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20th IEEE International Conference on Communication Technology, ICCT 2020

作者： Lu, Hua Li, Dengwang Kpalma, Kidiyo Yang, Mingqiang Shandong Normal University Shandong Key Lab. of Med. Physics and Image Processing Shandong Provincial Engineering and Technical Center of Light Manipulations School of Physics and Electronics Jinan250358 China Ietr VAADER- Umr 6164 Univ Rennes Insa Rennes RennesF-35000 France Shandong University School of Information Science and Engineering Qingdao266237 China

ISBN: (纸本)9781728181417

In this paper, we propose a new model for micro-expression detection in videos in conjunction with a set of facial keypoints. The main contribution lies at the construction of geometric features extracted using the geometric distances between pairs of keypoints in different groups. The proposed geometric features-based micro-expression detection model requires a very low computation complexity and simultaneously attains highly accurate detection rates. We compare the proposed geometric features-based model to the existing state-of-the-art micro-expression detection and recognition models in three datasets, where the proposed method has achieved better, or at least comparably accurate results but requiring much less computation time. © 2020 IEEE.

关键词： Support vector machines

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Intra-Guided Dual-Energy Material Decomposition Method for Dual-Source CBCT Systems 46

Intra-Guided Dual-Energy Material Decomposition Method for D...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Zhong, Xinyun Lyu, Tianling Wu, Zhan Gao, Wei Xi, Yan Zhao, Wei Zhu, Wentao Chen, Yang Zhejiang Lab Hangzhou China First-Imaging Tech. Shanghai China Beihang University Beijing China Southeast University School of Computer Science and Engineering Laboratory of Image Science and Technology Nanjing China Southeast University Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Nanjing China

ISBN: (纸本)9798350371499

Dual-energy CBCT imaging plays a crucial role in advanced imaging applications due to its ability to quantify material components. Although there are multiple established systems for dual-energy imaging, they often come with high deployment costs. This study investigates the feasibility of dual-energy material decomposition on a less expensive dual-source CBCT system. Due to the specific geometry of dual-source CBCT, cone-beam (CB) artifacts seriously degrade the material decomposition quality when the direct inversion method is adopted. In this paper, a novel dual-source intra-guided material decomposition framework is proposed to suppress CB artifacts in the material maps. Firstly, a dual-source two-pass algorithm is developed for artifact pre-correction during image reconstruction. Then, the structure of small cone-angle regions from one X-ray source is utilized to guide cone beam artifact removal in large cone-angle regions from another source. Finally, direct matrix inverse material decomposition is applied to the filtered image. Extensive experiments have been conducted on the authentic human phantom and demonstrate that the proposed framework achieves excellent performance in cone beam artifact reduction compared to other methods, and significantly enhances the quality of the material maps. © 2024 IEEE.

关键词： image reconstruction

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RGB-D based action recognition with light-weight 3D convolutional networks

arXiv

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arXiv 2018年

作者： Zhang, Haokui Li, Ying Wang, Peng Liu, Yu Shen, Chunhua Shaanxi Provincial Key Lab of Speech and Image Information Processing School of Computer Science Northwestern Polytechnical University Xi’an710129 China School of Computer Science University of Adelaide AdelaideSA5005 Australia

Different from RGB videos, depth data in RGB-D videos provide key complementary information for tristimulus visual data which potentially could achieve accuracy improvement for action recognition. However, most of the existing action recognition models solely using RGB videos limit the performance capacity. Additionally, the state-of-the-art action recognition models, namely 3D convolutional neural networks (3D-CNNs) contain tremendous parameters suffering from computational inefficiency. In this paper, we propose a series of 3D lightweight architectures for action recognition based on RGB-D data. Compared with conventional 3D-CNN models, the proposed lightweight 3D-CNNs have considerably less parameters involving lower computation cost, while it results in favorable recognition performance. Experimental results on two public benchmark datasets show that our models can approximate or outperform the state-of-the-art approaches. Specifically, on the RGB+DNTU (NTU) dataset, we achieve 93.2% and 97.6% for cross-subject and cross-view measurement, and on the Northwestern-UCLA Multiview Action 3D (N-UCLA) dataset, we achieve 95.5% accuracy of cross-view. Copyright © 2018, The Authors. All rights reserved.

关键词： Convolution

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Analysis of multiscale sign series entropy of the young and middle-aged electroencephalogram signals

Analysis of multiscale sign series entropy of the young and ...

