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arXiv 2022年

作者： Li, Chenglong Yang, Xiaobin Wang, Guohao Zheng, Aihua Tan, Chang Jia, Ruoran Tang, Jin Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Lab of Intelligent Computing and Signal Processing Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China iFLYTEK Co. Ltd. Hefei230088 China

License plate recognition plays a critical role in many practical applications, but license plates of large vehicles are difficult to be recognized due to the factors of low resolution, contamination, low illumination, and occlusion, to name a few. To overcome the above factors, the transportation management department generally introduces the enlarged license plate behind the rear of a vehicle. However, enlarged license plates have high diversity as they are non-standard in position, size, and style. Furthermore, the background regions contain a variety of noisy information which greatly disturbs the recognition of license plate characters. Existing works have not studied this challenging problem. In this work, we first address the enlarged license plate recognition problem and contribute a dataset containing 9342 images, which cover most of the challenges of real scenes. However, the created data are still insufficient to train deep methods of enlarged license plate recognition, and building large-scale training data is very time-consuming and high lab.r cost. To handle this problem, we propose a novel task-level disentanglement generation framework based on the Disentangled Generation Network (DGNet), which disentangles the generation into the text generation and background generation in an end-to-end manner to effectively ensure diversity and integrity, for robust enlarged license plate recognition. Extensive experiments on the created dataset are conducted, and we demonstrate the effectiveness of the proposed approach in three representative text recognition frameworks. © 2022, CC BY.

关键词： Optical character recognition

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A new logistic map based chaotic biogeography-based optimization approach for cluster analysis

A new logistic map based chaotic biogeography-based optimiza...

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IEEE International Conference on Progress in Informatics and Computing (PIC)

作者： Shui-Hua Wang Hui-Min Lu Yu-Jie Li Yang Wang Zhi-Min Chen Yu-Min Wei Wen-Juan Jia Fang-Yuan Liu Yu-Dong Zhang Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Policy Academy Changsha Hunan China School of Computer Science and Technology Nanjing Normal University Nanjing Jiangsu China Department of Electrical Engineering CUNY New York NY USA Department of Mechanical and Control Engineering Kyushu Institute of Technology Fukuoka Prefecture Japan School of Information Engineering Yangzhou University Yangzhou Jiangsu China School of Physics and Electronic Engineering Taizhou University Taizhou Zhejiang China School of Electronic Information Shanghai Dianji University Shanghai China School of Energy and Mechanical Engineering Nanjing Normal University Nanjing Jiangsu China Key Laboratory of Intelligent Computing and Information Processing in Fujian Provincial University Quanzhou Normal University Quanzhou Fujian China

ISBN: (纸本)9781509034857

Cluster analysis is important in scientific and industrial fields. In this study, we proposed a novel chaotic biogeography-based optimization (CBBO) method, and applied it in centroid-based clustering methods. The results over three types of simulation data showed that this proposed CBBO method gave better performance than chaotic particle swarm optimization, genetic algorithm, firefly algorithm, and quantum-behaved particle swarm optimization. In all, our CBBO method is effective in centroid-based clustering.

关键词： Genetic algorithms Optimization Clustering algorithms Particle swarm optimization Algorithm design and analysis Logistics Mathematical model

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Fast and effective method for stereo matching

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Nanjing Youdian Daxue Xuebao (Ziran Kexue Ban)/Journal of Nanjing University of Posts and Telecommunications (Natural Science) 2007年第3期27卷 28-33页

作者： Dong, Yu-Ning College of Communication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing 210003 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210093 China Jiangsu Provincial Key Laboratory of Computer Information Processing Technology Soochow University Suzhou 215006 China

In this paper we propose a novel edge-based, fast and effective stereo matching method. If utilizes various geometrical and local area constraints as well as structural information among edge segments to perform a constraint-directed matching. By employing a heuristic lab.lling technique, the combinatorial search for isomorphic graphs is greatly reduced in the matching process. The implementation and experimental results are presented to show the efficacy of the proposed method.

关键词： 3D information recovery Binocular vision Stereo matching

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Slnspeech: Solving Extended Speech Separation Problem with the Help of Sign Language

SSRN

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SSRN 2023年

作者： Wu, Jiasong Li, Xuan Li, Taotao Meng, Fanman Kong, Youyong Yang, Guanyu Senhadji, Lotfi Shu, Huazhong Laboratory of Image Science and Technology Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing210096 China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast University Nanjing210096 China Univ Rennes INSERM LTSI - UMR 1099 RennesF-35000 France Univ Rennes INSERM RennesF-35000 France

Previous audio-visual speech separation methods use the synchronization of the speaker's facial movement and speech in the video to supervise the speech separation in a self-supervised way. In this paper, we propose a model to solve the speech separation problem assisted by both face and sign language, which we call the extended speech separation problem. We design a general deep learning network for learning the combination of three modalities, audio, face, and sign language information, for better solving the speech separation problem. To train the model, we introduce a large-scale dataset named the Sign Language News Speech (SLNSpeech) dataset in which three modalities of audio, face, and sign language coexist. Experiment results show that the proposed model has better performance and robustness than the usual audio-visual system. Besides, sign language modality can also be used alone to supervise speech separation task, and the introduction of sign language is helpful for hearing-impaired people to learn and communicate. Last, our model is a general speech separation framework and can achieve very competitive separation performance on two open-source audio-visual datasets. The code is availab.e at https://***/iveveive/SLNSpeech. © 2023, The Authors. All rights reserved.

