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International Conference on Affective Computing and Intelligent Interaction

作者： Meshia Cedric Oveneke Isabel Gonzalez Weiyi Wang Dongmei Jiang Hichem Sahli VUB-NPU Joint AVSP Research Lab Vrije Universiteit Brussel (VUB) Deptartment of Electronics & Informatics (ETRO) VUB-NPU Joint AVSP Research Lab Northwestern Polytechnical University (NPU) Shaanxi Key Lab on Speech and Image Information Processing

ISBN: (纸本)9781479999545

Understanding social signals is a very important aspect of human communication and interaction and has therefore attracted increased attention from various research areas. Among the different types of social signals, particular attention has been paid to facial expression of emotions and its automated analysis from image sequences. Automated facial expression analysis is a very challenging task due to the complex three-dimensional deformation and motion of the face associated to the facial expressions and the loss of 3D information during the image formation process. As a consequence, retrieving 3D spatio-temporal facial information from image sequences is essential for automated facial expression analysis. In this paper, we propose a framework for retrieving three-dimensional facial structure, motion and spatio-temporal features from monocular image sequences. First, we estimate monocular 3D scene flow by retrieving the facial structure using shape-from-shading (SFS) and combine it with 2D optical flow. Secondly, based on the retrieved structure and motion of the face, we extract spatio-temporal features for automated facial expression analysis. Experimental results illustrate the potential of the proposed 3D facial information retrieval framework for facial expression analysis, i.e. facial expression recognition and facial action-unit recognition on a benchmark dataset. This paves the way for future research on monocular 3D facial expression analysis.

关键词： EXPRESSION ANALYSIS facial information Personal Communication Face Facial Expression image sequences information retrieval Face Automation formation process

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Investigating the stability of fast iterative shrinkage thresholding algorithm for MR imaging reconstruction using compressed sensing

Investigating the stability of fast iterative shrinkage thre...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Guishan Zhang Haitao Deng Yaowen Chen Zhiwei Shen Renhua Wu Key Lab. of Digital Signal and Image Processing of Guangdong Province Shantou University Shantou China Department of Medical Imaging 2nd Affiliated Hospital Medical College of Shantou University Shantou China

ISBN: (纸本)9781467376839

Compressed sensing (CS) has been applied to the field of sub-sampled magnetic resonance imaging (MRI) reconstruction (CS-MRI). Fast iterative shrinkage-thresholding algorithm (FISTA) is an effective method for CS-MR images reconstruction. To investigate the accuracy and efficiency of the proposed algorithm, we applied it to the under-sampling MR images gained by different MRI scanning sequences. We found the peak signal to noise ratio (PSNR) of reconstructed MRI with varying sampling ratios diminished from Axial T1 weighted images (Ax T1) (45.13±12.97 dB), Axial T2 weighted images (Ax T2) (42.8 ± 14.95 dB), FLAIR (41.74 ± 14.15 dB), Diffusion Weighted Imaging (DWI) (40.23 ±17.40 dB) and Sagittal T2 weighted images (Sag T2) (36.28±12.32 dB), but there was no significant difference among the groups. In addition, the changes to the elapsed time of them was minor, Ax T1 (1.09±0.13 s), Ax T2 (1.30±0.13s), FLAIR (1.02±0.12s), DWI (1.07±0.13s) and Sag T2 (1.12±0.07s). Our results confirmed the stability of the proposed fast MRI reconstruction method for different scanning sequences. Further efforts are still needed to design the clinical sequence with sub-sampled acquisition strategy which may be a developing technique with clinical value.

关键词： Compressed Sensing Magnetic Resonance Imaging PSNR Fast Iterative Shrinkage-thresholding Algorithm (FISTA)

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Indoor Localization with Incomplete Observation Using Set-membership Filter

Indoor Localization with Incomplete Observation Using Set-me...

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International Symposium on Micro-NanoMechatronics and Human Science

作者： Yuan Wang Jian Huang Key Lab of Image Processing and Intelligent Control School of Automation Huazhong University of Science and Technology

ISBN: (纸本)9781467382182

Indoor localization is a popular topic because of the poor performance of GPS in the indoor environment. This paper has provided a new method to achieve the goal of indoor localization. Combining the usage of posture sensors with the Radio Frequency Identification (RFID) technology, we solved the problem of plentiful tags in the traditional RFID indoor localization. The relative localization subsystem is based on posture sensors and the absolute localization subsystem is based on RFID. A set-membership theory based data fusion algorithm is proposed to fuse the data of relative localization and absolute localization. Experimental results show that the proposed data fusion algorithm could significantly reduce the accumulated error of our indoor localization system.

