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作者： Hu, D. Hu, J. Education Ministry's Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications China Jiangsu Province's Key Lab of Image Procession and Image Communications Nanjing University of Posts and Telecommunications China

ISBN: (纸本)9781845648282

Nonparametric model is one of the popular ones for background modeling for its ability to adapt to changes quickly in dynamic environment and enable very sensitive detection of moving objects. However, the method is too complex and computationally inefficient to real-time application. In this paper, a fast and efficient scheme for moving object detection by nonparametric background modeling was proposed, whereas the background was modeled combining clustering theory to eliminate the large amount of redundant information and noise pixels in background modeling stage. Moreover, adaptive background and temporal differencing was used to filter out unchanged background pixels, thus enabling an additional reduction in computational requirements and improving the detection results. Besides, RGB color components were substituted by chromaticity coordinates to suppress shadow effects. Experimental results demonstrated the effectiveness of the proposed algorithm. © 2014 WIT Press.

关键词： Object detection

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Research on moving object detection based on the Bayesian theory

Research on moving object detection based on the Bayesian th...

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作者： Hu, D. Liu, X. Education Ministry's Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications China Jiangsu Province's Key Lab of Image Procession and Image Communications Nanjing University of Posts and Telecommunications China

ISBN: (纸本)9781845648282

For effective and efficient detection of moving objects from complex surveillance scenarios, a novel integrated object detection scheme based on clustering and Bayesian theory is proposed in this paper. The algorithm conducts clustering analysis in the time domain as the pre-segmentation step for later Bayesian judgment processing. Furthermore, temporal differential image was used to classify dynamical and static pixels, which were then used to direct the Bayesian judgment with proper models. The clustering processing reduced the number of the mixture models, which in turn, reduced the computational complexity while ensuring accurate moving object detection. Simulation results show that the proposed scheme well exploited the temporal correlation of the video sequences and the accuracy of the detected objects can be improved by nearly 20% compared to the typical Gaussian mixture modeling algorithm. © 2014 WIT Press.

关键词： Object detection

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Vehicle color recognition using monocular camera

Vehicle color recognition using monocular camera

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2011 International Conference on Wireless communications and Signal processing, WCSP 2011

作者： Yang, Mengjie Han, Guang Li, Xiaofei Zhu, Xiuchang Li, Liang Jiangsu Provincial Key Lab of Image Processing and Image Communication Nanjing Univ. of PandT Nanjing China Engineering Research Center of Wideband Wireless Communication and Display Technique Ministry of Education Nanjing Univ. of PandT Nanjing China

ISBN: (纸本)9781457710100

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. © 2011 IEEE.

关键词： Color

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Mean Shift Object Tracking with Modified LTP

Mean Shift Object Tracking with Modified LTP

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2012 IEEE 11th International Conference on Signal processing(ICSP 2012)

作者： ZHANG Shaona HU Dong HU Yanting Education Ministry's Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications The key lab of image processing and transmission of Jiangsu province Nanjing University of Posts and Telecommunications School of Telecommunication and Information Engineering Nanjing University of Posts and Telecommunications

ISBN: (纸本)9781467321969

A new scheme of object tracking with mean shift is put forward in this paper. At first, texture feature is fused in the processing by Local Ternary Pattern (LTP). Since LTP is sensitive to local noise, least median of squares (LMedS) algorithm is used to adaptively calculate the noise threshold for accurate estimation of the LTP texture information. Furthermore, target scale and orientation is estimated in case of partial occlusion or rotation, so as to realize robust object tracking. Experimental results show that the proposed algorithm can acquire robust tracking performance under complex background .

关键词： Object tracking, Mean Shift, LTP, LMedS

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Content-Adaptive Interpolation for Spatial Error Concealment

Content-Adaptive Interpolation for Spatial Error Concealment

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International Conference on Information and Computing Science, ICIC

作者： Hua-yan Luo Zong-liang Gan Xiu-chang Zhu Jiangsu Province Key Laboratory on Image Processing & Image Communication Nanjing University of Posts and Telecommunications Nanjing China

When transmitting encoded images over a communication channel, the reconstructed image quality can be substantially degraded by channel errors. This paper presents a spatial error concealment algorithm that utilizes variance of surrounding pixels, and then classifies each error block (EB) into two categories: uniform block and edge block. For uniform block, nearest border prior spatial interpolation is adopted to restore missing pixels. We use the Wiener interpolation algorithm and special interpolation sequence for edge block. Experimental results indicate the proposed algorithm can attain well restored quality of intra-frames both subjectively and objectively. Meanwhile, the computational cost of the proposed algorithm can be significantly reduced, compared to Lipsilas method. And the restored quality is almost the same, sometimes even much better.

