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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 Novel Two-Stage PCA Algorithm for Object Tracking

A Novel Two-Stage PCA Algorithm for Object Tracking

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

作者： Yuanyuan Yang 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 Procession and Image Communications Nanjing University of Posts and Telecommunications

ISBN: (纸本)9781510830981

In this paper, we propose a two-stage PCA algorithm to deal with the problem of target appearance changes in object tracking. Our method is based on particle filter framework and aims at building up a robust appearance model for the target. In the first-stage, PCA is applied on several templates collected before tracking to construct a low dimensional subspace for the targets. Correspondingly, in the later tracking, all particles will be projected into this subspace to calculate the weight of particles, as well as the location of the target by weighted sum of particles. In the second-stage, PCA is adopted to decompose the reconstruction error into orthogonal basis to find the basis with biggest variance which can best present the appearance changes. Correspondingly, we uses this basis to update the old subspace. Besides, a threshold is set to decide when to update which greatly reduce the numbers of update. The two stages work together to establish a robust appearance model making our tracking algorithm more robust. Experimental results on public video sequence demonstrate the effectiveness of our proposed algorithm.

关键词： particle filter two-stage PCA eigenspace reconstruction error

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Visual perception preserving decolorization method

International Journal of Signal Processing, Image Processing...

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International Journal of Signal processing, image processing and Pattern Recognition 2016年第7期9卷 65-78页

作者： Chen, Jie Li, Xin Zhu, Xiuchang Wang, Jin Key Lab of Image Processing and Image Communication of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing210003 China College of Information Engineering Yangzhou University Yangzhou225009 China

This paper presents a decolorization method using gradient and saliency as the maintained features in the conversion to preserve the local and global visual perception. First, we construct a linear parametric mapping function of RGB color channels. Then, we calculate the feature value of each pixel in the color image and the parameterized grayscale image, the feature value integrates the pixel gradient and region saliency. Finally, we search for the parameters which can get the minimum of the total differences between the feature values of color and grayscale images, and substitute into the linear parametric function to get the decolorization result. To enhance the efficiency of getting the best parameters, we properly relax the strict computation formulas of the gradient and saliency to construct a linear least square problem, and obtain the optimal parameters by solving optimization. Experimental results show that our method using the discrete searching strategy can maintain the contrasts meanwhile avoid the excessive enlargement of the contrasts during the color-to-gray conversion, this property guarantees the preserving of the visual perception. Our method using the linear least square strategy can reduce the computation time and frequently get the similar results with our discrete searching method. © 2016 SERSC.

关键词： Pixels

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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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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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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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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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Neonatal Pain Expression Recognition Based on LBP Features and Sparse Representation

Neonatal Pain Expression Recognition Based on LBP Features a...

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2015 World Conference on Control, Electronics and Electrical Engineering(WCEE 2015)

作者： Guanming Lu Mengying Chen Xiaonan Li Jiangsu Province Key Lab on Image Processing & Image Communication Nanjing University of Posts and Telecommunications Nanjing Children's Hospital Affiliated to Nanjing Medical University

Facial expressions are considered a reliable indicator in neonatal pain *** paper proposes a new neonatal pain expression recognition method,which utilizes the feature descriptors based on weighted Local Binary Pattern(LBP)and the classifier based on sparse ***,the normalized facial image is described using a feature vector,which is histogram sequence obtained by concatenating the weighted histograms of the LBP maps of all the local ***,the Principal Component Analysis(PCA)method is used to reduce the dimension of the feature ***,the classifier based on sparse representation is applied to classify test sample into four classes of facial expressions:calm,crying,moderate pain,severe *** objective of this study is to assist the clinicians in assessing neonatal pain by utilizing computer-based image analysis *** experimental results on neonate facial image database show the effectiveness of the proposed *** classification accuracy is up to 85.50%.

关键词： expression recognition,local binary pattern,sparse representation,neonate,pain expression.

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A parallel HEVC encoder scheme based on Multi-core platform

A parallel HEVC encoder scheme based on Multi-core platform

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2015 4th National Conference on Electrical,Electronics and Computer Engineering(NCEECE 2015)

作者： Shu Jun Hu Dong Education Ministry’s Key Lab of Broadband Wireless Communication and Sensor Network Technology Education Ministry’s Engineering Research Center of Ubiquitous Network and Health Service System Jiangsu Province’s Key Lab of Image Procession and Image Communications Nanjing University of Post and Telecommunications

ISBN: (纸本)9781510819085

In this paper, we propose a parallel HEVC encoder scheme based on multi-core platform, which provides maximized parallel scalability by exploiting two-level parallelism, namely, the frame level parallelism and the CTB level parallelism. Inspired by the intra-CTB row level parallelism of WPP in HEVC, we investigate the inter-frame CTB prediction dependency to its reference CTBs, and find the inter-CTB correlation. Using this inter-correlation, we divide a frame into CTB units and create CTB-row level coding threads when their corresponding reference CTBs are available. Each thread is bonded to a processing core, therefore, both intra- and inter-CTB rows can be encoded in parallel. Moreover, we introduce a priority scheduling mechanism to control the coding threads. Experiments on Tilera-Gx36 multi-core platform show that, compared with serial execution, the proposed method achieves 3.6 and 4.3 times speedup for 1080 P and 720 P video sequences, respectively.

关键词： HEVC multi-core platform parallel processing frame-level CTB-level

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