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Motion cue based pedestrian detection with two-frame-filtering

High Technology Letters 2015年第3期21卷 328-332页

作者：吕敬钦 Zhang Miaohui Yang Jie Institute of Image Processing and Pattern Recognition Shanghai Jiaotong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of China

This study proposes a motion cue based pedestrian detection method with two-trame-filtering （Tff） for video surveillance. The novel motion cue is exploited by the gray value variation between two frames. Then Tff processing filters the gradient magnitude image by the variation map. Summa- tions of the Tff gradient magnitudes in cells are applied to train a pre-deteetor to exclude most of the background regions. Histogram of Tff oriented gradient （HTffOG） feature is proposed for pedestrian detection. Experimental results show that this method is effective and suitable for real-time surveil- lance applications.

关键词： pedestrian detection two-frame-filtering （TFF） Tff magnitude vector （TffMV） Histogram of Tff oriented gradient （ HTffOG） SVM video surveillance

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Robust visual tracking via inverse nonnegative matrix factorization

Robust visual tracking via inverse nonnegative matrix factor...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Fanghui Liu Tao Zhou Keren Fu Irene Y. H. Gu Jie Yang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China Department of Signals and Systems Chalmers University of Technology Gothenburg Sweden

The establishment of robust target appearance model over time is an overriding concern in visual tracking. In this paper, we propose an inverse nonnegative matrix factorization (NMF) method for robust appearance modeling. Rather than using a linear combination of nonnegative basis vectors for each target image patch in conventional NMF, the proposed method is a reverse thought to conventional NMF tracker. It utilizes both the foreground and background information, and imposes a local coordinate constraint, where the basis matrix is sparse matrix from the linear combination of candidates with corresponding nonnegative coefficient vectors. Inverse NMF is used as a feature encoder, where the resulting coefficient vectors are fed into a SVM classifier for separating the target from the background. The proposed method is tested on several videos and compared with seven state-of-the-art methods. Our results have provided further support to the effectiveness and robustness of the proposed method.

关键词： Target tracking Encoding Visualization Robustness Computational modeling Sparse matrices Support vector machines

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LEARNING FULL-RANGE AFFINITY FOR DIFFUSION-BASED SALIENCY DETECTION

LEARNING FULL-RANGE AFFINITY FOR DIFFUSION-BASED SALIENCY DE...

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IEEE International Conference on Acoustics, Speech and Signal processing

作者： Keren Fu Irene Y.H. Gu Jie Yang Department of Signals and Systems Chalmers University of Technology Gothenburg Sweden Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

ISBN: (纸本)9781479999897

In this paper we address the issue of enhancing salient object detection through diffusion-based techniques. For reliably diffusing the energy from labeled seeds, we propose a novel graph-based diffusion scheme called affinity learning-based diffusion (ALD), which is based on learning full-range affinity between two arbitrary graph nodes. The method differs from the previous existing work where implicit diffusion was formulated as a ranking problem on a graph. In the proposed method, the affinity learning is achieved in a unified graph-based semi-supervised manner, whose outcome is leveraged for global propagation. By properly selecting an affinity learning model, the proposed ALD outperforms the ranking-based diffusion in terms of accurately detecting salient objects and enhancing the correct salient objects under a range of background scenarios. By utilizing the ALD, we propose an enhanced saliency detector that outperfomis 7 recent state-of-the-art saliency models on 3 benchmark datasets.

关键词： Saliency detection graph-based diffusion affinity learning semi-supervised learning Semi-supervised Learning affinity diffusion Adrenoleukodystrophy Affine transformation Object detection Learning

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Pulsar Candidate Selection by Assembling Positive Sample Emphasized Classifiers

Pulsar Candidate Selection by Assembling Positive Sample Emp...

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International Conference on Computational Intelligence and Security

作者： Yao Yao Xin Xin Ping Guo School of Computer Science and Technology Beijing Institute of Technology Beijing China Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

ISBN: (纸本)9781509048410

Pulsar candidate selection identifies prospective observations of modern radio pulsar surveys for further inspection in search of real pulsars. Typically, human experts visually select valuable candidates and eliminate radio frequency interference or other noises. Recently, machine learning methods are adopted to automate this task, which saves human labor and makes it possible for processing millions of observations efficiently. Considering the number of positive training samples are relatively too small and the cost of incorrectly labeling a real pulsar candidate as negative is large, we propose a novel hierarchical candidate-sifting model by emphasizing the cost of incorrect prediction of positive samples and assembling multiple classifiers trained with different weighting parameters. Experiments on three pulsar selection datasets demonstrate our proposed method improves the pulsar-sifting performance a lot according to several standard evaluation metrics.

关键词： Training Support vector machines Predictive models Feature extraction Measurement Labeling Histograms

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A real-time algorithm for train position monitoring using optical time-domain reflectometry

A real-time algorithm for train position monitoring using op...

