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SAR Target Recognition Using Improved Monogenic-Based Feature Extraction Framework

SAR Target Recognition Using Improved Monogenic-Based Featur...

引用

IEEE International Conference on Radar

作者： Feng Li Weijun Yao Yang Li Wei Chen Radar Research Laboratory School of Information and Electronics Beijing Institute of Technology Beijing Institute of Technology Chongqing Innovation Center Beijing China Radar Research Lab Beijing Key Laboratory of Embedded Real-time Information Processing Technology School of Information and Electronics Beijing Institute of Technology Beijing China

ISBN: (纸本)9781665468893

Applying computer vision methods to synthetic aperture radar (SAR) image recognition is a research trend in recent years, and a series of valuable results have been achieved. In order to use machine learning classifiers for recognition, it is necessary to extract effective features, and most of these features are directly extracted based on grayscale SAR images. SAR data is usually rare, and more difficult to collect than optical images. Therefore, the problem of recognition using a small-size training set for SAR is more challenging to practical pattern recognition methods. The monogenic signal is an extended version of analytic signals in high-dimensional space which has attracted attention. In this paper, a new recognition framework which is based on feature dimensionality augmentation using combined multi-scale monogenic components and histogram of oriented gradient (HOG) feature is proposed. Proposed feature is named as MONO-HOG. This letter focuses on recognition under both standard operating condition (SOC) and small-sample scene. Experiments on moving and stationary target automatic recognition (MSTAR) data set show that our proposed framework has satisfying performance.

关键词： Training Computer vision Image recognition Target recognition Feature extraction Optical imaging Radar polarimetry

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Research on Crop Planting Area Classification from Remote Sensing Image Based on Deep Learning

Research on Crop Planting Area Classification from Remote Se...

引用

2019 IEEE International Conference on Signal, information and Data processing, ICSIDP 2019

作者： Huang, Yun Tang, Linbo Jing, Donglin Li, Zhen Tian, Yibing Zhou, Shichao Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9781728123455

Remote sensing technology is widely used in agriculture monitoring because of the advantages of large-area simultaneous observation, low cost and dynamic monitoring of time and space. However, a manual visual interpretation method is often used to extract the information behind the remote sensing images, which is time and labor consuming. Moreover, handcraft features such as texture and structure of crop images are applied to classify crop planting area while these features are not robust. In order to reduce the cost and enhance the classification accuracy, we improved the state-of-the-art image semantic segmentation network SegNet in crop planting area classification which can speed up the convergence and reduce the model size largely with small gains in accuracy. The experiment result shows that the classification accuracy of the improved SegNet is slightly increased compared with SegNet, and the computational cost (FLOPs) of the improved SegNet is much less than SegNet. © 2019 IEEE.

关键词： Remote sensing

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Ground Target Automatic Recognition and Tracking in Complex Battlefield Environment

Ground Target Automatic Recognition and Tracking in Complex ...

引用

2019 IEEE International Conference on Signal, information and Data processing, ICSIDP 2019

作者： Wu, Chen Tang, Linbo Duan, Chenhui Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9781728123455

With the developments of science and the changes of modern warfare modes, the precise guidance and attack towards hostile targets in complex battlefield environments has extremely important military needs and research significance. However, the existing guidance mode is often constrained by factors such as battlefield background interference, target appearance variation and limited data links, which in turn affects tracking and strike performance. To this end, we present an integrated recognition and tracking scheme for the ground targets in complex battlefield environment. Specifically, with the missile-target distance information, we design an adaptive scale estimation scheme to tackle the aspect ratio variation during tracking. Furthermore, an efficient tracking monitoring index is advocated to judge the tracking condition as well as forecast the potential tracking failure. Once tracking failure is determined, a deep-learning based detection module is activated to re-detect the target as well as correct the tracking drift. In the strike stage of the mission, the key region of the hostile target would be identified and locked by the missile in order to maximize the attack effect. The proposed recognition and tracking scheme are tested upon extensive actual videos and achieved a tracking accuracy over 85%, which demonstrates its effectiveness as well as provides a novel solution for accurate and robust attack on enemy targets in a complex battlefield environment. © 2019 IEEE.

