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International Conference on Wireless communications and Signal processing (WCSP)

作者： Ya'nan Yang Xiaofan Wang Feng Liu Zongliang Gan Jiangsu Province Key Lab on Image Processing & Image Communications Nanjing University of Posts and Telecommunications Nanjing China Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781509028610

Since the lighting conditions in strong contrast regions between the light and dark cant be estimated accurately by traditional center/surround Retinex algorithm, the over-enhancement and color distortion may exist. In view of this, combining with the human visual characteristics, a color image enhancement algorithm based on tone-preserving was proposed. A determination function was added to the bilateral filter to estimate illuminance image more accurately and weaken over-enhancement. According to human visual masking effect, the improved gamma correction was utilized to correct the brightness of illumination image adaptively and the local contrast of reflection image obtained by division was enhanced based on local statistics. Besides, the final enhanced image was obtained by combining illumination image with reflection image, which can make image appear more natural. Compared with other similar algorithms from both subjective and objective aspects, the results show that this method being applied to low-contrast color image enhancement can not only improve image clarity, but reduce color distortion.

关键词： image color analysis Brightness Lighting Color Visualization Nonlinear distortion

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A novel improved binarized normed gradients based objectness measure through the multi-feature learning 8th

A novel improved binarized normed gradients based objectness...

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8th International Conference on image and Graphics, ICIG 2015

作者： Zhao, Danfeng Hu, Yuanyuan Gan, Zongliang Chen, Changhong Liu, Feng Jiangsu Provincial Key Lab of Image Processing and Image Communication Nanjing University of Posts and Telecommunications Nanjing210003 China

ISBN: (纸本)9783319219776

In this paper, we propose a novel improved binarized normed gradients (BING) objectness method based on the multi-feature boosting learning. A series of difference of gaussians (DoG) of the images with given parameters are used for the feature extraction stage, since the image DoG filter can better describe objects border. In addition, in training phase, the classifier can adaptively combine the features from different scales and different frequency components. Moreover, since the norm of the feature gradients is a simple 64D feature in the proposed framework, the computational complexity of the algorithm is in the same level compared with the BING measure. Experiments on the challenging PASCAL VOC 07 dataset show that the proposed method can not only achieve higher detection rate and average score than some current related objectness measures, but also lead to a very competitive accuracy of locating objects, even in some difficult cases. © Springer International Publishing Switzerland 2015.

关键词： Computational complexity

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Edge directed single image super resolution through the learning based gradient regression estimation 8th

Edge directed single image super resolution through the lear...

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8th International Conference on image and Graphics, ICIG 2015

作者： Si, Dandan Hu, Yuanyuan Gan, Zongliang Cui, Ziguan Liu, Feng Jiangsu Provincial Key Lab of Image Processing and Image Communication Nanjing University of Posts and Telecommunications Nanjing210003 China

ISBN: (纸本)9783319219622

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 based gradient estimation. In proposed method, the gradient of HR image is estimated by using the example based ridge regression model. 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. Moreover, the precomputed projective matrix of the ridge regression can reduce the computational complexity further. Experimental results suggest that the proposed method can achieve better gradient estimation of HR image and competitive SR quality compared with other SR methods. © Springer International Publishing Switzerland 2015.

关键词： Optical resolving power

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A novel saliency detection model based on curvelet transform

A novel saliency detection model based on curvelet transform

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International Conference on Wireless communications and Signal processing (WCSP)

作者： Peiqing Bai Ziguan Cui Zongliang Gan Guijin Tang Feng Liu Image Processing & Image Communication Lab Nanjing University of Posts & Telecommunications Nanjing China Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts & Telecommunications Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9781509028610

Visually salient object or region detection in images is an active research field in recent years. Inspired by that curvelets can provide multi-scale sparse representation of objects with edges and textures, in this paper, we propose a novel saliency detection model based on fast discrete curvelet transform (SDCT) to detect more compact salient objects in an image. First, fast discrete curvelet transform is used to acquire multi-scale representation of feature maps in CIElab.color space. Then the feature maps are transformed to feature salient maps based on dissimilarity measure between patches in a global manner. Finally, the complementary feature salient maps at each scale and each color channel are merged linearly to obtain unitary saliency map. Experimental results on MSRA saliency benchmark database show that the proposed SDCT model outperforms the most state-of-the-art saliency detection models in spatial and frequency domain with higher overall performance, especially acquires more compact salient object and suppresses background saliency effectively, which is desirable for many computer vision applications.