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International Congress on image and Signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Fei Du Shitong Wang Jun Wang Jiafei Dai Fengzhen Hou Jin Li Image Processing and Image Communications Key Lab Nanjing Univ. of Posts & Telecomm Nanjing China Nanjing General Hospital of Nanjing Military Command Nanjing China School of Science China Pharmaceutical University Nanjing China College of Physics and Information Technology Shanxi Normal University Xian China

ISBN: (纸本)9781509037117

The physiological analysis of electroencephalogram (EEG) signals is of great significance in assessing the activity of the brain function and the physiological state. EEG is a means of clinical examination of brain diseases. Age is one of the important factors that affect the results of the EEG. EEG signal analysis is mainly to analyze the time series of the signal, multiscale entropy (MSE) analysis [1-3] is the method that used to analyze the finite length of the time series. Multiscale sign series entropy (MSSE) method is proposed for the analysis of EEG signals in the young and middle-aged. We use the proposed method to analyze the signals from several aspects of data length, word length, noise, multi scale etc. By analyzing the influence of these factors, we can still distinguish the EEG signals of different ages. Multiscale sign series entropy (MSSE) analysis algorithm can effectively separate the brain electrical signals from the young and middle aged, which is expected to have a certain reference value for the traditional pathological analysis of the EEG signals.

关键词： Electroencephalography Entropy Time series analysis Brain models Algorithm design and analysis Physiology

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Dual-Source CBCT for Larger Longitudinal Coverage: System Design and image Reconstruction 46

Dual-Source CBCT for Larger Longitudinal Coverage: System De...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Zhang, Xusheng Lyu, Tianling Shi, Yijie Zhong, Xinyun Wu, Zhan Xi, Yan Wang, Shijie Chen, Yang Zhu, Wentao Southeast University School of Software Nanjing China Zhejiang Lab Hangzhou China First-Imaging Tech. Shanghai China Southeast University Laboratory of Image Science and Technology The School of Computer Science and Engineering Nanjing China Southeast University Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Nanjing China

ISBN: (纸本)9798350371499

The application of CBCT systems in intraoperative environments has become increasingly common, but concurrent CBCT systems are unsuitable for situations that require a large longitudinal imaging FoV, such as orthopedics. To increase longitudinal coverage, we developed a dual-source CBCT (DS-CBCT) system in which two ray sources are symmetrically placed along the central plane. After further analyzing its geometric characteristics, we propose an analytical reconstruction algorithm termed DT-FDK specialized for DS-CBCT, which combines cone-beam rebinning and two rays with smaller cone angles among the four conjugate rays to further suppress cone beam artifacts. The system design and reconstruction algorithm are tested on both simulated and real-scanned data. Results show that the DS-CBCT can expand the effective imaging volume by 37.1% compared to single-source CBCT systems. The reconstructed images by DT-FDK also show improved image quality compared to traditional reconstruction algorithms. © 2024 IEEE.

关键词： Cones

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An improved algorithm of pattern matching for information security audit system

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Journal of Computational Information Systems 2008年第5期4卷 2221-2228页

作者： Ding, Lei Yu, Fei Xu, Cheng Yue, Guangxue School of Physics Science and Information Engineering Jishou University Jishou 416000 China Provincial Key Lab. of Image Processing and Imagecom Communication Nanjing University of Post and Telecommunications Nanjing 210003 China College of Computer and Communication Hunan University Changsha 410082 China

At present, network information audit system is almost based on text information filtering. Text categorization is the basic technology of text information filtering. Pattern matching algorithms are very important for the rule based information audit system. It directly influences the accuracy and real-time performance of the system. The KMP and BM algorithms are introduced in this paper. Research is carried out to improve the BM algorithm and a better BM algorithm is proposed According to the situation that the system characters group is bigger and that characters of the pat tern are less, the thesis puts forward an improved string-matching algorithm. The results of our experiments prove that it promotes the precision of text categorization. © 2008 Binary Information Press.

关键词： Pattern matching

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Hybrid Deep Neural Network--Hidden Markov Model (DNN-HMM) Based Speech Emotion Recognition

Hybrid Deep Neural Network--Hidden Markov Model (DNN-HMM) Ba...

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International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII

作者： Longfei Li Yong Zhao Dongmei Jiang Yanning Zhang Fengna Wang Isabel Gonzalez Enescu Valentin Hichem Sahli VUB-NPU Joint AVSP Research Lab Northwestern Polytechnical University Xi'an China Shaanxi Provincial Key Lab on Speech and Image Information Processing VUB-NPU Joint AVSP Research Lab Vrije Universiteit Brussel Brussels Belgium Electronics & Informatics Department

Deep Neural Network Hidden Markov Models, or DNN-HMMs, are recently very promising acoustic models achieving good speech recognition results over Gaussian mixture model based HMMs (GMM-HMMs). In this paper, for emotion recognition from speech, we investigate DNN-HMMs with restricted Boltzmann Machine (RBM) based unsupervised pre-training, and DNN-HMMs with discriminative pre-training. Emotion recognition experiments are carried out on these two models on the eNTERFACE'05 database and Berlin database, respectively, and results are compared with those from the GMM-HMMs, the shallow-NN-HMMs with two layers, as well as the Multi-layer Perceptrons HMMs (MLP-HMMs). Experimental results show that when the numbers of the hidden layers as well hidden units are properly set, the DNN could extend the labeling ability of GMM-HMM. Among all the models, the DNN-HMMs with discriminative pre-training obtain the best results. For example, for the eNTERFACE'05 database, the recognition accuracy improves 12.22% from the DNN-HMMs with unsupervised pre-training, 11.67% from the GMM-HMMs, 10.56% from the MLP-HMMs, and even 17.22% from the shallow-NN-HMMs, respectively.

