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作者： Yan, Jingjie Lu, Guanming Bai, Xiaodong Li, Haibo Sun, Ning Liang, Ruiyu Jiangsu Provincial Key Laboratory of Image Processing and Image Communication College of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China Engineering Research Center of Wide Band Wireless Communication Technology Nanjing University of Posts and Telecommunications Nanjing211167 China School of Communication Engineering Nanjing Institute of Technology Nanjing211167 China

In this letter, we propose a supervised bimodal emotion recognition approach based on two important human emotion modalities including facial expression and body gesture. A effectively supervised feature fusion algorithms named supervised multiset canonical correlation analysis (SMCCA) is presented to established the linear connection between three sets of matrices, which contain the feature matrix of two modalities and their concurrent category matrix. The test results in the bimodal emotion recognition of the FABO database show that the SMCCA algorithm can get better or considerable efficiency than unsupervised feature fusion algorithm covering canonical correlation analysis (CCA), sparse canonical correlation analysis (SCCA), multiset canonical correlation analysis (MCCA) and so on. © 2018 The Institute of Electronics, Information and communication Engineers.

关键词： Emotion Recognition

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Spatial Error Concealment Based on Judgement on Edge Information

Spatial Error Concealment Based on Judgement on Edge Informa...

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2008 International Conference on Audio，Language and image processing(2008国际声音、语言、图像过程大会)

作者： XueFeng Zhan XiuChang Zhu Nanjing Univ.of Posts & Telecomm Image Processing and Image Communication Key Lab Nanjing 210003

An efficient spatial error concealment algorithm is proposed for corrupted image by transmission losses in this work. Firstly, information of edges in the neighboring blocks is extracted by judgements on which edges may traverse the loss block. Then, all detected edges are divided into relevant and irrelevant edge. Subsequently, one or more directions for interpolation can be selected from the relevant edge set by minimizing the boundary pixel difference for each direction. The method improves the capability of recovering more high-detailed contents of lost block with low complexity. Experimental results demonstrate the better performance of the proposed methods as compared to previous techniques.

关键词： image edge detection Pixel Interpolation image reconstruction PSNR image restoration Sun

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image demosaicing by non-local similarity and local correlation

Image demosaicing by non-local similarity and local correlat...

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International Conference on Signal processing Proceedings (ICSP)

作者： Guo-gang Wang Xiu-chang Zhu Zong-liang Gan Province Key Lab on Image Processing and Image Communication Nanjing University of Posts and Telecommunications Nanjing China

Conventional single-chip digital cameras use color filter arrays(CFA) to sample different spectral components. image demosaicing is a problem of interpolating these data to complete red, green, and blue values for each image pixel, to produce an RGB image. Many color demosaicing(CDM) methods assume that the high local spatial redundancy exists among the color samples. Such an assumption, however, may be fail for images with high color saturation and sharp color transitions. This paper presents an adaptive demosaicing algorithm by exploiting both the non-local similarity and the local correlation(NLS-LC) in the color filter array image. First, the most flattest nonlocal image patches are searched in the searching window centered on the estimated pixel. Second, the patch, which is the most similar to the current patch, is selected among the most smoothest nonlocal patches. Third, according to the similar degree and the local correlation degree, the obtained nonlocal image patch and the current patch are adaptively chosen to estimate the missing color samples. Experimental results indicate that the proposed method exhibits superior performance over many state-of-the-art color interpolation methods.

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Adaptive dictionary learning for distributed compressive video sensing

International Journal of Digital Content Technology and its ...

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International Journal of Digital Content Technology and its Applications 2012年第4期6卷 141-149页

作者： Wu, Minghu Zhu, Xiuchang Gan, Zongliang Li, Xin Jiangsu Province Key Lab on Image Processing and Image Communication Nanjing University of Posts and Telecommunications Nanjing China School of Electrical and Electronic Enginesering Hubei University of Technology Wuhan China

Compressive sensing (CS) is a new technique for data sampling and compression simultaneously. In this paper, we propose a novel distributed video coding algorithm with dynamic measurement rate allocation based on compressive sensing principles, where almost all computation burdens can be shifted to the decoder, resulting in a very low-complexity encoder. So the proposed algorithm can be useful in those video applications that require very low complex encoders. At the decoder, the compressed video can be efficiently reconstructed with adaptive dictionary learning. The simulation results show that the proposed algorithm outperforms the distributed compressive video sensing with non-adaptive learning local dictionary and global dictionary.

