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TechRxiv 2022年

作者： Yan, Tianhao Meng, Hao Parada-Cabaleiro, Emilia Tao, Jianhua Schuller, Björn W. The College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin150001 China Embedded Intelligence for Health Care & Wellbeing University of Augsburg Augsburg86159 Germany The Institute of Computational Perception Johannes Kepler University Linz Linz4040 Austria National Laboratory of Pattern Recognition University of Chinese Academy of Sciences Beijing100190 China The Group on Language Audio & Music Imperial College London LondonSW7 2AZ United Kingdom

The great variety of human emotional expression as well as the differences in the ways they perceive and annotate them make Speech Emotion recognition (SER) an ambiguous and challenging task. With the development of deep learning, long-term progress has been made in supervised SER systems. However, the existing convolutional neural networks present certain limitations, such as their inability to well capture global features, which contain important emotional information. In addition, due to the subjective nature and continuity of emotion, the instance segments in which emotional speech is typically segmented do not fully reflect the true labels and cannot describe dynamic temporal changes. Thus, accurate emotional representation cannot be learnt in the process of feature extraction. In order to overtake these limitations, we propose an end-to-end network only for speech that maps sequences of different lengths to a fixed number of chunks and strictly preserves the order of chunks by adaptively adjusting their overlap. Subsequently, it extracts log-mel spectrogram features from chunk-level segments and feeds them into the Residual Multi-Scale Convolutional Neutral Networks with Transformer(RMSCTx) model framework. Finally, by keeping the order of the chunk-level segments, a temporal domain mean layer is used to further extract utterance-level feature representations. With this method, we perform multidimensional SER, i. e., the prediction of arousal, valence, and dominance. The experimental results on three popular corpora demonstrate not only the superiority of our approach, but also the robustness of the model for SER, showing an improvement of the recognition accuracy in the newest version of the public dataset MSP-Podcast (1.9). © 2022, CC BY.

关键词： Convolution

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Edge-Aware Graph Attention Network for Ratio of Edge-User Estimation in Mobile Networks

Edge-Aware Graph Attention Network for Ratio of Edge-User Es...

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International Conference on pattern recognition

作者： Jiehui Deng Sheng Wan Xiang Wang Enmei Tu Xiaolin Huang Jie Yang Chen Gong PCA Lab the Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Hong Kong Polytechnic University Hong Kong SAR China

Estimating the Ratio of Edge-Users (REU) is an important issue in mobile networks, as it helps the subsequent adjustment of loads in different cells. However, existing approaches usually determine the REU manually, which are experience-dependent and labor-intensive, and thus the estimated REU might be imprecise. Considering the inherited graph structure of mobile networks, in this paper, we utilize a graph-based deep learning method for automatic REU estimation, where the practical cells are deemed as nodes and the load switchings among them constitute edges. Concretely, Graph Attention Network (GAT) is employed as the backbone of our method due to its impressive generalizability in dealing with networked data. Nevertheless, conventional GAT cannot make full use of the information in mobile networks, since it only incorporates node features to infer the pairwise importance and conduct graph convolutions, while the edge features that are actually critical in our problem are disregarded. To accommodate this issue, we propose an Edge-Aware Graph Attention Network (EAGAT), which is able to fuse the node features and edge features for REU estimation. Extensive experimental results on two real-world mobile network datasets demonstrate the superiority of our EAGAT approach to several state-of-the-art methods.

