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2020 International Symposium on Automation, Information and Computing, ISAIC 2020

作者： Zhang, Ting Hu, Dong Zhang, Jing Jiangsu Province’s Key Lab of Image Procession and Image Communications Nanjing210003 China Education Ministry’s Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing210003 China Education Ministry’s Engineering Research Center of Ubiquitous Network and Heath Service Nanjing University of Posts and Telecommunications Nanjing210003 China

Target tracking is currently a hot research topic in Computer Vision and has a wide range of use in many research fields. However, due to factors such as occlusion, fast motion, blur and scale variation, tracking method still needs to be deeply studied. In this paper, we propose a block target tracking method based on multi-convolutional layer features and Kernel correlation filter. Our method divides the tracking process into two parts: target position estimation and target scale estimation. First, we block the target frame based on the condition number. Second, we extract the features by the convolutional layer and apply it to the kernel correlation filter to get the center position of different block targets. With the reliability of different blocks measured by the Barker coefficient, the overall target position center is obtained. Then, the affine transformation is adopted to achieve the scale adaptation. The algorithm in this paper is evaluated by the public video sequences in OTB-2013. Numerous experimental results demonstrate that the proposed tracking method can achieve target scale adaptation and effectively improve the tracking accuracy. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Target tracking

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An improved image dehazing algorithm based on dark channel prior

An improved image dehazing algorithm based on dark channel p...

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IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA)

作者： Jiajie Liu Jieying Zheng Ziguan Cui Guijin Tang Feng Liu Jiangsu Province Key Lab on Image Processing & Image Communications Nanjing University of Posts and Telecommunications Nanjing China Ministry of Education Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781479969906

image dehazing algorithm based on dark channel prior has been proved to be effective, but it cannot still guarantee accurate transmission. To solve this problem, we firstly propose a more reasonable estimation of atmospheric light, because bias in the atmospheric light estimation will cause an inaccurate transmission. Secondly, we improve the estimation of transmission in bright area so as to alleviate color distortion. After image recovery, we carry out denoising to improve the image quality. Finally, we use a blind image quality assessment method based on property of Human Visual System, and the experimental results show that this improved algorithm is more effective.

关键词： Mathematical model Equations image color analysis Atmospheric modeling Estimation image quality Noise reduction

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Joint DBF and SAO parallel filtering based on multithread load balancing

Joint DBF and SAO parallel filtering based on multithread lo...

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2020 International Symposium on Automation, Information and Computing, ISAIC 2020

作者： Ma, Hao Hu, Dong Li, Yi Jiangsu Province’s Key Lab of Image Procession and Image Communications Nanjing210003 China Education Ministry’s Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing210003 China Education Ministry’s Engineering Research Center of Ubiquitous Network and Heath Service Nanjing University of Posts and Telecommunications Nanjing210003 China

This paper presents a joint parallel loop filtering algorithm based on multi-thread load balancing in HEVC decoding, which implements the parallel processing of deblocking filtering (DBF) and sample adaptive compensation (SAO). Because of the diversity of video, the texture of different regions in an image is also different, which leads to various CTU partition methods. Therefore, the number of the boundary to be filtered is greatly different, resulting the computation load among multiple threads unbalanced in parallel processing. To solve this problem, an area division scheme is proposed, which divides the image into multiple areas, and the number of boundaries to be filtered in each area is similar. Then, the mapping relationship table is used to allocate these areas to multiple threads for parallel processing, so as to achieve the load balancing among the filtering threads. Finally, the cache technology is used to combine DBF and SAO to reduce the delay between them and improve the overall parallelism of the loop filter. Experimental results show that the performance of the proposed load balancing joint filtering algorithm is 8.15% higher than the previous scheme. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Textures

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Multi-class segmentation based on generalized fuzzy Gibbs random fields

Multi-class segmentation based on generalized fuzzy Gibbs ra...

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Proceedings: 2003 International Conference on image processing, ICIP-2003

作者： Lin, Yazhong Chen, Wufan Chan, Francis H.Y. Key Lab. for Med. Image Processing First Military Medical University Guangzhou China Department of Electrical Engineering University of Hong Kong Hong Kong Hong Kong

The model of Gibbs random fields is widely applied to Bayesian segmentation due to its best property of describing the spatial constraint information. However, the general segmentation methods, whose model is defined only on hard levels but not on fuzzy set, may come across a lot of difficulties, e.g., getting the unexpected results or even nothing, especially when the blurred or degraded images are considered In this paper, two multi-class approaches, based on the model of Piecewise Fuzzy Gibbs Random Fields (PFGRF) and that of Generalized Fuzzy Gibbs Random Fields (GFGRF) respectively, are presented to address these difficulties. In our experiments, both magnetic resonance image and simulated image are implemented with the two approaches mentioned above and the classical "hard" one. These three different results show that the approach of GFGRF is an efficient and unsupervised technique, which can automatically and optimally segment the images to be finer.