关键词： Interpolation image reconstruction image quality image restoration Video compression Telecommunication computing Gallium nitride image processing image communication Electronic mail

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Vehicle Type Classification via Adaptive Feature Clustering for Traffic Surveillance Video

Vehicle Type Classification via Adaptive Feature Clustering ...

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International Conference on Wireless communications and Signal processing

作者： Shu Wang Feng Liu Zongliang Gan Ziguan Cui Jiangsu Province Key Lab on Image Processing & Image Communications Nanjing University of Posts and Telecommunications

ISBN: (纸本)9781509028610

Vehicle type classification has become an important part of intelligent traffic. However traditional methods can not deal with the varying situations in the reality. In this paper, a novel method is proposed to handle this task in the real road traffic surveillance video. In order to distinguish different vehicles, we categorize vehicles into three types: compact cars, mid-size cars, and heavy-duty vehicles. For a certain video, our method has four steps. First, a deep convolutional neural network is used to detect vehicles in the candidate region and a data set would be generated. Second, the main features of vehicles can be extracted using a fully-connected network. Also, for the sake of higher accuracy, weak labels given by pre-trained extreme learning machine (ELM) are fused into the final features, adding prior information proportionally. Third, K-means is implemented to learn three vehicle-type cluster centers adaptively. Finally, vehicle type will be recognized according to the closest distance principal. Experimental results show that the recognition rate outperforms other traditional methods, verifying the feasibility and effectiveness of the proposed method.

关键词： Vehicle type classification Adaptive clustering Feature learning Deep learning Extreme learning machine

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Adaptive Mutation particle filter based on diversity guidance

Adaptive Mutation particle filter based on diversity guidanc...

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International Conference on Machine Learning and Cybernetics

作者： Yu, Jin-Xia Tang, Yong-Li Liu, Wen-Jing College of Computer Science and Technology Henan Polytechnic University Jiaozuo 454003 China Jiangsu Provincial Key Lab of Image Processing and Image Communication Nanjing University of Posts and Communication Nanjing 210003 China Department of Computer Science and Technology Tsinghua University Beijing 100084 China

ISBN: (纸本)9781424465262

Aimed at the deficiency of the resampling algorithm in PF, diversity measures ESS (effective sample size) and PDF (population diversity factor) are evaluated respectively. Combined with the estimation result, diversity measures PDF is used for adaptively tuning the resampling threshold. By integrating the operation of particle mutation after resampling into PF and using the above mechanism of diversity guidance, the AMPF algorithm (Adaptive Mutation PF) is presented so as to assure the diversity of particle sets. With the simulation program using matlab 7.0 to track a single target motion from a fixed visual observation points, the performance of diversity measures and AMPF are evaluated and the validity of the proposed method is verified. © 2010 IEEE.

关键词： Monte Carlo methods

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A Video Code Based on Distribution Compressive Sensing

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Procedia Engineering 2012年 29卷 3613-3618页

作者： Wu Ming-hu Zhu Xiu-chang Jiangsu Province Key Lab on Image Processing & Image Communication Nanjing University of Posts and Telecommunications Nanjing China School of Electrical and Electronic Engineering Hubei University of Technology Wuhan China

Compressed sensing (CS) is a new technique for simultaneous data sampling and compression. In this paper, we propose a new video coding algorithm based on Distributed Compressive Sampling(DCS) principles, where almost all computation burdens can be shifted to the decoder, resulting in a very lowcomplexity encoder. At the decoder, compressed video can be efficiently reconstructed. Our algorithm can be useful in those video applications that require very low complex encoders. Simulation results show that our scheme compares favorably with existing schemes at a much lower implementation cost.

关键词： Distributed Compressive Sampling sparsity distributed video coding

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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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Research on diversity measure in particle filter

Research on diversity measure in particle filter

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2010 International Conference on Intelligent Computation Technology and Automation, ICICTA 2010

作者： Yu, Jinxia Tang, Yongli Liu, Wenjing College of Computer Science and Technology Henan Polytechnic University Jiaozuo 454003 China Jiangsu Provincial Key Lab of Image Processing and Image Communication Nanjing University of Posts and Communication Nanjing 210003 China Department of Computer Science and Technology Tsinghua University Beijing 100084 China

ISBN: (纸本)9780769540771

It is very important to find diversity measure when to perform a resampling step in particle filter. By analyzing the inherent deficiency in resampling algorithm of particle filter, some diversity measures including effective sample size, population diversity factor and particle swarm diversity measure are introduced. Then combined with the simulation program using matlab 7.0 to track a single target motion from a fixed visual observation points, the performance of diversity measures in resampling algorithm of PF are evaluated. © 2010 IEEE.

关键词： Monte Carlo methods

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