited computational resources. In this paper, we design an adaptive pixel pair generation strategy to solve the above problem. This strategy adaptively generates pixel pair for the unknown pixel according to its estimated alpha value, which promotes diversity of pixel pair and improves the efficiency of pixel pair optimization. It categorizes unknown pixels into three types and additionally incorporates nearby foreground and background pixels to generate pixel pair. Experimental results show that using our strategy can achieve high-quality alpha mattes and competitive matting performance under limited computational resources. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Pixels

来源：评论

学校读者我要写书评

暂无评论

Geometric Numerical Integral Method in Compact Lie Group 6

Geometric Numerical Integral Method in Compact Lie Group

引用

6th IEEE International Conference on intelligent Computing and Signal Processing, ICSP 2021

作者： Liu, Chao Yang, Shengyi Guizhou Minzu University Key Laboratory of Pattern Recognition Intelligent System of Guizhou Province Gui Yang China

ISBN: (纸本)9780738143705

Three dimensions special orthogonal group SO (3) is widely used to describe the rotation kinematics of the rigid body without local coordinates, which can avoid rotation singularity and unwinding in traditional methods. Propagating the rotation kinematics in SO (3) with a specific geometric integration method is not only to obtained numerical results with improved qualitative behavior, but also provided more accurate long-time integration results. While many studies have focused on geometric integration algorithms to preserve the geometric structure, this work has the additional objective of studying result accuracy. Integral curves on SO (3) obtained using the third-order Crouch-Grossman Lie group method are compared with numerical results using the third-order RKMK algorithms, the exponential coordinates method, and the simple projection method. Results show that the use of the Crouch-Grossman Lie group method better preserves the geometric structure of SO (3) for the larger time steps considered. It is also found that the third-order Crouch-Grossman algorithm is more accurate than the RKMK except for the smallest time step used. © 2021 IEEE.

关键词： Geometry

来源：评论

学校读者我要写书评

暂无评论

Research on Teaching Reform of 'Motor and Drive'Based on Matlab Simulation 2

Research on Teaching Reform of 'Motor and Drive'Based on Mat...

引用

2nd International Conference on Education, Knowledge and Information Management, ICEKIM 2021

作者： Chengwei, Zhang Yihua, Liang Liu, Chao Yang, Shengyi Key Laboratory of Pattern Recognition and Intelligent System of Guizhou Province Guizhou Minzu University Gui Yang China

ISBN: (纸本)9781728168340

'Motor and Drive' is a professional basic course with strong theory and wide application in practical engineering. Its theory is abstract and formula is complex, which is considered by teachers and automation students as one of the professional courses difficult to teach and learn. In order to better teaching and learning, teaching reform is provided. The teaching content is optimized, the teaching methods, experimental methods and assessment methods are reformed based on matlab simulation. It is helpful to improve the teaching quality, stimulate students' learning enthusiasm and solve practical problems. The teaching reform foundation of 'motor and drive' is provided in this paper. © 2021 IEEE.

关键词： Teaching

来源：评论

学校读者我要写书评

暂无评论

intelligent Parking Big Data Cloud Platform Algorithm Research and Application 6

Intelligent Parking Big Data Cloud Platform Algorithm Resear...

引用

6th International Conference on Computer Information Science and Application Technology, CISAT 2023

作者： Yan, Xiaobo Zou, Xue Cai, Shan Wang, Lin Key Laboratory of Pattern Recognition and Intelligent System of Guizhou Province Guizhou Minzu University Guiyang China College of Date Science and Information Engineering Guizhou Minzu University Guiyang China

ISBN: (纸本)9781510668546

With the rapid development of the domestic economy and the increasing living standards of the people, the ownership of private cars has increased explosively. Currently, urban traffic congestion and parking difficulty have gradually become a hot topic of research from all walks of life. Parking difficulty is mainly reflected on the following aspects: first, the existing parking spaces in cities are not effectively utilized;The second reason is that drivers cannot grasp relevant information about parking spaces near the ground in real time. This article conducts in-depth research on the issue of parking difficulty. Based on the existing security monitoring systems in parking lots, real-time detection of parking spaces status is conducted based on computer vision, machine learning, and other methods. By building a comprehensive, synthesize, and multi-dimensional parking resources dynamic sensing system, a smart parking big data cloud platform is constructed to achieve the effect of intelligent indoor parking lots management. © 2023 SPIE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

A Garbage Classification Method Based on Improved YOLOv5

A Garbage Classification Method Based on Improved YOLOv5

引用

2022 International Conference on Networks, Communications and Information Technology, CNCIT 2022

作者： Yan, Xiaobo Yang, Yuan Feng, Le Wang, Lin Tan, Mian Guizhou Minzu Unversity Key Laboratory of Pattern Recognition and Intelligent System of Guizhou Province. Guiyang China College of Date Science and Information Engineering. Guizhou Minzu Unversity Guiyang China

ISBN: (数字)9781665452960

ISBN: (纸本)9781665452960

With the development of national economy, people's daily garbage is increasing day by day. Relying on manpower to sort garbage is a heavy workload and low efficiency. In this paper, an automatic garbage classification system model based on computer vision is proposed to solve the above problems. Firstly, the YOLOv5 object detection algorithm is improved, and abandoning small object detection to obtain faster detection speed. Secondly, shortcut the complex network layer in the YOLOv5 algorithm framework to speed up the recognition speed while ensuring the recognition accuracy. Finally, the traditional loss function is used to improve the detection speed of large objects in raspberry pie. We collect various types of garbage data set, trains the model on the improved algorithm and tests it by embedding raspberry pie. The experimental results show that the proposed algorithm framework has the characteristics of fast speed and high precision, which can complete the garbage classification at the source of garbage. © 2022 IEEE.

