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2012 IEEE 5th International Conference on Advanced Computational Intelligence, ICACI 2012

作者： Wu, Shuang Fan, Yawen Zheng, Shibao Yang, Hua Institute of Image Communication and Network Engineering Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai Jiao Tong University Shanghai 200240 China

ISBN: (纸本)9781467317436

Object tracking is an important issue in video surveillance. In this paper, we present a tracking framework based on ORB (Oriented FAST and Rotated BRIEF) feature using temporal-spacial constraint. ORB is a fast binary descriptor which needs low computation cost and has similar matching performance with SIFT or SURF. Firstly, ORB keypoints and their descriptors of the object are calculated on two adjacent frames. Afterwards, a best match between these two point sets is obtained through computing Hamming distance. Then, the next location of the object can be deduced from the matched keypoints. In the case of without match, we can still predict the target's location using temporal-spacial constraint. The performance of our tracking system is evaluated on datasets of real surveillance scenes. The experiments results show that the presented approach can track objects accurately and efficiently. © 2012 IEEE.

关键词： Hamming distance

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Linear Extended State Observer-based Sliding Mode Control of Active Magnetic Bearing Systems 41

Linear Extended State Observer-based Sliding Mode Control of...

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第41届中国控制会议

作者： Ziheng Yu Yifeng Tang Bowen Xu Xiang Huang 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

In the last decades,as a typical nonlinear system,active magnetic bearings(AMB) system has been widely applied in manufacturing systems.A sliding mode control(SMC) scheme for the AMB system is proposed with the disturbance observation of the linear extended state observer(LESO) in this *** chattering of the AMB system has been reduced by LESO-SMC by at least 60%.Sufficient BIBO(bounded input-bounded output) stability conditions of the closed-loop AMB system governed by the proposed LESO-SMC are derived by Lyapunov ***,experiments are conducted to verify the effectiveness and superiority of the proposed LESO-SMC than conventional SMC.

关键词： Active magnetic bearing systems state observer sliding mode control chattering

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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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Exudate detection in fundus images via convolutional neural network 14th

Exudate detection in fundus images via convolutional neural ...

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14th International Forum of Digital TV and Wireless Multimedia communication, IFTC 2017

作者： Li, Guo Zheng, Shibao Li, Xinzhe Shanghai Key Labs of Digital Media Processing and Transmission Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9789811081071

Exudate detection in fundus images is an important task for the screening of people with diabetic retinopathy. In this paper, Convolutional Neural Network (CNN) is used to detect the exudates in fundus images. An auxiliary loss for classification is designed to better train the CNN architecture. Besides, we use a boosted training method to improve and speed-up the CNN training. The trained model has been evaluated on our own annotated dataset and three public available databases, obtaining an AUC of 0.98, 0.96, 0.94, 0.91 respectively. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Convolution

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Automatic exudate detection in color fundus images 13th

Automatic exudate detection in color fundus images

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13th International Forum of Digital TV and Wireless Multimedia communication, IFTC 2016

作者： Qi, Fucong Li, Guo Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Key Labs of Digital Media Processing and Transmission Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9789811042102

Diabetic retinopathy is a major cause of blindness in working age population and exudates are considered the most significant characteristics of diabetic retinopathy. Therefore, automatic exudate detection is beneficial to large-scale diabetic retinopathy screening. In this paper, an automatic approach for detection of exudates on color fundus images is presented and discussed, which is based on the thresholding technique and Kirsch’s edge detection. Besides, a color space conversion step (from RGB to YIQ) is utilized to improve the detection performance. The method is evaluated on a public dataset of fundus images from various ethnic groups. We obtain an average sensitivity of 75.17% and an average specificity of 97.98%, which outperforms the baseline method and validates the effectiveness of the proposed method. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Edge detection

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Neural Architecture Search Based on Tabu Search and Evolutionary Algorithm 11

Neural Architecture Search Based on Tabu Search and Evolutio...

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

作者： Fan, Zhun Long, Zhoubin Li, Wenji Wang, Zhaojun Yang, Zhi Wang, Liu Guangdong Province Engineering College Shantou University Department of Key Lab of Digital Signal and Image Processing GuangdongShantou China

ISBN: (纸本)9781665425278

Most existing optimization methods for neural architecture search (NAS), including evolutionary algorithms, reinforcement learning and gradient-based approaches, have not employed memory strategies explicitly, which may lack of efficiency when searching neural architectures. To solve this issue, we propose a new NAS approach by using an evolutionary algorithm which employs a tabu mechanism to help to improve the search efficiency. To be more specific, the individuals of parent population are selected by tournament selection and tabu list. The tournament selection select parent population according to the accuracy of each individual. And the tabu mechanism builds a tabu list to record the chosen operations in the last previous search process, which employs a search memory mechanism to improve the efficiency explicitly. To confirm the superior performance of our approach, a well-designed surrogate model is used to accelerate the process of performance evaluation on CIFAR-10. The comprehensive experimental results show that the proposed method can reach to 2.48% error rate with about 2 GPU days, which demonstrates the superiority of the suggested method. © 2021 IEEE.

关键词： Evolutionary algorithms

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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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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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An empirical study of translation rule extraction with multiple parsers

An empirical study of translation rule extraction with multi...

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23rd International Conference on Computational Linguistics, Coling 2010

作者： Xiao, Tong Zhu, Jingbo Zhang, Hao Zhu, Muhua Natural Language Processing Lab. Northeastern University China Key Laboratory of Medical Image Computing Ministry of Education China

Translation rule extraction is an important issue in syntax-based Statistical Machine Translation (SMT). Recent studies show that rule coverage is one of the key factors affecting the success of syntaxbased systems. In this paper, we first present a simple and effective method to improve rule coverage by using multiple parsers in translation rule extraction, and then empirically investigate the effectiveness of our method on Chinese- English translation tasks. Experimental results show that extracting translation rules using multiple parsers improves a string-to-tree system by over 0.9 BLEU points on both NIST 2004 and 2005 test corpora.

关键词： Extraction

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Edge directed single image super resolution through the learning based gradient regression estimation

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ICIC Express Letters, Part B: Applications 2016年第2期7卷 337-344页

作者： Wang, Guogang Gan, Zongliang Zhu, Xiuchang Jiangsu Provincial Key Lab of Image Processing and Image Communication Nanjing University of Posts and Telecommunications No. 66 Xinmofan Road Nanjing China

Single image super resolution (SR) aims to estimate high resolution (HR) image from the low resolution (LR) one, and estimating accuracy of HR image gradient is very important for edge directed image SR methods. In this paper, we propose a novel edge directed image SR method by learning simple priors by mapping functions. Recognizing that the training samples of the given sub-set for regression should have similar local geometric structure based on clustering, we employ high frequency of LR image patches with removing the mean value to perform such clustering. Then we learn effective mapping functions of gradient of bicubic image patches and that of HR image patches for each cluster. Experimental results suggest that the proposed method can achieve better gradient estimation of HR image and competitive SR quality compared with other SR methods. © 2016 ICIC International.

关键词： Optical resolving power

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