关键词： Hidden Markov models Emotion recognition Training Speech recognition Speech Databases Neural networks

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3D FACE REPRESENTATION AND RECOGNITION BY INTRINSIC SHAPE DESCRIPTION MAPS

3D FACE REPRESENTATION AND RECOGNITION BY INTRINSIC SHAPE DE...

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IEEE International Conference on Acoustics, Speech, and Signal processing

作者： Zhe Guo Yanning Zhang Yong Xia Zenggang Lin Dagan Feng Shaanxi Provincial Key Lab of Speech & Image Information Processing (SAIIP) School of Computer Science Northwestern Polytechnical University Xi'an China Biomedical and Multimedia Information Technology (BMIT) Research Group School of Information Technologies University of Sydney Australia

ISBN: (纸本)9781424442959

We present a novel method for 3D face recognition, in which the 3D facial surface is first mapped into a 2D domain with specified resolution through a global optimization by constrained conformal geometric maps. The Intrinsic Shape Description Map (ISDM) is then constructed through a modeling technique capable to express geometric and appearance information of the 3D face. Hence the 3D surface matching problem can be simplified to a 2D image matching problem, which greatly reduces the computational complexity. Finally, the Intrinsic Shape Description Feature (ISDF) of ISDM and the discrimination analysis can be calculated. Experimental results implemented on GavabDB demonstrate that our proposed method significantly outperforms the existing methods with respect to pose variation.

关键词： Face recognition Conformal Geometric Maps Intrinsic Shape Description Map

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Vehicle Color Recognition Using Monocular camera

Vehicle Color Recognition Using Monocular camera

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2011年无线通信与信号处理国际会议(WCSP 2011)

作者： Mengjie Yang Xiuchang Zhu Liang Li Guang Han Xiaofei Li Jiangsu Provincial Key Lab of Image Processing and ImageCommunication Nanjing Univ. of P&TNanjing Engineering Research Center of Wideband Wireless Communication and Display Technique Ministry of Ed Engineering Research Center of Wideband WirelessCommunication and Display Technique Ministry ofEduc

This paper introduces a novel moving vehicle color recognition method. By processing videos recorded by monocular camera on traffic, we get single color image of vehicles which are crossed the traffic in any direction. First, paper employed a H-S two dimension histogram method to detect the color of the vehicles, and distinguish red, yellow, green and blue four color vehicles. Due to deal with the vehicles that do not satisfy above 4 colors, Regional Color Judgment method is proposed. This method consists of the following five steps: Firstly, position the color detection region by identifying the travelling direction of the vehicle. Then pick out the candidate color extraction region used to vehicle recognition. At last extract the color of the candidate region and match the color with the templates we predefined, the color of the best fit template is the color of the vehicle.

关键词： vehicle color recognition region choice HSV space vehicle detection

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BAF-Net: Bidirectional Attention-aware Fluid Pyramid Feature Integrated Multi-modal Fusion Network for Prognosis

BAF-Net: Bidirectional Attention-aware Fluid Pyramid Feature...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Huiqin Wu Wenbing Lv Dongyang Du Hui Xu Guoyu Lin Zidong Zhou Jianhua Ma Lijun Lu School of Biomedical Engineering Southern Medical University Guangzhou China Guangdong Provincial Key Laboratory of Medical Image Processing and the Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology Southern Medical University Guangzhou China Pazhou Lab Guangzhou China

In this work, we proposed a bidirectional attention-aware fluid pyramid features integrated fusion network (BAF-Net) with cross-modal interactions for multi-modality medical images prognosis. The network is composed of two identical branches to preserve the unimodal feature and one paralleled bidirectional attention-aware distillation stream to assimilate cross-modal complements progressively and learn supplementary refined features in both the bottom-up and the top-down processes. The fluid pyramid connections were adopted to integrate the hierarchical features at different levels of deep neural network, and channel-wise attention modules were exploited to mitigate the cross-modal cross-level incompatibility. Furthermore, the depth-wise separable convolution was introduced to fuse the cross-modal cross-level features to alleviate the increase of parameters to a great extent. Better performance was obtained by the proposed BAF-Net compared to unimodal network for cancer prognosis in a public TCIA dataset (head & neck cancer (HNC) dataset composed of 800 patients from nine centers).

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