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15th International Conference on Measurement Technology and Mechanical Automation, ICMTMA 2023

作者： Yi, X. Wang, Z.M. Heng, X. School of Computer Science and Technology Xi'An University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi 'An Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China

In order to reconfigure its structure from the static state in the vision odd ball task, so as to realize the intention recognition based on the characteristics of the brain functional network. The thesis proposes the intention recognition method based on resting state and P300 task state dynamic brain functional network features. First, the brain connectivity in each time window is constructed into a brain functional network using phase lock value (PLV). Then, extract the global features (global efficiency, transitivity) of the brain functional network, and use Louvain algorithm to obtain the brain functional network community. The experimental results show that in the (100-200) ms of P300 task status, the core nodes are mainly concentrated in the forehead region and the central region, while in the (300-500) ms of P300, the core nodes are concentrated in the temporal lobe. The recognition accuracy based on this method reaches 93%. © Published under licence by IOP Publishing Ltd.

关键词： Brain Function Network Intention Recognition P300 PLV Sliding Windows

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Selective Bokeh Effect Transformation

Selective Bokeh Effect Transformation

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Peng, Juewen Pan, Zhiyu Liu, Chengxin Luo, Xianrui Sun, Huiqiang Shen, Liao Xian, Ke Cao, Zhiguo Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control China Nanyang Technological University S-Lab Singapore

ISBN: (纸本)9798350302493

Bokeh effect transformation is a novel task in computer vision and computational photography. It aims to convert bokeh effects from one camera lens to another. To this end, we introduce a new concept of blur ratio, which represents the ratio of the blur amount of a target image to that of a source image, and propose a novel framework SBTNet based on this concept. For cat-eye simulation and lens type transformation, a two-channel coordinate map and a two-channel one-hot map are added as extra inputs. The core of the framework is a sequence of parallel FeaNets, along with a feature selection and integration strategy, which aims to transform the blur amount with arbitrary blur ratio. The effectiveness of the proposed framework is demonstrated through extensive experiments, and our solution has achieved the top LPIPS metric in NTIRE 2023 Bokeh Effect Transformation Challenge. © 2023 IEEE.

关键词： Color photography

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LCS-based Visual Recognition for AGV Guide Paths under Complex Illumination Conditions 15

LCS-based Visual Recognition for AGV Guide Paths under Compl...

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15th International Conference on Measurement Technology and Mechanical Automation, ICMTMA 2023

作者： Wang, Z.M. Li, M. Gao, C. Jin, X.M. School of Computer Science and Technology Xi'An University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'An Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China

Complex lighting is one of the most challenging problems in automatic guided vehicle (AGV) vision recognition system. In order to overcome the influence of uneven illumination on the accuracy and robustness of path recognition, this paper proposes the LCS based visual recognition for AGV guide paths under complex illumination conditions method, which converts the collected image into an invariant image through logarithmic chromaticity space (LCS) to eliminate the influence of illumination, and the minimum average entropy angle is used as the projection angle to generate the invariant image to improve the speed of image conversion to the invariant image. Experimental results show that the proposed method can effectively improve the robustness of AGV vision recognition system under complex lighting conditions. © Published under licence by IOP Publishing Ltd.

关键词： Lighting

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Video Compression Artifacts Removal With Spatial-Temporal Attention-Guided Enhancement

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IEEE Transactions on Multimedia 2024年 26卷 5657-5669页

作者： Jiang, Nanfeng Chen, Weiling Lin, Jielian Zhao, Tiesong Lin, Chia-Wen Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou350108 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology Xiamen361024 China Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou350116 China Department of Electrical Engineering National Tsing Hua University Hsinchu30013 Taiwan Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan

Recently, many compression algorithms are applied to decrease the cost of video storage and transmission. This will introduce undesirable artifacts, which severely degrade visual quality. Therefore, Video Compression Artifacts Removal (VCAR) aims at reconstructing a high-quality video from its corrupted version of compression. Generally, this task is considered as a vision-related instead of media-related problem. In vision-related research, the visual quality has been significantly improved while the computational complexity and bitrate issues are less considered. In this work, we review the performance constraints of video coding and transfer to evaluate the VCAR outputs. Based on the analyses, we propose a Spatial-Temporal Attention-Guided Enhancement Network (STAGE-Net). First, we employ dynamic filter processing, instead of conventional optical flow method, to reduce the computational cost of VCAR. Second, we introduce self-attention mechanism to design Sequential Residual Attention Blocks (SRABs) to improve visual quality of enhanced video frames with bitrate constraints. Both quantitative and qualitative experimental results have demonstrated the superiority of our proposed method, which achieves high visual qualities and low computational costs. © 2023 IEEE.

