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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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Efficient spatial temporal convolutional features for audiovisual continuous affect recognition 9

Efficient spatial temporal convolutional features for audiov...

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9th International Audio/Visual Emotion Challenge and Workshop, AVEC 2019, held in conjunction with the ACM Multimedia 2019

作者： Chen, Haifeng Deng, Yifan Cheng, Shiwen Wang, Yixuan Jiang, Dongmei Sahli, Hichem Shaanxi Key Lab on Speech and Image Information Processing Xi'an China Peng Cheng Laboratory Shenzhen China Belgium

ISBN: (纸本)9781450369138

Affective dimension prediction from multi-modal is becoming an increasingly attractive research field in artificial intelligence (AI) and human-computer interaction (HCI) . Previous works have shown that discriminative features from multiple modalities are of importance to accurately recognize emotional states. Recently, deep representations have proved to be effective for emotional state recognition. To investigate new deep spatial-temporal features and evaluate their effectiveness for affective dimension recognition, in this paper, we propose: (1) combining a pre-trained 2D-CNN and a 1D-CNN for learning deep spatial-temporal features from video images and audio spectrograms;and (2) a spatial-Temporal Graph Convolutional Networks (ST-GCN) adapted to facial landmarks graph. To evaluate the effectiveness of the proposed spatialtemporal features for affective dimension prediction, we propose Deep Bidirectional Long Short-Term Memory Networks (DBLSTM) model for single-modality prediction, early-fusion and late-fusion predictions. With respect to the liking dimension, we use the text modality for prediction. Experimental results, on the AVEC2019 CES dataset, show that our proposed spatial-temporal features and recognition model obtain promising results. On the development set, the obtained concordance correlation coefficient (CCC) is up to 0.724 for arousal and 0.705 for valence, and on the test set, the CCC is 0.513 for arousal and 0.515 for valence, which outperform the baseline system with corresponding CCC of 0.355 and 0.468 on arousal and valence, respectively. © 2019 Association for Computing Machinery.

关键词： Forecasting

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Slimming Convolutional Neural Network Based on Attention Mechanism for Pavement Crack Detection 11

Slimming Convolutional Neural Network Based on Attention Mec...

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11th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2021

作者： Huang, Wenning Zhu, Guijie Fan, Zhun Li, Wenji Rong, Yibiao Cai, Yuwei Key Lab of Digital Signal and Image Processing of Guangdong Province College of Engineering Shantou University Shantou515063 China

ISBN: (纸本)9781665425278

In recent years, convolutional neural networks (CNNs) have achieved excellent performance in pavement crack segmentation tasks. However, CNNs usually need high computational cost and large storage. As a result, CNN slimming is important for embedded systems with limited resources. Inspired by the channel attention techniques in CNNs, we propose an effective method to slim CNNs based on attention mechanism. The proposed method first removes the unimportant filters and their connecting feature maps according to their attention scales. The retraining process is then performed on the slimmed model to regain performance. The proposed pruning method is easily embedded in common convolutional layers without any dedicated software libraries. Comprehensive experiments show that the proposed method can prune over 40% parameters of CNNs applied on pavement crack segmentation tasks while the deterioration in performance is slight compared with the original UNet architecture. © 2021 IEEE.

关键词： Convolutional neural networks

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Real-time target tracking with particle filter in moving monocular camera

Real-time target tracking with particle filter in moving mon...

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5th International Symposium on Multispectral image processing and Pattern Recognition

作者： Liu, Guocheng Wang, Yongji Huazhong Univ Sci & Technol Key Lab Image Processing & Intelligent Control Chinese Educ Minist Wuhan 430072 Peoples R China

ISBN: (纸本)9780819469502

In this paper, we propose an improved particle filter algorithm for real-time tracking a randomly moving target in dynamic environment with a moving monocular camera. For making the tracking task robustly and effectively, color histogram based target model is integrated into particle filter algorithm. Bhattacharyya distance is used to weight samples by calculating each sample's histogram with a specified target model and it makes the measurement matching and samples' weight updating more reasonable. In order to reduce sample depletion, the improved algorithm will be able to take the latest observation into account. The experimental results confirm that the method is effective even when the monocular camera is moving and the target object is partially occluded in a clutter background.

关键词： visual tracking particle filter state-space model

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A novel ant colony based cross-layer QoS routing algorithm for wireless mesh networks

A novel ant colony based cross-layer QoS routing algorithm f...

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2008 11th IEEE International Conference on Communication Technology, ICCT 2008

作者： Hui, Zhang Yu-Ning, Dong Long-Xiang, Yang Hong-Bo, Zhu Jiangsu Provincial Key Lab. of Wireless Communications College of Communication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing 210003 China Jiangsu Provincial Key Lab. of Image Processing and Image Communication College of Communication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing 210003 China

ISBN: (纸本)9781424422517

According to the heterogeneous characteristics of wireless mesh networks and the QoS requirements of multimedia applications, we design a novel QoS adaptive architecture for WMNs that is cross-domain, cross-layer and cross-node. Furthermore, this architecture is mathematically described by using a bi-level programming model. This model uses the optimization results of subnets as the optimization parameters or optimization constraints of backbone networks, and combines the features of different network domains. Based on this bi-level programming model, we propose an ant colony optimization based routing algorithm. Simulation results show that the bi-level programming model, which fully optimizes various QoS parameters of paths, can effectively adapt to the heterogeneous characteristics of WMNs so as to obtain the optimal path;the proposed routing algorithm, which has both advantages of low complexity and fast convergence speed, can converge to the theoretical optimal solution of bi-level programming model. © 2008 IEEE.