关键词： Ellipsoids Indoor environments RFID tags Filtering theory Filtering algorithms

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Multimodal depression recognition with dynamic visual and audio cues

Multimodal depression recognition with dynamic visual and au...

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

作者： Lang He Dongmei Jiang Hichem Sahli NPU-VUB Joint AVSP Research lab Shaanxi Key Lab on Speech and Image Information Processing Xi'an China Deptartment of Electronics & Informatics(ETRO) Vrije Universiteit Brussel(VUB) Brussels Belgium Interuniversity Microelectronics Centre(IMEC) Heverlee Belgium

In this paper, we present our system design for audio visual multi-modal depression recognition. To improve the estimation accuracy of the Beck Depression Inventory (BDI) score, besides the Low Level Descriptors (LLD) features and the Local Gabor Binary Pattern-Three Orthogonal Planes (LGBP-TOP) features provided by the 2014 Audio/Visual Emotion Challenge and Workshop (AVEC2014), we extract extra features to capture key behavioural changes associated with depression. From audio we extract the speaking rate, and from video, the head pose features, the Space-Temporal Interesting Point (STIP) features, and local kinematic features via the Divergence-Curl-Shear descriptors. These features describe body movements, and spatio-temporal changes within the image sequence. We also consider global dynamic features, obtained using motion history histogram (MHH), bag of words (BOW) features and vector of local aggregated descriptors (VLAD). To capture the complementary information within the used features, we evaluate two fusion systems - the feature fusion scheme, and the model fusion scheme via local linear regression (LLR). Experiments are carried out on the training set and development set of the Depression Recognition Sub-Challenge (DSC) of AVEC2014, we obtain root mean square error (RMSE) of 7.6697, and mean absolute error (MAE) of 6.1683 on the development set, which are better or comparable with the state of the art results of the AVEC2014 challenge.

关键词： Feature extraction Visualization Speech Head Histograms Optical imaging History

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Alpha wave attention EEG analysis based on the multiscale Jensen-Shannon divergence

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2nd International Conference on Materials Science and Mechanical Engineering, ICMSME 2014

作者： Zhang, Zheng Xia Xu, Si Qiu Zhou, Er Ning Huang, Xiao Lin Wang, Jun Image Processing and Image Communications Key Lab College of Geo and Bio Information Nanjing Univ of Posts and Telecomm Nanjing 210003 China Institute of Biomedical Electronic Engineering School of Electronic Science and Engineering Nanjing University Nanjing 210093 China

ISBN: (纸本)9783038351689

The article adopted the multiscale Jensen - Shannon Divergence method for EEG complexity analysis. Then the study found that the method can distinguish between three different status (eyes closed, count, in a daze) acquisition of Alpha EEG time series, which shows three different states of Alpha EEG time series having significant differences. In each state of the three different states (eyes closed, count, in a daze), we aimed at comparing and analyzing the statistical complexity of EEG time series itself and the statistical complexity of EEG time series proxy data, finding that there are large amounts of nonlinear time series in the Alpha EEG signals. This method is also fully proved that the multiscale JSD algorithm can be used to analyze EEG signals. Attention statistical complexity can be used as a measure of brain function parameter, which can be applied to the auxiliary clinical brain function evaluation in the future. © (2014) Trans Tech Publications, Switzerland.

关键词： Time series

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Research on attention eeg based on the multiscale jensen-shannon divergence

Research on attention eeg based on the multiscale jensen-sha...

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3rd International Conference on Mechanical Automation and Materials Engineering, ICMAME 2014

ISBN: (纸本)9783038350941

The article adopted the multiscale Jensen-Shannon Divergence analysis method for EEG complexity analysis. Then the study found that this method can distinguish between three different status (Eyes closed, count, in a daze) acquisition of EEG time series. It showed that three different states of EEG time series have significant differences. In each state of the three different states (Eyes closed, count, in a daze), we aimed at comparing and analyzing the statistical complexity of EEG time series itself and the statistical complexity of EEG time series shuffled data. It was found that there are large amounts of nonlinear time series in the EEG signals. This method is also fully proved that the multiscale JSD algorithm can be used to analyze attention EEG signals. The multiscale Jensen-Shannon Divergence statistical complexity can be used as a measure of brain function parameter, which can be applied to the auxiliary clinical brain function evaluation in the future. © (2014) Trans Tech Publications, Switzerland.

关键词： Time series

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Multiscale jensen-shannon divergence based analysis of beta wave attention EEG

Multiscale jensen-shannon divergence based analysis of beta ...