关键词： Deep learning Fuses Estimation Switches pattern recognition

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Autonomous Obstacle Avoidance Scheme Using Monocular Vision Applied to Mobile Robots

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Journal of Physics: Conference Series 2021年第1期1894卷

作者： Yun Yun Song Sheng Yi Yang Chao Liu Qing Gui Wu School of Data Science and Information Engineering Key Laboratory of Pattern Recognition and Intelligent Systems of Guizhou Province Guizhou Minzu University Guiyang China Key Laboratory of Pattern Recognition and Intelligent Systems of Guizhou Province Guizhou Minzu University Guiyang China School of Mechatronics Engineering Guizhou Minzu University Guiyang China

Mobile robots cannot move in an unknown environment with static or slow-moving obstacles effectively. We present an enhanced obstacle avoidance strategy using monocular vision to solve this problem. First, we combine Canny and Otsu to extract the barrier feature and find the critical pixel position of obstacles by the monocular vision. Then the image depth estimation algorithm is used to estimate the gaps. With these parameters of barriers, an improved bug algorithm is proposed to avoid the obstacles autonomously. The experiments show that the proposed obstacles avoidance strategy can effectively make a small mobile robot avoid different kinds of obstacles.

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A Generalized Denoising Method with an Optimized Loss Function for Automated Bird Sound recognition

A Generalized Denoising Method with an Optimized Loss Functi...

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International Conference on Computers, Communications, and systems (ICCCS)

作者： Huangqiang Fang Yulin He Wanyang Xu Yanyan Xu Dengfeng Ke Kaile Su School of Information Science and Technology Beijing Forestry University Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China Institute for Integrated and Intelligent Systems Griffith University Nathan Australia

ISBN: (数字)9781728161365

ISBN: (纸本)9781728161372

In natural environments, bird sounds are often accompanied by background noise, so denoising becomes crucial to automated bird sound recognition. Recently, thanks to neural network embeddings, the deep clustering method has achieved better performances than traditional denoising methods, like filter-based methods, due to its ability to solve the problem when noise is in the same frequency range as bird sounds. In this paper, we propose a generalized denoising method based on deep clustering, which can process more complex recordings with less distortion. Also, we optimize the original affinity loss function to get a novel loss function to ensure the embedding vectors with the minimum distance belong to the same source, named Joint Center Loss (JCL), which can both increase the inter-class variance and decrease the intra-class variance of embeddings. Experiments are conducted on the gated convolutional neural network architecture and the bidirectional long short term memory architecture respectively with different loss functions. Given the signal-noise ratio being -3dB, the recognition accuracy increases relatively by 9.5% with the proposed denoising method in the best case, and the Relative Root Mean Square Error (RRMSE) increases relatively by 14.2% by using JCL, compared with the original affinity loss function AL.

关键词： Birds Noise reduction Logic gates Training Noise measurement Time-frequency analysis Matrix converters

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NTIRE 2023 Image Shadow Removal Challenge Report

NTIRE 2023 Image Shadow Removal Challenge Report

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2023 IEEE/CVF Conference on Computer Vision and pattern recognition Workshops, CVPRW 2023

