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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

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

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

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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

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Trimap generation with background for natural image matting 3

Trimap generation with background for natural image matting

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3rd International Conference on Optics and Machine Vision, ICOMV 2024

作者： Fu, Qian Liang, Yihui Kun, Zou Feng, Fujian Xu, Xiang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan China Guizou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China

ISBN: (纸本)9781510680319

Image matting is a widely-used image processing technique that aims at accurately separating foreground from an image. However, this is a challenging and ill-posed problem that demands additional input, such as trimaps and background images, for providing prior knowledge. However, the manual annotation of trimaps require lots of labor, limiting the application of trimap-based methods. Some trimap-free methods explore alternatives with low labor requirements by utilizing captured background images, including background-based methods. However, the quality of alpha mattes predicted by trimap-free methods still fall short of trimap-based methods. To reduce the performance gap between background-based and trimap-based methodes, we present Trimap Generation from Background Image (TG-BG) method which can generate trimaps from the input image and a captured background image. It provides an economical solution to facilitate the application of trimap-based methods, allowing for low-cost and high-quality alpha matte predictions. TP-BG leverages a ViT backbone for feature extraction and employs the Image and Background Detail Fusion Stream (IBDFS) to capture multi-scale detail information. The introduction of foreground impact loss encourages the network to pay more attention to the foreground in the image. We validate the trimap prediction performance of TP-BG by comparing the alpha matte quality obtained by background-based methods and that obtained by trimap-based methods integrated with TP-BG. The experimental results demonstrate that TP-BG can generate high-quality trimap from a background image, and trimap-based methods integrated with TP-BG outperform the state-of-the-art background-based methods in terms of four alpha matte quality metrics. © 2024 SPIE.

关键词： Costs

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Cross-Domain Person Re-identification Combining Feature Concatenation and Attention

Cross-Domain Person Re-identification Combining Feature Conc...

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作者： Feng Pan Lin Wang Yansha Zhang Jie Wang College of Data Science and Information Engineering Guizhou Minzu University Key Laboratory of Pattern Recognition and Intelligent System of Guizhou Province

To improve the insufficient generalization and poor cross-domain capability of the existing direct cross-dataset person re-identification methods,a cross-domain person re-identification method combining feature concatenation and attention(FCANet) is *** deep features of the network are concatenated to complement the feature information and obtain discriminatively feature,and the position attention module is introduced to enhance the data feature representation capability of the cross-domain task,using the joint training network of label smooth cross-entropy loss and triplet loss,model training in the source domain,and directly deploy to the target domain for *** verify the performance of the proposed method,it was experimented on three public datasets of Market1501,DukeMTMC-reID and MSMT17,which mAP and Rank1can reach 51.4%and 62.7% on *** results show that the proposed method has good performance in improving the generalization of cross-domain tasks,and the recognition accuracy outperforms the domain generalization algorithms of comparison.

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Adaptive geodesic feedback controller design for the quadrotor

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International Journal of Vehicle Autonomous systems 2020年第3-4期15卷 319-342页

作者： Liu, Chao Yang, Shengyi Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Huaxi District Guizhou Province Guiyang China

In this paper, a non-linear adaptive control method based on SO(3) for the quadrotor attitude tracking is proposed. Distinct from other control methods on Euclidean space, the controller proposed is developed on SO(3), which can avoid singularities and ambiguities. Besides, the geodesic on SO(3) is used to provide the shortest curve between the current attitude configuration and the desire attitude configuration. Then, a feedback controller based on geodesic is derived. To solve the problem that the inertial tensor of the quadrotor is unknown, an adaptive term is designed with the Lyapunov theory to estimate the inertial tensor. Thus, the almost global asymptotic stability of the quadrotor attitude tracking is achieved without the prior knowledge of the quadrotor inertia tensor. The numerical simulations are utilised to show better control performance of the adaptive geodesic feedback control method. Copyright © 2020 Inderscience Enterprises Ltd.