关键词： Compressed sensing

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A Novel Representation for Video-based Person Reidentification with Attribute-constraints

A Novel Representation for Video-based Person Reidentificati...

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International Conference on Signal processing Proceedings (ICSP)

作者： Wanru Song Jieying Zheng Yahong Wu Qingqing Zhao Changhong Chen Feng Liu Jiangsu Key Lab of Image Processing and Image Communications Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781538646748;9781538646731

Person re-identification is an important task in the field of intelligent video surveillance, which has become one of the research focus spots in the field of computer vision. Video-based person re-identification aims to verify a pedestrian identity of the video sequences which captured from non-overlapping cameras at different time. In this paper, we propose a novel feature extractor based on LSTM networks. These LSTM networks are used to extract the effective space-time feature representation named the attribute-constraints space-time feature (ASTF). Different from other methods, we manually annotate pedestrians in videos with three attributes. In the meantime, the attributes with the IDs of pedestrians are regarded as labels to train the feature extractor. The ASTF representation for a testing video is extracted by this feature extractor, which is an effective space-time feature representation for video-based re-identification. Extensive experiments on two public datasets demonstrate that our approach outperforms the state-of-the-art video-based re-identification methods.

关键词： Feature extraction Task analysis Training Cameras Testing Measurement Video sequences

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image fusion algorithm based on features motivated multi-channel pulse coupled neural networks

Image fusion algorithm based on features motivated multi-cha...

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2nd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2008

作者： Qu, Xiaobo Yan, Jingwen Dept. of Communication Engineering Xiamen University Fujian 361005 China Key Lab. of Digital Signal and Image Processing of Guangdong Province Shantou University Guangdong 515063 China

ISBN: (纸本)9781424417483

Pulse coupled neural networks (PCNN) is a mammal visual cortex-inspired artificial neural networks. Owing to the coupling links in neurons, PCNN is successful to utilize the local information, thus it has been successfully employed in image fusion. However, in traditional PCNN for image fusion, value of per pixel is used to motivate per neuron. In this paper, image feature of per pixel, e.g. gradient and local energy, is used to motivate per neuron and generate firing maps. Each firing map is corresponding to one type feature. Furthermore, a new multichannel PCNN is presented to combine these firing maps via a weighting function which measures the contribution of these features to the fused image quality. Finally, pixels with maximum firing times, when firing times of source images are compared, are selected as the pixels of the fused image. Experimental results demonstrate that the proposed algorithm outperforms Waveletbased and Wavelet-PCNN-based fusion algorithms. © 2008 IEEE.

关键词： image fusion

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Hydrodynamics-Informed Neural Network for Simulating Dense Crowd Motion Patterns 24

Hydrodynamics-Informed Neural Network for Simulating Dense C...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhou, Yanshan Lai, Pingrui Yu, Jiaqi Xiong, Yingjie Yang, Hua Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Lab of Digital Media Processing and Transmission Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798400706868

With global occurrences of crowd crushes and stampedes, dense crowd simulation has been drawing great attention. In this research, our goal is to simulate dense crowd motions under six classic motion patterns, more specifically, to generate subsequent motions of dense crowds from the given initial states. Since dense crowds share similarities with fluids, such as continuity and fluidity, one common approach for dense crowd simulation is to construct hydrodynamics-based models, which consider dense crowds as fluids, guide crowd motions with Navier-Stokes equations, and conduct dense crowd simulation by solving governing equations. Despite the proposal of these models, dense crowd simulation faces multiple challenges, including the difficulty of directly solving Navier-Stokes equations due to their nonlinear nature, the ignorance of distinctive crowd characteristics which fluids lack, and the gaps in the evaluation and validation of crowd simulation models. To address the above challenges, we build a hydrodynamic model, which captures the crowd physical properties (continuity, fluidity, etc.) with Navier-Stokes equations and reflects the crowd social properties (sociality, personality, etc.) with operators that describe crowd interactions and crowd-environment interactions. To tackle the computational problem, we propose to solve the governing equation based on Navier-Stokes equations using neural networks, and introduce the Hydrodynamics-Informed Neural Network (HINN) which preserves the structure of the governing equation in its network architecture. To facilitate the evaluation, we construct a new dense crowd motion video dataset called Dense Crowd Flow Dataset (DCFD), containing six classic motion patterns (line, curve, circle, cross, cluster and scatter) and 457 video clips, which can serve as the groundtruths for various objective metrics. Numerous experiments are conducted using HINN to simulate dense crowd motions under six motion patterns with video clips fro