作者： Vasluianu, Florin-Alexandru Seizinger, Tim Timofte, Radu Cui, Shuhao Huang, Junshi Tian, Shuman Fan, Mingyuan Zhang, Jiaqi Zhu, Li Wei, Xiaoming Wei, Xiaolin Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Dong, Xiaoyi Zhang, Xi Sheryl Li, Chenghua Leng, Cong Yeo, Woon-Ha Oh, Wang-Taek Lee, Yeo-Reum Ryu, Han-Cheol Luo, Jinting Jiang, Chengzhi Han, Mingyan Wu, Qi Lin, Wenjie Yu, Lei Li, Xinpeng Jiang, Ting Fan, Haoqiang Liu, Shuaicheng Xu, Shuning Song, Binbin Chen, Xiangyu Zhang, Shile Zhou, Jiantao Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Wang, Bo Ren, Jiahuan Luo, Yan Kondo, Yuki Miyata, Riku Yasue, Fuma Naruki, Taito Ukita, Norimichi Chang, Hua-En Yang, Hao-Hsiang Chen, Yi-Chung Chiang, Yuan-Chun Huang, Zhi-Kai Chen, Wei-Ting Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Xianwei, Li Fu, Huiyuan Liu, Chunlin Ma, Huadong Fu, Binglan He, Huiming Wang, Mengjia She, Wenxuan Liu, Yu Nathan, Sabari Kansal, Priya Zhang, Zhongjian Yang, Huabin Wang, Yan Zhang, Yanru Phutke, Shruti S. Kulkarni, Ashutosh Khan, Md Raqib Murala, Subrahmanyam Vipparthi, Santosh Kumar Ye, Heng Liu, Zixi Yang, Xingyi Liu, Songhua Wu, Yinwei Jing, Yongcheng Yu, Qianhao Zheng, Naishan Huang, Jie Long, Yuhang Yao, Mingde Zhao, Feng Zhao, Bowen Ye, Nan Shen, Ning Cao, Yanpeng Xiong, Tong Xia, Weiran Li, Dingwen Xia, Shuchen Computer Vision Lab Ifi Caidas University of Würzburg Germany Computer Vision Lab Eth Zürich Switzerland Meituan Group China Department of Information Technology Uppsala University Sweden Institute of Automation Chinese Academy of Sciences Beijing China Nanjing China Maicro Nanjing China Department of Artificial Intelligence Convergence Sahmyook University Seoul Korea Republic of Megvii Technology China University of Electronic Science and Technology of China China University of Macau China China Toyota Technological Institute Japan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States Beijing University of Post and Teleconmunication Beijing China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education China Couger Inc. Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar Punjab Rupnagar India Research Institute Singapore National University of Singapore Singapore Research Institute Singapore University of Sydney Australia Brain-Inspired Vision Laboratory Information Science and Technology Institution University of Science and Technology of China China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou310027 China South China University of Technology China

ISBN: (纸本)9798350302493

This work reviews the results of the NTIRE 2023 Challenge on Image Shadow Removal. The described set of solutions were proposed for a novel dataset, which captures a wide range of object-light interactions. It consists of 1200 roughly pixel aligned pairs of real shadow free and shadow affected images, captured in a controlled environment. The data was captured in a white-box setup, using professional equipment for lights and data acquisition sensors. The challenge had a number of 144 participants registered, out of which 19 teams were compared in the final ranking. The proposed solutions extend the work on shadow removal, improving over the performance level describing state-of-the-art methods. © 2023 IEEE.

关键词： Data acquisition

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MiSiM:Microstructured Fibers' Simulator

MiSiM:Microstructured Fibers' Simulator

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Latin American Electron Devices Conference (LAEDC)

作者： Paola Montero-Sánchez Ricardo Román-Brenes F. Siles Jaime Cascante-Vindas LAFTLA: Laboratorio de Fotónica y Tecnología Láser Aplicada Universidad de Costa Rica San José Costa Rica PRIS-Lab: Pattern Recognition and Intelligent Systems Laboratory Universidad de Costa Rica San José Costa Rica

ISBN: (数字)9781728110448

ISBN: (纸本)9781728110455

In this work we present MISIM: a free and open software tool for the simulation of microstructured fibers, that can be used for the calculation of their dispersion profiles, for its design and posterior manufacturing. The software is based on the numerical model of Saitoh and Koshiba, and it is able to simulate chromatic dispersion, effective index and group index for a given spectral range. MISIM generates output text files as well as line charts of the simulated parameters. As far as we known, it is the only open and free software with a GUI that can visualize its output directly. For the validation of the tool, results from MISIM were compared with those reported in the literature using different methods to calculate their profiles, obtaining, for example effective indexes within one standard deviation of those reported. Even smaller errors (2.61%) were obtained for the chromatic dispersion using our proposed tool.

关键词： Software Indexes Chromatic dispersion Mathematical model Tools Graphical user interfaces

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Ballot character recognition based on image processing

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Journal of Physics: Conference Series 2021年第1期2024卷

作者： Yu-qing Zhao Xiao-bo Yan Lin Wang College of Date Science and Information Engineering Guizhou Minzu University Guiyang Guizhou 550025 China Key Laboratory of Pattern Recognition and Intelligent Systems of Guizhou Guiyang Guizhou 550025 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.