关键词： Computational complexity

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Shadow Removal from a Single image Using an Enhanced Generative Adversarial Network 19

Shadow Removal from a Single Image Using an Enhanced Generat...

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19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2023

作者： Zhang, Jing Kong, Weiwei Xi'an University of Posts and Telecommunications College of Computer Science Xi'an China Xi'an University of Posts and Telecommunications The Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an China

ISBN: (纸本)9798350304398

image shadow removal is challenging due to the diversity of shadows and the dependence on the background of the area covered by shadows. In this paper, we design an enhanced generative adversarial network (EGAN) to handle the shadow removal task. EGAN is a complicated generative adversarial network that may exploit previous info, like shadow masks and lightness estimation, for improving recovery capability. In addition, EGAN can effectively utilize the details of shadow regions and eliminate artifacts. EGAN owns three main advantages. First, EGAN employs stricter rules for discriminators, which can upgrade the instability of traditional GAN. Second, EGAN involves a completely unique enhanced attention module, which may efficiently pick up different types of previous info to accelerate the learning efficiency of image reconstruction. Finally, EGAN is essentially an original cascaded network, that is, DRDU, which is beneficial for efficiently detecting hierarchical features of image recovery. Simulation experiments are conducted to verify the effectiveness of the proposed method on two benchmark datasets including SRD and ISTD, the strengths of our proposed method are indicated over the current representative ones according to subjective performance and objective estimation indexes. © 2023 IEEE.

关键词： image reconstruction

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Temporal Global Re-detection Based on Interaction-Fusion Attention in Long-Term Visual Tracking 12th

Temporal Global Re-detection Based on Interaction-Fusion At...

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12th International Conference on image and Graphics, ICIG 2023

作者： Ma, Jingyuan Hou, Zhiqiang Han, Ruoxue Ma, Sugang School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Key Laboratory of Network Data Analysis and Intelligent Processing of Shaanxi Province Xi’an710121 China

ISBN: (纸本)9783031463075

In long-term visual tracking, target occlusion and out-of-view are common problems that lead to target drift, adding re-detection to short-term tracking algorithms is a general solution. To better handle the problem of target disappearing and reappearing in long-term visual tracking, this paper proposes a temporal global re-detection method based on interaction-fusion attention. Firstly, ResNet50 is used as the feature extraction network to obtain the depth features of the template and the search region. Then, a new interaction-fusion attention is added to extract the connection of different dimensionality of features. Finally, Temporal ROI Align is introduced to select candidate boxes, increasing the use of historical information by re-detection method, and improving the accuracy of target localization. STMTrack algorithm is selected as the short-term tracking algorithm, which works with the proposed re-detection method to construct a long-term tracking algorithm, and experiments are conducted on the UAV123, LaSOT, UAV20L, and VOT2018-LT datasets, and the effectiveness of this re-detection method can be seen from the experimental results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Chemical detection

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Drug-Protein Interaction Prediction by Fusion of Attention and Graph Neural Network 42

Drug-Protein Interaction Prediction by Fusion of Attention a...