关键词： Navier Stokes equations

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A NOVEL CROSS-LAYER STRATEGY OF VIDEO TRANSMISSION TO SOLVE PERFORMANCE ANOMALY IN MULTI-RATE IEEE 802.11 NETWORKS

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Journal of Electronics(China) 2011年第3期28卷 349-358页

作者： Tang Guijin Zhu Xiuchang Key Lab of Image Processing ＆ Communication Nanjing University of Posts and Telecommunications Nanjing 210003 China

When wireless hosts use different rates to transmit data in IEEE 802.11 networks, it will take on the state of performance anomaly which will severely decrease the throughputs of all the higher rate hosts. Hence, it is bad for video service transmission. Considering that video is very sensitive to packet delivery delay but can tolerate some packet losses, we propose a novel cross-layer scheme which takes these two characteristics into consideration. Firstly, the maximum number of retransmissions for a video Medium Access Control (MAC) frame is computed in MAC layer according to video frame rate requirement of application layer and current access delay of MAC layer. Secondly, within the margin of the tolerant Packet Loss Rate (PLR) of application layer, several video MAC frames are allowed to drop so that we can adaptively select the transmission rate as high as possible for the rest of video MAC frames in terms of current channel quality and the maximum number of retransmissions. Experiment results show that the proposed method can reduce the delay and jitter of video service and improve the throughputs of fast hosts. Therefore, it increases the quality of reconstructed video to a certain extent and relieves the performance anomaly of network effectively.

关键词： Multi-rate Video transmission IEEE 802.11 Performance anomaly Cross-layer

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Neonatal Pain Expression Recognition Based on LBP Features and Sparse Representation

Neonatal Pain Expression Recognition Based on LBP Features a...

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2015 World Conference on Control, Electronics and Electrical Engineering(WCEE 2015)

作者： Guanming Lu Mengying Chen Xiaonan Li Jiangsu Province Key Lab on Image Processing & Image Communication Nanjing University of Posts and Telecommunications Nanjing Children's Hospital Affiliated to Nanjing Medical University

Facial expressions are considered a reliable indicator in neonatal pain *** paper proposes a new neonatal pain expression recognition method,which utilizes the feature descriptors based on weighted Local Binary Pattern(LBP)and the classifier based on sparse ***,the normalized facial image is described using a feature vector,which is histogram sequence obtained by concatenating the weighted histograms of the LBP maps of all the local ***,the Principal Component Analysis(PCA)method is used to reduce the dimension of the feature ***,the classifier based on sparse representation is applied to classify test sample into four classes of facial expressions:calm,crying,moderate pain,severe *** objective of this study is to assist the clinicians in assessing neonatal pain by utilizing computer-based image analysis *** experimental results on neonate facial image database show the effectiveness of the proposed *** classification accuracy is up to 85.50%.

关键词： expression recognition,local binary pattern,sparse representation,neonate,pain expression.

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PRN: Psychology-Inspired Relation Network for Detecting Social Interaction Groups from Single images 32

PRN: Psychology-Inspired Relation Network for Detecting Soci...

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32nd British Machine Vision Conference, BMVC 2021

作者： Yu, Jiaqi Yang, Jinhai Yang, Hua Zhai, Guangtao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University ChinaShanghai Key Lab of Digital Media Processing and Transmission Shanghai ChinaMoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China

Detecting interaction groups is an essential task for understanding human behaviours and social activities. However, it is still challenging to identify social interactions and the resulting crowd groups using purely visual cues, especially from single images. Prior works either require additional statistics, such as interpersonal angles and kinaesthetic information, or simply deduce the group memberships with the similarity of individual actions. In this paper, we present the Psychology-inspired Relation Network (PRN) to comprehensively understand the static social scenes and effectively model the interaction relations between individuals. More concretely, stimulated by recent advances in social psychology, we first predict the keypoint heatmap from an image with the human bounding boxes as the visual representations of the key factors determining interaction groups: distance, orientation and postural openness. We then incorporate the personal and mutual influences together to compute the interaction strength matrix via self-attention, and finally utilise a perception to convert this matrix into dyadic interaction probability. Moreover, we devise two loss functions, the dyad loss to optimise the dyadic interaction probability and the group loss to enhance the distinguishability among different social groups. To evaluate the performance of PRN, we introduce a novel dataset containing various scenes with different crowd densities, by merging representative databases and relabeling the group labels. Our method achieves outstanding results on the proposed dataset. © 2021. The copyright of this document resides with its authors.

关键词： Computer vision

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