关键词： Audition

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Matching Biomedical Ontologies with Long Short-Term Memory Networks

Matching Biomedical Ontologies with Long Short-Term Memory N...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Chao Jiang Xingsi Xue Intelligent Information Processing Research Center Fujian University of Technology Fuzhou Fujian China School of Computer Science and Mathematics Fujian University of Technology Fuzhou Fujian China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou Fujian China Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology Guilin Guangxi China Fujian Key Lab for Automotive Electronics and Electric Drive Fujian University of Technology Fuzhou Fujian China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Semantic Web (SW) has attracted the increasing attention of researchers, which facilitates people to link and handle various data. Ontology is the kernel technique of SW, and biomedical ontology is a state-of-art biomedical knowledge modeling technique, which formally defines the biomedical concepts and their relationships. However, the same biomedical concepts in different biomedical ontologies could be defined in various contexts or with different terms, which yields the biomedical ontology heterogeneity problem. It is crucial to find mapping among heterogeneity concepts of different biomedical ontologies for bridging the semantic gaps, which is the so-called biomedical ontology matching. Biomedical ontology matching problem is an open challenge due to the rich semantic meaning and the flexible representation on a biomedical concept. To address this challenging problem, in this work, it is regarded as a binary classification problem, and a Long Short-Term Memory Networks (LSTM)-based ontology matching technique is proposed to solve it. Our proposal improves the quality of the alignment by introducing the char-embedding technique, which takes into account the semantic and context information of concepts. The comparing results with OAEI's participants show the effectiveness of our proposal.

关键词： Ontologies Semantics Logic gates Training Human anatomy Proposals Biomedical measurement

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Multi-level graph convolutional network with automatic graph learning for hyperspectral image classification

arXiv

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arXiv 2020年

作者： Wan, Sheng Gong, Chen Pan, Shirui Yang, Jie Yang, Jian PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Laboratory of Image Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Faculty of Information Technology Monash University ClaytonVIC3800 Australia

Nowadays, deep learning methods, especially the Graph Convolutional Network (GCN), have shown impressive performance in hyperspectral image (HSI) classification. However, the current GCN-based methods treat graph construction and image classification as two separate tasks, which often results in suboptimal performance. Another defect of these methods is that they mainly focus on modeling the local pairwise importance between graph nodes while lack the capability to capture the global contextual information of HSI. In this paper, we propose a Multi-level GCN with Automatic Graph Learning method (MGCN-AGL) for HSI classification, which can automatically learn the graph information at both local and global levels. By employing attention mechanism to characterize the importance among spatially neighboring regions, the most relevant information can be adaptively incorporated to make decisions, which helps encode the spatial context to form the graph information at local level. Moreover, we utilize multiple pathways for local-level graph convolution, in order to leverage the merits from the diverse spatial context of HSI and to enhance the expressive power of the generated representations. To reconstruct the global contextual relations, our MGCN-AGL encodes the long range dependencies among image regions based on the expressive representations that have been produced at local level. Then inference can be performed along the reconstructed graph edges connecting faraway regions. Finally, the multi-level information is adaptively fused to generate the network output. In this means, the graph learning and image classification can be integrated into a unified framework and benefit each other. Extensive experiments have been conducted on three real-world hyperspectral datasets, which are shown to outperform the state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Image classification

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Hand Hygiene Assessment via Joint Step Segmentation and key Action Scorer

arXiv

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arXiv 2022年

作者： Li, Chenglong Zhu, Qiwen Liu, Tubiao Tang, Jin Su, Yu Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China Hefei Normal University Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230061 China

Hand hygiene is a standard six-step hand-washing action proposed by the World Health Organization (WHO). However, there is no good way to supervise medical staff to do hand hygiene, which brings the potential risk of disease spread. Existing action assessment works usually make an overall quality prediction on an entire video. However, the internal structures of hand hygiene action are important in hand hygiene assessment. Therefore, we propose a novel fine-grained learning framework to perform step segmentation and key action scorer in a joint manner for accurate hand hygiene assessment. Existing temporal segmentation methods usually employ multi-stage convolutional network to improve the segmentation robustness, but easily lead to over-segmentation due to the lack of the long-range dependence. To address this issue, we design a multi-stage convolution-transformer network for step segmentation. Based on the observation that each hand-washing step involves several key actions which determine the hand-washing quality, we design a set of key action scorers to evaluate the quality of key actions in each step. In addition, there lacks a unified dataset in hand hygiene assessment. Therefore, under the supervision of medical staff, we contribute a video dataset that contains 300 video sequences with fine-grained annotations. Extensive experiments on the dataset suggest that our method well assesses hand hygiene videos and achieves outstanding performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