关键词： Transforms Feature extraction image color analysis Frequency-domain analysis Computational modeling Visualization image edge detection

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Robust face hallucination via similarity selection and representation 8th

Robust face hallucination via similarity selection and repre...

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8th International Conference on image and Graphics, ICIG 2015

作者： Liu, Feng Yin, Ruoxuan Gan, Zongliang Chen, Changhong Tang, Guijin Jiangsu Provincial Key Lab of Image Processing and Image Communication Nanjing University of Posts and Telecommunications Nanjing210003 China

ISBN: (纸本)9783319219684

Face image super resolution, also referred to as face hallucination, is aiming to estimate the high-resolution (HR) face image from its low-resolution (LR) version. In this paper, a novel two-layer face hallucination method is proposed. Different from the previous SR methods, by applying global similarity selecting, the proposed approach can narrow the scope of samples and boost the reconstruction speed. And the local similarity representation step make the method have better ability to suppress noise for applications under severe condition. As a general framework, other useful algorithms can also be incorporated into it conveniently. Experiments on commonly used face database demonstrate our scheme has better performance, especially for noise face image. © Springer International Publishing Switzerland 2015.

关键词： Computers

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Multiscale Relative Transition Entropy Analysis of Electroencephalogram

Multiscale Relative Transition Entropy Analysis of Electroen...

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2016 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics(AMEII 2016)

作者： Ying Wang Fengzhen Hou Jiafei Dai Jin Li Jun Wang Image Processing and Image Communications Key Lab. Nanjing Univ.of Posts & Telecomm. School of Science China Pharmaceutical University Nanjing General Hospital of Nanjing Military Command College of Physics and Information Technology Shaanxi Normal University

ISBN: (纸本)9781510823808

Recently,quantifying the complexity of the physiological time series has become more and more *** one of the most complex physiological signals,electroencephalogram(EEG) including a large number of physiological and pathological information attracts widespread ***,many traditional algorithms fail to account for the multiple times scales inherent in physiologic *** this paper,we proposed multiscale relative transition entropy algorithm(MRTE) to analyze the white noise and pink noise,the adolescent and adults EEG as well as normal and epileptic *** results indicate that there are distinct tendency among different types EEG which indicating that the multiscale relative transition entropy can distinguish different physiological and pathological signals.

关键词： multiscale relative transition entropy complexity

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Effective Scheme for Global Abnormal Event Detection for Surveillance Video

Effective Scheme for Global Abnormal Event Detection for Sur...

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2016 International Conference on communications, Information Management and Network Security (CIMNS2016)

作者： Fangxu Dong Dong Hu Education Ministry's Key Lab of Broadband Wireless Communication and Sensor Network Technology Education Ministry's Engineering Research Center of Ubiquitous Network and Heath Service Jiangsu Province's Key Lab of Image Processing and Image Communications Nanjing University of Posts and Telecommunications

ISBN: (纸本)9781510830981

An effective algorithm for global abnormal detection from surveillance video is proposed in this paper. The algorithm is based on sparse representation. To deal with the illumination change in video scenes, specific feature extract methods are designed for corresponding illumination conditions. In the case of non-uniform illumination, features are extracted directly on the original image;in the case of uniform illumination, features are extracted on the binary image obtained by threshold segmentation on the difference image, where the thresholds are computed by the Otsu's method. The features extracted on normal video are used to learn an over-complete dictionary. Then, the sparse reconstruction cost over the dictionary is used to detect abnormal events. Experiments on the open global abnormal dataset and the comparison to the state-of-the-art methods validate effectiveness and quickness of our algorithm.