关键词： Object detection

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Lightweight convolutional neural network for false alarm elimination in SAR ship detection 5

Lightweight convolutional neural network for false alarm eli...

引用

IET International Radar Conference 2020, IET IRC 2020

作者： Zheng, Xingfei Feng, Yangkai Shi, Hao Zhang, Bocheng Chen, Liang Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Shanghai Institute of Satllite Engineering Shanghai China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Beijing Institute of Technology Chongqing Innovation Center Chongqing China

ISBN: (纸本)9781839535635

SAR ship detection is an important task of SAR image interpretation and plays a significant role in global marine surveillance. However, since there are usually many false alarms in the detection results, a desirable performance is rarely achieved. Aiming at this problem, this paper proposes an improved method for the ship detection process, using the lightweight convolutional neural network EfficientNet-B0 as a false alarm elimination network, cascaded after the detection stage, to reduce the false alarm rate in the detection results and improve the overall detection accuracy. The results on the MSTAR data set show that EfficientNet-B0 has a good classification effect, and the experiments on the custom data set also verify the effectiveness of the network. © 2020 Institution of Engineering and technology. All rights reserved.

关键词： Synthetic aperture radar

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De Novo Drug Design via Multi-Label Learning and Adversarial Autoencoder

De Novo Drug Design via Multi-Label Learning and Adversarial...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qing Ye Xiaolong Zhang Xiaoli Lin Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9781665429825

generating new molecules is very important for drug design. Currently, many deep generative models have been designed, such as variational autoencoder (VAE), adversarial autoencoder (AAE), and reinforcement learning (RL). However, many problems are also existed in these models. Firstly, the information among molecules could be not utilized in optimizing these models. Secondly, some useful molecule information could be not used, such as fingerprint. Thirdly, the information contained in different molecule representations could be not used together. To overcome the above problems, in this paper, a multi-label learning and adversarial autoencoder (MLAAE) based de novo drug design method is designed. MLAAE enhances its learning ability with a new designed multi-label classifier and a new designed double AAEs collaborative optimization framework. These new designs can learn a better latent space whose global distribution is similar with the random distribution, but local distribution contains much information. As a result, the generator can be trained by more information, and the input of generator in testing is similar with that in training. The conducted experiments validate the effectiveness of our MLAAE.

关键词： Drugs Training Measurement Collaboration Reinforcement learning Fingerprint recognition Generators

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Discovering DTI and DDI by Knowledge Graph with MHRW and Improved Neural Network

Discovering DTI and DDI by Knowledge Graph with MHRW and Imp...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shuo Zhang Xiaoli Lin Xiaolong Zhang Hubei Key Laboratory of Intelligent Information Processing and Real-time Industrial System School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9781665429825

Drug discovery is of great significance in medical and biological research, while the study of Drug-Target Interaction (DTI) and Drug-Drug Interaction (DDI) can help accelerate drug discovery progress. This paper proposes a new hybrid method for DTI prediction and DDI prediction, which is called MHRW2Vec-TBAN that combines graph representation learning and neural network. MHRW2VecTBAN first constructs knowledge graph KG-DTI and KG-DDI that integrate data related to drugs and targets. Then, an improved graph representation learning model, MHRW2Vec model, is used to obtain feature vectors of reflecting the network structure information for improving the performance of representation learning. Finally, the feature vectors obtained are input to the improved neural network model TextCNN-BiLSTM-Attention Network (TBAN). The experimental results show that, compared with other existing methods, our method could discover more deeper the relationship between drugs and their potential neighborhoods, and has great improvements in DTI prediction and DDI prediction. In addition, the case study of prediction COVID-19 DTI also shows that the proposed model has the potential for actual drug discovery.