关键词： Routing algorithms

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Hybrid immune algorithm with Lamarckian local search for multi-objective optimization

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Memetic Computing 2010年第1期2卷 47-67页

作者： Gong, Maoguo Liu, Chao Jiao, Licheng Cheng, Gang Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University 710071 Xi'an China

Lamarckian learning has been introduced into evolutionary computation as local search mechanism. The relevant research topic, memetic computation, has received significant amount of interests. In this study, a novel Lamarckian learning strategy is designed for improving the Nondominated Neighbor Immune Algorithm, a novel hybrid multi-objective optimization algorithm, Multi-objective Lamarckian Immune Algorithm (MLIA), is proposed. The Lamarckian learning performs a greedy search which proceeds towards the goal along the direction obtained by Tchebycheff approach and generates the improved progenies or improved decision vectors, so single individual will be optimized locally and the newcomers yield an enhanced exploitation around the nondominated individuals in less-crowded regions of the current trade-off front. Simulation results based on twelve benchmark problems show that MLIA outperforms the original immune algorithm and NSGA-II in approximating Pareto-optimal front in most of the test problems. When compared with the state of the art algorithm MOEA/D, MLIA shows better performance in terms of the coverage of two sets metric, although it is laggard in the hypervolume metric. © 2009 Springer-Verlag.

关键词： Artificial immune system Lamarckian learning Memetic algorithm Multi-objective optimization

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Edge region color autocorrelogram: A new low-level feature applied in CBIR

Edge region color autocorrelogram: A new low-level feature a...

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2010 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB 2010

作者： Chen, Xiaolin Yang, Xiaokang Zhang, Rui Liu, Anwen Zheng, Shibao Institute of Image Communication and Information Processing Shanghai Key Lab. of Digital Media Processing and Transmission Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781424444625

Simple yet effective feature extraction is crucial for content- based image retrieval (CBIR). In this paper we propose a novel type of regional feature, which is called edge region color autocorrelogram (ERCAC). It aims to combine the color and shape characteristics of image jointly, by capturing both color distribution of image and spatial correlation of edge points with a structure-based early fusion. Hence, both color information and sketch information are encoded into a single representation. Experimental results show that our method has better performance on the task of TRECVID 2005 concept detection.

关键词： Color

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Distributed Equally Surrounding Control for Multiple Unmanned Surface Vessels 41

Distributed Equally Surrounding Control for Multiple Unmanne...

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第41届中国控制会议

作者： Fu-Long Hu Chuan-Cong Tang Bin-Bin Hu Bowen Xu Hai-Tao Zhang School of Artificial Intelligence and Automation the Key Laboratory of Image Processing and Intelligent Controland the State Key Lab of Digital Manufacturing Equipment and TechnologyHuazhong University of Science and Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper proposes a distributed controller to equally surround a static target with multiple unmanned surface vessels(USVs).By utilizing the mutually repulsive method,the proposed controller addresses the equally surrounding problem,taking into account the influence of time-varying topologies,and the total number USVs which is a 'global information is not used in the *** asymptotic convergence of the closed-loop multi-USV system is guaranteed with rigorous ***,the effectiveness of the controller is verified by numerical simulations.

关键词： Equally surrounding unmanned surface vessels(USVs) multi-agent system

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A robust homography estimation method based on keypoint consensus and appearance similarity

A robust homography estimation method based on keypoint cons...

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2012 13th IEEE International Conference on Multimedia and Expo, ICME 2012

作者： Yan, Qing Xu, Yi Yang, Xiaokang Institute of Image Communication and Information Processing Shanghai Key Lab. of Digital Media Processing and Transmition Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781467316590

In this paper, a robust homography estimation method is proposed to match multiview images in the uncalibrated case. This method formulates a new loss function to verify homography hypothesis, which combines models of key-point consensus and appearance similarity. In the consensus model, Lap lace distribution is exploited to better characterize the imprecision of key points. And in the appearance model, a truncated exponential function is utilized to represent the distribution of image similarity values. With these improvements, our method can be more robust in complex situations when a rather high percentage of key points are ambiguous and unreliable, and can output a more accurate homography that satisfies most pixels' geometric relationship. The experimenttal results highlight the robustness and accuracy of our method in the matching tasks of both synthetic images and real life photos. © 2012 IEEE.

关键词： Exponential functions

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Robust blind watermark algorithm of color image based on neural network

Robust blind watermark algorithm of color image based on neu...

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2008 IEEE International Conference Neural Networks and Signal processing, ICNNSP

作者： Xuelong, Hu Xu, Lian Lin, Chen Yongai, Zheng School of Information Engineering Yangzhou University Yangzhou Jiangsu 225009 China Jiangsu Province Key Lab. for Image Processing and Image Communication Nanjing University of Posts and Communications Nanjing Jiangsu 210003 China Shanghai University of Electrical Power Shanghai 200090 China

ISBN: (纸本)9781424423118

A new algorithm of color image blind watermarking based on BP neural network and wavelet significant tree is proposed. In YCbCr, the luminance component is decomposed with wavelet, and the wavelet significant tree can be found. Then the correlativity among nodes of wavelet significant tree is described by the nonlinear relationship responded by the trained BP neural network. Based on the relationship, the watermark is embedded into wavelet significant trees. Finally, the embedded watermark can be extracted without original image. The experimental results show that the embedded watermark can be extracted preferably in some common attacks, and this algorithm has better transparency and stronger robustness with some comparisons to other algorithms. © 2008 IEEE.

关键词： Neural networks

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