关键词： abnormal detection illumination change binary images sparse reconstruction cost

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A fractal image coding method combined with compressed sensing algorithm

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu

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Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu 2016年第4期 78-83页

作者： Guo, Hui He, Jie Hu, Defa Jiang, Weijin Guangxi Colleges and Universities Key Lab. of Image Processing and Intelligent Information System Wuzhou University Wuzhou Guangxi China School of Computer and Information Engineering Hunan University of Commerce Changsha Hunan China

Purpose. Since fractal image coding is time-consuming and is prone to causing "blocking artifact", the article aims to combine fractal image coding, wavelet transform and compressed sensing to put forward a method which can shorten the coding time effectively and improve the quality of a reconstructed image. Methodology. The compressed sensing algorithm can quickly compress and highly restore sparse matrixes. The paper made use of this feature to conduct fractal coding for a low-frequency sub-image after wavelet transform, followed by recoding the samples of low-frequency differential sub-graphs and high-frequency sub-images by means of the compressed sensing algorithm for the purpose of compensating the quality of reconstructed images. Findings. Compared to the traditional fractal coding method, the algorithm in the paper (hereinafter referred to as "this Algorithm") can shorten the time considerably and get a maximum speed-up ratio by up to 6.45 times. Compared to the compressed sensing coding method, the quality of the reconstructed images is improved significantly. Originality. The innovation of the paper lies in applying the compressed sensing theory to the fractal coding algorithm to compensate the quality of the reconstructed images obtained by means of fractal coding based on wavelet transform. Practical value. This Algorithm can shorten the coding time on the basis of ensuring the quality of a reconstructed image, and has certain significance for promoting the fractal coding method. © Hui Guo, Jie He, Defa Hu, Weijin Jiang, 2016.

关键词： Wavelet transforms

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Robust feature detection using particle keypoints and its application to video stabilization in a consumer handheld camera

Robust feature detection using particle keypoints and its ap...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Semi Jeon Inhye Yoon Seung'i Yang Bongmo Kim Jisung Kim Joonki Paik Image Processing and Intelligent Systems Laboratory Multimedia and Film Chung-Ang University Seoul Korea Future Technology R&D Division Video Technology Lab.

Compact, portable digital cameras have been popular to consumers. This paper presents a robust feature detection method using particle keypoints and its application to video stabilization. The proposed video stabilization algorithm consists of three steps: i) generation of a flat region map based on the Gaussian filter, ii) robust feature point detection using particle keypoints, and iii) camera paths estimation for enhancing a shaky video. As a result, the proposed algorithm can estimate optimal homography by redefining important feature points using particle keypoints in the flat region map. The proposed robust feature detection algorithm is suitable for enhancing the quality of video acquired by consumer handheld imaging devices.

关键词： Conferences Consumer electronics

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image normalization for quantitative immunohistochemistry in digital pathology

Image normalization for quantitative immunohistochemistry in...

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IEEE International Symposium on Biomedical Imaging

作者： Yves-Rémi Van Eycke Justine Allard Mélanie Derock Isabelle Salmon Olivier Debeir Christine Decaestecker Lab. of Image Signal processing & Acoustics Université Libre de Bruxelles (ULB) Brussels Belgium DIAPath Center for Microscopy and Molecular Imaging (ULB) Gosselies Belgium Department of Pathology Université Libre de Bruxelles (ULB) Brussels Belgium

ISBN: (纸本)9781479923519

We propose to adapt to immunohistochemistry (IHC) some methods proposed to normalize images from histological slices stained with hematoxylin-eosin (H&E). Our final aim is to provide a coherent quantitative characterization of IHC biomarkers across different IHC batches with possible staining variations. In contrast to H&E, IHC staining strongly varies with the tissue analyzed and the protein targeted, making image normalization challenging. To solve this problem, we added in each IHC batch a slice from a reference tissue microarray (TMA) and then digitalized it to establish an inter-batch normalization transform. A comparison of two methods adapted to the specificity of IHC-stained slides evidences some normalization requirements to make valid IHC biomarker quantification across different staining batches.

关键词： image color analysis Biological tissues Proteins Deconvolution Biomarkers Immune system Pathology

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