关键词： Drugs Representation learning Knowledge engineering COVID-19 Conferences Biological system modeling Neural networks

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RIPPB: A Robust and Improved PBFT Protocol for Blockchain

RIPPB: A Robust and Improved PBFT Protocol for Blockchain

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Meng Ding Heng He Rui Qiao Xiaohu Zhou School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China School of Computing Engineering and Built Environment Birmingham City University Birmingham UK

ISBN: (纸本)9781665409858

In this paper, we propose RIPPB, a robust and improved PBFT protocol for Blockchain, to improve the quality of service for blockchain applications. At RIPPB, we introduce reputation for nodes and use reputation as the voting weight for node consensus to go against Sybil attacks. And we design a novel improved logistic regression model to assign reputations to nodes based on their behavior involved in block generation. With this model, nodes performing Stake-grinding attacks and Bribery attacks, face high reputation penalties, which is unacceptable for malicious nodes to reduce the probability of launching these attacks. In addition, RIPPB adds a pre-commit phase to the PBFT algorithm to reduce the number of inter-node communications, thus improving the performance of the protocol. Cost-of-attack analysis shows the high robustness of our protocol. Experimental results show a 28% improvement in the throughput of our protocol over PBFT.

关键词： Industrial electronics Protocols Quality of service Throughput Robustness Blockchains Behavioral sciences

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Multi-Object Tracking Based on Anchor-Free Detection and Message Passing Association

Multi-Object Tracking Based on Anchor-Free Detection and Mes...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Zhou Fang Zhi Zhang College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9781665409858

Due to the current multi-target tracking work, in the scene of dense and staggered targets, the tracked targets are prone to ID switch, which greatly affects the tracking effect. Aiming at this problem, this paper proposed a new multi-target tracking algorithm: using an improved anchor-free detection method and extracting features from multiple aspects, combined with the message passing graph neural network to participate in the data association process to further improve the exchange of feature information, so as to improve the algorithm's ability to associate target information data between frames and improve the tracking accuracy. After conducting quantitative analysis and comparison experiments on datasets such as MOT17 and MOT20, the experimental results show that the algorithm in this paper can effectively improve the problem of target ID switching and provide better tracking accuracy and performance.

关键词： Industrial electronics Target tracking Statistical analysis Message passing Switches Feature extraction real-time systems

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Uncertainty-Aware Semi-Supervised Framework for Automatic Segmentation of Macular Edema in Oct Images

Uncertainty-Aware Semi-Supervised Framework for Automatic Se...

引用

IEEE International Symposium on Biomedical Imaging

作者： Xiaoming Liu Shaocheng Wang School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan

ISBN: (纸本)9781665412469;9781665429474

As a non-invasive imaging modality, Optical coherence tomography (OCT) has been widely used in clinical applications, mainly for monitoring the development of ophthalmic diseases. OCT can provide high-resolution images to reveal changes in retinal tissues, such as the accumulation of fluid caused by macular edema. This paper proposed an uncertainty-aware semi-supervised framework for retinal fluid segmentation. This framework composed of a teacher network and a student network, and they share the same network architecture. Each network consists of an encoder and three decoders. The three decoders in these two networks can simultaneously predict the probability map, contour map and distance map. A fusion operation is performed on the three maps predicted by the student network to obtain the segmentation results. The method proposed has been verified effectiveness in the RTOUCH challenge dataset.

关键词： Image segmentation Fluids Optical coherence tomography Network architecture Retina Decoding Reliability

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Text Detection of Clinical Medical Documents Based on SWT Algorithm 20

Text Detection of Clinical Medical Documents Based on SWT Al...

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4th International Conference on Computer Science and Application Engineering, CSAE 2020

作者： Wang, Jingyi Liu, Zhao Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology and College Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei China

ISBN: (纸本)9781450377720

Clinical medical document images are rich in rich text information, and the detection of text areas is the basis for subsequent text analysis. However, the existing text detection algorithms are mainly for a single language, and the results for mixed Chinese and English text detection are not ideal. In this regard, this paper proposes a hybrid Chinese and English text detection algorithm based on the stroke width transform (SWT) algorithm. The algorithm first preprocesses the image, then determines the connected domain, and determines and filters the text area based on the morphological rules of the connected domain, then connects the pixels into Chinese characters and English characters according to the stroke characteristics, and finally outputs the text area result of the image. The simulation experiment results show that the algorithm can detect the Chinese and English mixed text areas in clinical medical document images better than the traditional text detection algorithm, and the effect is better. © 2020 ACM.

关键词： Character recognition

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