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Actor and Action Modular Network for Text-based Video Segmentation

arXiv

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arXiv 2020年

作者： Yang, Jianhua Huang, Yan Niu, Kai Huang, Linjiang Ma, Zhanyu Wang, Liang The Pattern Recognition and Intelligent Systems Lab. School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China The School of Computer Science Northwestern Polytechnical University Xi’an China The Multimedia Laboratory Chinese University of Hong Kong Hong Kong Beijing China Beijing China

Text-based video segmentation aims to segment an actor in video sequences by specifying the actor and its performing action with a textual query. Previous methods fail to explicitly align the video content with the textual query in a fine-grained manner according to the actor and its action, due to the problem of semantic asymmetry. The semantic asymmetry implies that two modalities contain different amounts of semantic information during the multi-modal fusion process. To alleviate this problem, we propose a novel actor and action modular network that individually localizes the actor and its action in two separate modules. Specifically, we first learn the actor-/action-related content from the video and textual query, and then match them in a symmetrical manner to localize the target tube. The target tube contains the desired actor and action which is then fed into a fully convolutional network to predict segmentation masks of the actor. Our method also establishes the association of objects cross multiple frames with the proposed temporal proposal aggregation mechanism. This enables our method to segment the video effectively and keep the temporal consistency of predictions. The whole model is allowed for joint learning of the actor-action matching and segmentation, as well as achieves the state-of-the-art performance for both single-frame segmentation and full video segmentation on A2D Sentences and J-HMDB Sentences datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantics

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Steganography algorithm for adaptive multi-rate wideband speech based on algebraic codebook search

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The Journal of China Universities of Posts and Telecommunications 2018年第3期25卷 71-79页

作者： Ruan Ye Xu Yanyan Ke Dengfeng Su Kaile School of Information Science and Technology Beijing Forestry University National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of Sciences Institute for Integrated and Intelligent Systems Griffith University College of Information Science and Technology Jinan University

With the popularity of adaptive multi-rate wideband （AMR-WB） audio in mobile communication, many AMR- WB based techniques, such as a similar compression architecture to transmit secret information during the process of compression, were proposed to transmit covert messages. However, if a sender does not have the original waveform audio format （WAV） audio, the architecture cannot be used. In this paper, a new covert message method, which takes effect after WAV audio is compressed into AMR-WB speech, is proposed. This method takes advantage of algebraic codebook search. Aiming at improving speed and reducing search space, it does not perform algebraic codebook search using the optimal search algorithm, and it does not reach the positions of non-zero pulses via depth-first tree search that characterizes the energy of audio. According to the features of search methods and the codebook index construction, every track in each subframe is analyzed to find the proper positions for embedding secret information. Experimental results show that the proposed method has satisfactory capacity and simplicity regardless of compression process.

关键词： steganography algorithm WAV audio compressed audio adaptive multi-rate wideband speech

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A Cellular Ant Colony Algorithm for Path Planning Using Bayesian Posterior Probability

A Cellular Ant Colony Algorithm for Path Planning Using Baye...

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2019 2nd International Conference on Informatics, Control and Automation (ICA 2019)

作者： Xiu-fen WANG Sheng-yi YANG School of data science and Information Engineering Guizhou Minzu University Key Laboratory of Pattern Recognition and Intelligent Systems of Guizhou Province Guizhou Minzu University

In order to solve the problem of slow convergence rate in traditional ant colony algorithm for UAV path planning,a new cellular ant colony algorithm is ***,we construct a sector prediction area in grid environment *** we build heuristic and obstacle repulsion functions of target nodes in the prediction *** these functions,we can get the Bayesian conditional probability and posterior estimation of target *** the end,we select the node with the largest posterior estimation as the next path *** simulation results show that the new algorithm has better global search ***,using the sector prediction area makes the planned path more consistent with the UAV flight *** the designed functions in the sector prediction area speeds up the path search process.

关键词： Bayesian ant colony algorithm Sector prediction region Bayesian posteriori probability

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