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Converged reconfigurable intelligent surface and mobile edge computing for space information networks

arXiv

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arXiv 2021年

作者： Cao, Xuelin Yang, Bo Huang, Chongwen Yuen, Chau Zhang, Yan Niyato, Dusit Han, Zhu Engineering Product Development Pillar Singapore University of Technology and Design Singapore Singapore College of Information Science and Electronic Engineering Zhejiang Provincial Key Lab of information processing communication and networking International Joint Innovation Center Zhejiang University China Department of Informatics University of Oslo Norway School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Department of Electrical and Computer Engineering University of Houston United States

Space information networks (SIN) are facing an ever-increasing thirst for high-speed and high-capacity seamless data transmission due to the integration of ground, air, and space communications. However, this imposes a new paradigm on the architecture design of the integrated SIN. Recently, reconfigurable intelligent surfaces (RISs) and mobile edge computing (MEC) are the most promising techniques, conceived to improve communication and computation capability by reconfiguring the wireless propagation environment and offloading. Hence, converging RISs and MEC in SIN is becoming an effort to reap the double benefits of computation and communication. In this article, we propose an RIS-assisted collab.rative MEC architecture for SIN and discuss its implementation. Then we present its potential benefits, major challenges, and feasible applications. Subsequently, we study different cases to evaluate the system data rate and latency. Finally, we conclude with a list of open issues in this research area. Copyright © 2021, The Authors. All rights reserved.

关键词： Mobile edge computing

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CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-Resolution

arXiv

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arXiv 2022年

作者： Gao, Guangwei Xu, Zixiang Li, Juncheng Yang, Jian Zeng, Tieyong Qi, Guo-Jun The Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China The Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China The School of Communication & Information Engineering Shanghai University Shanghai China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing China The School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China The Center for Mathematical Artificial Intelligence Department of Mathematics The Chinese University of Hong Kong Hong Kong Research Center for Industries of the Future School of Engineering Westlake University OPPO Research Seattle United States

Recently, deep convolution neural networks (CNNs) steered face super-resolution methods have achieved great progress in restoring degraded facial details by joint training with facial priors. However, these methods have some obvious limitations. On the one hand, multi-task joint learning requires additional marking on the dataset, and the introduced prior network will significantly increase the computational cost of the model. On the other hand, the limited receptive field of CNN will reduce the fidelity and naturalness of the reconstructed facial images, resulting in suboptimal reconstructed images. In this work, we propose an efficient CNN-Transformer Cooperation Network (CTCNet) for face super-resolution tasks, which uses the multi-scale connected encoder-decoder architecture as the backbone. Specifically, we first devise a novel Local-Global Feature Cooperation Module (LGCM), which is composed of a Facial Structure Attention Unit (FSAU) and a Transformer block, to promote the consistency of local facial detail and global facial structure restoration simultaneously. Then, we design an efficient Feature Refinement Module (FRM) to enhance the encoded features. Finally, to further improve the restoration of fine facial details, we present a Multi-scale Feature Fusion Unit (MFFU) to adaptively fuse the features from different stages in the encoder procedure. Extensive evaluations on various datasets have assessed that the proposed CTCNet can outperform other state-of-the-art methods significantly. Source code will be availab.e at https://***/IVIPlab.CTCNet. Copyright © 2022, The Authors. All rights reserved.

关键词： Restoration

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Learning robust and discriminative low-rank representations for face recognition with occlusion

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Pattern Recognition 2017年 66卷 129-143页

作者： Guangwei Gao Jian Yang Xiao-Yuan Jing Fumin Shen Wankou Yang Dong Yue Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Engineering Laboratory of Big Data Analysis and Control for active distribution network Nanjing University of Posts and Telecommunications Nanjing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) Fuzhou China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Automation Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Automation Southeast University Nanjing China

For robust face recognition tasks, we particularly focus on the ubiquitous scenarios where both training and testing images are corrupted due to occlusions. Previous low-rank based methods stacked each error image into a vector and then used L 1 or L 2 norm to measure the error matrix. However, in the stacking step, the structure information of the error image can be lost. Depart from the previous methods, in this paper, we propose a novel method by exploiting the low-rankness of both the data representation and each occlusion-induced error image simultaneously, by which the global structure of data together with the error images can be well captured. In order to learn more discriminative low-rank representations, we formulate our objective such that the learned representations are optimal for classification with the availab.e supervised information and close to an ideal-code regularization term. With strong structure information preserving and discrimination capabilities, the learned robust and discriminative low-rank representation (RDLRR) works very well on face recognition problems, especially with face images corrupted by continuous occlusions. Together with a simple linear classifier, the proposed approach is shown to outperform several other state-of-the-art face recognition methods on databases with a variety of face variations.

关键词： Face recognition Low-rank matrix recovery Nuclear norm

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