关键词： Network layers

来源：评论

学校读者我要写书评

暂无评论

Feature Decoupled of Deep Mutual Information Maximization 2

Feature Decoupled of Deep Mutual Information Maximization

引用

2nd International Conference on Automation, Robotics and Computer Engineering, ICARCE 2023

作者： He, Xing Peng, Changgen Wang, Lin Tan, Weijie Wang, Zifan State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China Guizhou Big Data Academy Guizhou University Guiyang China Guizhou Minzu University Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guiyang China Institute of Guizhou Aerospace Measuring and Testing Technology Guiyang China

ISBN: (纸本)9798350308341

In deep learning, supervised learning techniques usually require a large amount of expensive labeled data to train the network, and the feature representations extracted by the model usually mix multiple attributes, resulting in feature representations that are difficult to decouple and are non-interpretable, which restricts the application and development of deep learning techniques, and for this reason, it is particularly important to study decoupled feature representation methods for unsupervised learning. Although the Learning deep representations by mutual information estimation and maximization (DIM) method achieves excellent results in unsupervised learning, the feature representations learned by the DIM method still suffer from the problem of difficult decoupling. decoupling problem. To address this problem, we minimize the mutual information between the intermediate layer feature representations learned by the hidden layer of the encoder during the encoder training process, so that the features learned by each filter are as uncorrelated as possible, thus realizing feature decoupling, and our method is called FP-DIM. Finally, the effectiveness of the FP-DIM method is verified on the CIFAR-10, STL-10, and Fashion-MNIST datasets. The experiments show that our proposed FP-DIM method is more significant for the learned decouplable middle layer feature representation. Finally, we also propose a reflection of future research for the FP-DIM method, aiming to provide a research idea and direction for solving unsupervised interpretable machine learning and to lay a solid theoretical and application foundation for machine learning fields such as image classification and migration learning. © 2023 IEEE.

关键词： Unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

Facial expression recognition under occlusion conditions based on multi-feature cross-attention

引用

Journal of intelligent and Fuzzy systems 2024年第4期46卷 9841-9856页

作者： Guo, Sheng Tan, Mian Cai, Shan Zhang, Zaijun Liang, Yihui Feng, Hongxi Zou, Xue Wang, Lin School of Data Science and Information Engineering Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China School of Mathematics and Statistics Qiannan Normal University for Nationalities Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province Duyun China Zhongshan Institute University of Electronic Science and Technology of China Zhongshan China Guizhou Aerospace Tianma Electromechanical Technology Co LTD Zunyi China

Although facial expression recognition (FER) has a wide range of applications, it may be difficult to achieve under local occlusion conditions which may result in the loss of valuable expression features. This issue has motivated the present study, as a part of which an effective multi-feature cross-attention network (MFCA-Net) is proposed. The MFCA-Net consists of a two-branch network comprising a multi-feature convolution module and a local cross-attention module. Thus, it enables decomposition of facial features into multiple sub-features by the multi-feature convolution module to reduce the impact of local occlusion on facial expression feature extraction. In the next step, the local cross-attention module distinguishes between occluded and unoccluded sub-features and focuses on the latter to facilitate FER. When the MFCA-Net performance is evaluated by applying it to three public large-scale datasets (RAF-DB, FERPlus, and AffectNet), the experimental results confirm its good robustness. Further validation is performed on a real FER dataset with local occlusion of the face. © 2024 – IOS Press.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Size and Defect Detection of Valve Based on Computer Vision

Size and Defect Detection of Valve Based on Computer Vision

引用

2022 International Conference on Computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2022

作者： Yan, Xiaobo Feng, Le Tan, Mian Yang, Yuan Zhang, Yu Wang, Lin Guizhou Key Laboratory of Pattern Recognition and Intelligent System China Guizhou Minzu University Guiyang550025 China Guiyang Fusheng Intelligent Technology Co. Ltd China

ISBN: (纸本)9781510663350

Engine valve is the core component of the engine, and its quality determines the performance of the engine. In industrial production quality inspection, it is necessary to detect the size of the valve and whether there are defects on the surface. Usually, the quality of the valve is determined by comparing the image of the valve surface with the standard image. However, the existing surface defect detection technology cannot detect the curved surface device. In order to solve this problem, this paper designs a valve size and defect detection method based on computer vision. The experimental results show that the method can quickly and accurately detect the rod diameter, groove radius and surface defects of the valve. The method is practical, robust and real-time. © 2023 SPIE.

关键词： Computer vision

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：