关键词： Controllers

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Cd-Vae: An Unsupervised Disentangled Representation Learning Framework for Visual Data

SSRN

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SSRN 2023年

作者： Huang, Chengquan Cai, Jianghai Luo, Senyan Wang, Shunxia Yang, Guiyan Lei, Huan Zhou, Lihua Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang550025 China School of Data Science and Information Engineering Guizhou Minzu University Guiyang550025 China

Disentangled representation learning is a crucial research problem in the field of artificial intelligence, and learning meaningful representation of visual data is useful for improving the generalizability and interpretability of machine learning models. To discover and learn the interpretable factors behind the visual data, previous approaches typically use extra regularization terms in learning disentangled representations of the generated images, which usually lead to poor results between disentanglement and generative quality. For the generative model variational auto-encoder (VAE), the traversal of the generated images along different factor directions shows meaningful and interpretable variations in the latent space. Building upon this observation, we argue that it is important to learn disentangled representations of the generated images by exploiting the scalability and training stability of the generative model VAE, and focusing on meaningful traversal directions in the latent space. Therefore, we propose contrast disentanglement based on VAE (CD-VAE), a deep unsupervised disentangled representation learning framework for visual data, that can discover the latent factors in image data. CD-VAE jointly learns disentangled representations by traversing image variations in the latent space, and constructing contrast disentanglement in the variation space. Experimental results demonstrate that CD-VAE can discover interpretable directions and achieve better disentangled representation learning for visual data. Source codes and pre-training data will be publicly available at GitHub website. © 2023, The Authors. All rights reserved.

关键词： Deep learning

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RBP-Former: Joint Prediction of RNA-protein Binding Sites on Full-length RNA Transcripts for Multiple RBPs

RBP-Former: Joint Prediction of RNA-protein Binding Sites on...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Yichong Liu, Xiaojian Cheng, Fan Pan, Xiaoyong Yang, Yang Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai200240 China

ISBN: (纸本)9798350386226

RNA-binding proteins (RBPs) are essential for gene expression, and the complex RNA-protein interaction mechanisms require analysis of global RNA information. Therefore, accurate prediction of RBP binding sites on full-length RNA transcripts is crucial for understanding these mechanisms and their roles in diseases. While machine learning methods can predict RBP binding to RNA fragments, extending this to full-length transcripts presents challenges due to sequence length and data imbalance. In this paper, we introduce RBP-Former, a binding site joint prediction model designed specifically for full-length RNA transcripts that can be used for multiple RBPs. This model processes information at both coarse and fine-grained levels to fully exploit sequence data and its interactions with multiple RBPs. We develop multi-level imbalance learning strategies, achieving favorable results on imbalanced data. Our method outperforms existing methods in predicting binding sites on full-length RNA transcripts for multiple RBPs, demonstrating its effectiveness in handling imbalanced label and sample distributions. © 2024 IEEE.

关键词： Binding sites

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Fire Detection Based on Flame Enhancement for Weak Fires 19th

Fire Detection Based on Flame Enhancement for Weak Fires

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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

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

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

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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

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Ballot character recognition based on image processing 2

Ballot character recognition based on image processing

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2nd International Conference on Computer Vision and Data Mining, ICVDM 2021

作者： Zhao, Yu-Qing Yan, Xiao-Bo Wang, Lin College of Date Science and Information Engineering Guizhou Minzu University Guizhou Guiyang550025 China Key Laboratory of Pattern Recognition and Intelligent Systems of Guizhou Guizhou Guiyang550025 China

According to the relevant provisions of the "Election Law of the People's Republic of China", the engineering software required for the implementation of large-scale conference elections can set voting regulations for "differential elections", "equal elections", and "alternative elections". This paper proposes an automatic ballot recognition system based on image processing, which is suitable for all levels of people’s congresses, party and government meetings or other meetings. It has complete functions and stable recognition. It can automatically count and summarize the voting results and display the voting results on the big screen. Show on the display system. Firstly, the paper ballot is scanned by a scanner to obtain the image of the ballot. Secondly, the system will preprocess the image of the ballot and segment the image into ***, according to the handwritten character recognition algorithm based on image processing and Shen filter proposed in this paper, the handwritten characters "√" and "×" in the subregion are *** final experimental results show that the recognition accuracy of the system is as high as 99.82%, which has an absolute advantage over the traditional manual voting, and can meet the performance requirements of high efficiency, accuracy, objectivity and security of large-scale conference election. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Image segmentation

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