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42nd Chinese Control Conference, CCC 2023

作者： Chen, Tianrui Tuo, Shouheng Feng, Zengyu School of Computer Science and Technology Xi'An University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'An Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China

ISBN: (纸本)9789887581543

The identification of drug-protein interactions (DTIs) is a critical step in drug development and repositioning. However, detecting these interactions using scientific methods presents a formidable challenge. Existing models often struggle to extract relevant information from drug-protein sequences, and they fail to consider pre-existing interactions between pairs. Here, we introduce a deep learning model, DPAG, based on attention mechanisms and graph neural networks, which overcomes these limitations. DPAG leverages information from both the drug and protein sequences, as well as the association between drug-protein pairs, to predict DTIs. We conducted multiple experiments to evaluate the performance of the model in benchmark datasets, and the results showed that DPAG achieved excellent performance in terms of AUC, AUPR, precision, and recall. Our findings suggest that DPAG holds significant potential as a promising tool for predicting DTIs in drug development and repositioning. Code is available at https://***/veresse/DPAG. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Graph neural networks

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Geometric Algebra Based Kalman Filter Trajectory Prediction

Geometric Algebra Based Kalman Filter Trajectory Prediction

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Aerospace Engineering and Systems (ISAES), International Symposium on

作者： Taiyu Li Weidong Jiao Tianjin Key Laboratory of Intelligent Signal and Image Processing Civil Aviation University of China Tianjin China

ISBN: (数字)9798350350418

ISBN: (纸本)9798350350425

To improve the safety and efficiency of aviation operation, a Kalman filtered trajectory prediction algorithm based on geometric algebra is proposed in this space. Firstly, the Kalman filter equations for trajectory prediction in geometric algebraic space are given by taking position, velocity and acceleration as state variables and position, velocity and acceleration in a typical trajectory as observations. Secondly, the matrix operation in the geometric algebraic space is simplified to the matrix operation by establishing a composite geometric algebraic space. Then, the versor operator in the conformal geometric algebraic space is introduced into the Kalman filter, and the steps of geometric algebra-based Kalman filter trajectory prediction are given to improve the prediction rate and accuracy. The experimental results show that the proposed trajectory prediction algorithm has the highest accuracy and the operation efficiency is improved by 27.66 % compared with the Kalman filter algorithm in Euclidean space and the DBSCAN clustering algorithm.

关键词： Accuracy Algebra Clustering algorithms Filtering algorithms Predictive models Prediction algorithms Mathematical models Trajectory Safety Kalman filters

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CLIP-based Text-to-image Pedestrian Retrieval Algorithm

CLIP-based Text-to-image Pedestrian Retrieval Algorithm

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image processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Xubin Shi Baohua Qiang Ruidong Chen Yuan Xie Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Text-to-image pedestrian retrieval involves querying a database of images based on a given textual description to find matching images. The main challenge lies in learning how to map visual and textual patterns to a common latent space. This paper improves upon the implicit learning inference framework based on the CLIP model, aiming to learn the relationship between images and text and enhance overall text-image matching. Through text matching learning, fine-grained detail representation learning is promoted. First, a masked language modeling paradigm is designed within the implicit relationship inference module. Then, cross-modal interaction is achieved through the modality alignment module using decomposable attention. Feed aligned image and text features into a multimodal synergy encoder to achieve cross modal interaction, and train the model using distribution consistency loss and ID loss. The improved method achieves better results on the RSTPReid public dataset, with the Rank-1 accuracy improving by approximately 3% compared to existing methods.

关键词： Representation learning Visualization Computer vision Pedestrians Databases image processing Semantics Text to image Transformers Feeds

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An English Pronunciation Evaluation Model Based on Acoustic Phonetics Features

An English Pronunciation Evaluation Model Based on Acoustic ...

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Computer Science and Blockchain (CCSB), International Conference on

作者： Hongkang Wang Qingkai Guo Guimin Huang Ya Zhou Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798350356502

ISBN: (纸本)9798350356519

Oral English practice constitutes a crucial component of English language acquisition for non-native speakers; however, traditional classroom teaching methods are increasingly insufficient to address the diverse needs of learners. This paper enhances the evaluation of pronunciation quality within Computer-Assisted Language Learning (CALL) system. To assess the fluency of English learners' pronunciation, this paper introduces the innovative feature known as wordDurationRatio. Additionally, an intonation evaluation method utilizing dynamic time warping (DTW) algorithms is explored. Finally, this study incorporates support vector regression (SVR) algorithms to effectively fuse features, enhancing the assessment result of pronunciation quality.

关键词： Support vector machines Computer science Acoustic phonetics Fuses Heuristic algorithms Evaluation models Education Quality assessment Blockchains Standards

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