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2014 International Conference on Mechanics and Mechatronics, ICMM 2014

ISBN: (纸本)9783038351627

The article adopted the multiscale Jensen-Shannon Divergence analysis method for EEG complexity analysis, then the study found that this method can distinguish between three different status (Eyes closed, count, in a daze) acquisition of Beta EEG time series, shows three different states of Beta EEG time series have significant differences. In each state of the three different states (Eyes closed, count, in a daze), we are aimed at comparing and analyzing the statistical complexity of EEG time series itself and the statistical complexity of EEG time series shuffled data, finding that there are large amounts of nonlinear time series in the Beta EEG signals. This method is also fully proved that the multi-scale JSD algorithm can be used to analyze EEG signals, attention statistical complexity can be used as a measure of brain function parameter, which can be applied to the auxiliary clinical brain function evaluation in the future. © (2014) Trans Tech Publications, Switzerland.

关键词： Time series

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3D Emotional Facial Animation Synthesis with Factored Conditional Restricted Boltzmann Machines

3D Emotional Facial Animation Synthesis with Factored Condit...

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International Conference on Affective Computing and Intelligent Interaction

作者： Yong Zhao Dongmei Jiang Hichem Sahli NPU-VUB Joint AVSP Research Lab School of Computer Science Northwestern Polytechnical University (NPU) Shaanxi Key Lab on Speech and Image Information Processing NPU-VUB Joint AVSP Research Lab Vrije Universitiet Brussel (VUB) Department of Electronics & Informatics (ETRO)

ISBN: (纸本)9781479999545

This paper presents a 3D emotional facial animation synthesis approach based on the Factored Conditional Restricted Boltzmann Machines (FCRBM). Facial Action Parameters (FAPs) extracted from 2D face image sequences, are adopted to train the FCRBM model parameters. Based on the trained model, given an emotion label sequence and several initial frames of FAPs, the corresponding FAP sequence is generated via the Gibbs sampling, and then used to construct the MPEG-4 compliant 3D facial animation. Emotion recognition and subjective evaluation on the synthesized animations show that the proposed method can obtain natural facial animations representing well the dynamic process of emotions. Besides, facial animation with smooth emotion transitions can be obtained by blending the emotion labels.

关键词： Facial expression synthesis FCRBM FAP

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Compressive tracking moving cells in time-lapse image sequences

Compressive tracking moving cells in time-lapse image sequen...

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IEEE International Conference on Orange Technologies (ICOT)

作者： Chen Ding Ying Li Yongsheng Pan Tao Zhou Pengcheng Gao Yong Xia Center for Multidisciplinary Convergence Computing (CMCC) Northwestern Polytechnical University Xi'an China Shaanxi Key Lab of Speech & Image Information Processing (SAIIP) Northwestern Polytechnical University Xi'an China School of Science Ningxia Medical University Yinchuan China

ISBN: (纸本)9781467382380

Tracking the motion of cells in time-lapse image sequences plays a pivotal role in both research settings and clinical practices. In spite of their prevalence, automated cell tracking approaches are still facing several major challenges, including the effectiveness of cell detection, accuracy of tracking and high computational complexity. In this paper, we propose a segmentation-based compressive tracking (SBCT) algorithm for moving cells. This algorithm consists three major steps, including detecting the bounding box of each cell, extracting image features in each bounding box using compressive sensing, and identifying the correspondence between cells in adjacent frames using a trained naive Bayes classifier. The proposed SBCT algorithm has been evaluated against seven state-of-the-art cell tracking approaches on two time-lapse images sequences provided by the 2014 cell tracking challenge. Our results suggest that the proposed algorithm can successfully tracking moving cells with relatively high accuracy and low computational complexity.

关键词： Tracking image segmentation Feature extraction image sequences Classification algorithms Compressed sensing Shape

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image super-resolution based on sparse representation and nonlocal regularization

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Journal of Computational Information Systems 2014年第5期10卷 2107-2116页

作者： Li, Xin Zhu, Xiuchang Jiangsu Province Key Lab. on Image Processing and Image Communication Nanjing University of Posts and Telecommunications Nanjing 210003 China

To solve the super-resolution reconstruction problem for single-frame image, an algorithm based on sparse representation and nonlocal regularization is proposed. By training the joint dictionaries, this algorithm looks for sparse representation in low-resolution dictionary for the input low-resolution image patch, and maps the sparse representation to high-resolution dictionary to construct high-resolution image patch. Then the reconstructed image patch is further regularized by the image's nonlocal self-similarity a priori information. Experimental results show that this algorithm can effectively ensure the quality of the reconstructed image, and greatly reduce the computation time. © 2014 Binary Information Press.

关键词： image reconstruction

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