关键词： image segmentation

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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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Shape registration based on modified chain codes

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5th International Workshop on Advanced Parallel processing Technologies, APPT 2003

作者： Bilal, Muhammad Park, Jong-An Chang, Min Hyuk Shim, Young-Suk Choi, Tae-Sun Signal and Image Processing Lab Dept. of Mechatronics Kwangju Institute of Science and Technology Kwangju Korea Republic of Electronics Information and Communications Engineering Chosun University Kwangju Korea Republic of

ISBN: (纸本)3540200541

Shape descriptions based on the traditional chain codes are very susceptible to small perturbations in the contours of the objects. Therefore, direct matching of traditional chain codes could not be used for image retrieval based on the shape boundaries from the large databases. In this paper, a histogram based chain codes are proposed which could be used for image retrieval. The modified chain codes matching are invariant to translation, rotation and scaling transformations, and have high immunity to noise and small perturbations. © Springer-Verlag Berlin Heidelberg 2003.

关键词： image retrieval

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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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Extraction method of microscopic moving particle basing on multimodal background model

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ICIC Express Letters, Part B: Applications 2015年第12期6卷 3153-3158页

作者： Zhang, Yanbin Yang, Zhangzhen Zhang, Hui Qin, Lei Image Processing and Image Communication Lab Jiangsu Key Lab of Wireless Communications Nanjing University of Posts and Telecommunications No. 66 New Mofan Road Nanjing210003 China Jiangsu Key Lab of Wireless Communications Nanjing University of Posts and Telecommunications No. 66 New Mofan Road Nanjing210003 China

Target extraction is a key technology for image measurement of moving particles distributed in fluidic system. In this paper, we propose a novel moving particle extraction method based on multimodal characteristic of the image. For the proposed particle extraction method, we firstly analyze the origin of multimodal characteristic of image background in the microscopic observation system, and then we design multimodal background model for particle extraction. For improving the performance of the model, we exploit the moving principle in the micro channel, and discuss the setting of learning rate. The experimental results demonstrate that the proposed methods achieve good performance compared with several traditional methods. © 2015, ICIC Express Letters Office. All rights reserved.

关键词： Extraction

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Video smoke removal based on low-rank tensor completion via spatial-temporal continuity constraint

Video smoke removal based on low-rank tensor completion via ...

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作者： Zhu, Hu Xu, Guoxia Liu, Lu Deng, Lizhen Jiangsu Province Key Lab on Image Processing and Image Communication Nanjing University of Posts and Telecommunication Nanjing China Department of Computer Science Norwegian University of Science and Technology Gjovik Norway National Engineering Research Center of Communication and Network Technology Nanjing University of Posts and Telecommunication Nanjing China

Smoke has a very bad effect on the outdoor vision system. Not only are the videos with poor visual effects obtained, but also the quality and structure of the videos are reduced. In this paper, we propose a video smoke removal method based on low-rank tensor completion via spatial-temporal continuity constraint. The proposed method is based on the smoke mixing model and consider the sparseness of smoke and the global and local consistency of clean video. Then, the optimal solution of the smoke removal algorithm model is quickly realized by the Alternating Direction Method of Multiplier. Finally, we evaluate the experiment results of real-world data and simulated data from the visual effects and objective indicators. And the experiment results show that our proposed algorithm can achieve better smoke removal results. © 2021 John Wiley & Sons, Ltd.

关键词： Smoke

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Gravity gradient-terrain aided navigation based on particle filter

Gravity gradient-terrain aided navigation based on particle ...

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MIPPR 2009 - Remote Sensing and GIS Data processing and Other Applications: 6th International Symposium on Multispectral image processing and Pattern Recognition

作者： Xiong, Ling Ma, Jie Tian, Jin-Wen State Key Lab. for Multi-spectral Information Processing Technologies Key Lab. of Education Ministry for Image Processing and Intelligence Control Huazhong University of Science and Technology Wuhan 430074 China College of Information Science and Engineering Wuhan University of Science and Technology Wuhan 430081 China

ISBN: (纸本)9780819478092

Based on Particle Filter, Gravity Gradient-Terrain aided position technology is proposed in this paper. With the sensitivity of gravity gradient to terrain, the gravity gradient reference map can be computed from the local terrain elevation data. The position can be actualized through matching the real-time measured gravity gradient data to the prepared gravity gradient reference map. The most widely used approximate filtering method is the extended Kaman filter (EKF). EKF is computationally simple but, the convergence of the state estimation for the position is not guaranteed. Particle filter (PF) makes use of the non-linear state and measurement functions, no linearization technology is needed. PF can assure the convergence of the state estimation which follows from the classical results on convergence of Bayesian estimators. Simulations have been done and Particle filter has been shown to be a superior alternative to the EKF in the gravity gradient-terrain matching navigation systems. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Extended Kalman filters

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