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IEEE International Conference on Intelligent Rail Transportation (ICIRT)

作者： Adam Papp Christoph Wiesmeyr Martin Litzenberger Heinrich Garn Walter Kropatsch Digital Safety and Security Department Austrian Institute of Technology GmbH Vienna Austria Pattern Recognition and Image Processing group Vienna University of Technology Vienna Austria

ISBN: (纸本)9781509015566

We propose an algorithm which uses an optical time-domain reflectometer (OTDR) for real-time tracking of trains. OTDR sensing, often also termed distributed acoustical sensing (DAS), measures the Rayleigh backscattering of a light pulse along an optical fiber. The resulting signal provides information on local acoustic pressure at linearly spaced positions along the fiber. While different approaches for train tracking with DAS are described in the literature, the results have been evaluated only for short time recordings with few train crossings. In this paper we provide details on the tracking performance of a novel algorithm that finds and tracks trains over 15km. Furthermore, this is the first contribution that uses ground truth data to assess the performance of the method. For the evaluation two one hour recordings are used.

关键词： Monitoring Vibrations Optical fibers Sensors Backscatter Rail transportation Time measurement

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Automatic hemorrhage detection in color fundus images based on gradual removal of vascular branches

Automatic hemorrhage detection in color fundus images based ...

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IEEE International Conference on image processing

作者： Lei Zhou Penglin Li Qi Yu Yu Qiao Jie Yang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Shanghai General Hospital Shanghai Jiao Tong University Shanghai China

Hemorrhages in color fundus images usually vary in size and shape, and some are even connected with the retinal vasculature such that they are often omitted by previous detection methods. In this paper, we propose a new method to deal with these problems. During the hemorrhage candidate extraction stage, dark regions and retinal vasculature are segmented out respectively. Their individual advantages are fused together into a binary image with good vascular continuity. Then the gradual removal of vascular branches is applied to generate hemorrhage candidates. In the classification stage, a SVM classifier using 49 features is trained to classify the candidates into hemorrhages and non-hemorrhages. The experiments on DIARETDB1 and DIARETDB0 show that our method achieves good performance on sensitivity and specificity, especially at lesion level.

关键词： Hemorrhaging image segmentation Lesions Retinal vessels image color analysis Feature extraction

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image Guided Surgery System for Abdominal Aortic Aneurysm Operation

Image Guided Surgery System for Abdominal Aortic Aneurysm Op...

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

作者： Ming Niu Yajun Wu Lisheng Wang Chaoyi Cui Institute of Image Processing and Pattern Recognition Department of AutomationShanghai Jiao Tong University The Ninth People's Hospital affiliated to Shanghai Jiao Tong University

ISBN: (纸本)9781510823808

Abdominal aortic aneurysm(AAA) is a local enlargement of the abdominal *** can bring 90% mortality rate when the aneurysm expands gradually until ***,the stent interventional procedure is an effective means of treating the disease,which requires clinicians clearly understand the anatomical structure of abdominal aorta and the *** order to reduce the measurement risk and enhance the safety of operation,an image guided surgery(IGS) system is applied into *** the system,surgeons can know exactly morphological structure about the lesions and extract concerned parameters during *** paper introduces how 3D models of the abdominal aorta and aneurysm are segmented and reconstructed from CT images,and how appropriate centerline path are extracted and used for internal virtual roaming in these 3D *** how tumor diameter is measured through cutting plane perpendicular to computed axis Clinical applications have shown that such IGS system is practical and helpful in the operation.

关键词： Abdominal aortic aneurysm segment reconstruct surgery planning

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CONGENITAL HEART DISEASE (CHD) DISCRIMINATION IN FETAL ECHOCARDIOGRAM BASED ON 3D FEATURE FUSION

CONGENITAL HEART DISEASE (CHD) DISCRIMINATION IN FETAL ECHOC...

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IEEE International Conference on image processing

作者： Liqun Ji Yun Gu Kun Sun Jie Yang Yu Qiao Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Division of Pediatric Cardiology Xin Hua Hospital School of Medicine Shanghai Jiao Tong University

ISBN: (纸本)9781467399623

Automatic diagnosis for fetal echocardiography plays an important part in diagnostic aid in the discrimination of congenital heart disease (CHD). Instead of traditional methods analyzing 2D cardiac echo video that need to find the standard view for discrimination, in this paper, we proposed a new system for automatic discrimination of CHD applying 4D original echocardiogram, which avoids the challenging work of searching standard views. We extracted the features of 3D static structure and 3D motion via 3D SIFT and 3D Histogram of Optical Flow (HOF) from the original 4D (3D+T) echocardiogram data, respectively. Bag of Words (BoW) method was employed to construct the quantized feature. Both static and motion features were fused to form the final image representation. One-Class SVM classifier was utilized to discriminate CHD due to the lack of CHD data and the significant difference in all the CHD cases. Experiments on the real data demonstrate the improved discrimination accuracy due to the fused feature.

关键词： Three-dimensional displays Feature extraction Support vector machines Heart Standards Training Optical imaging

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Low dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss

arXiv

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arXiv 2017年

作者： Yang, Qingsong Yan, Pingkun Zhang, Yanbo Yu, Hengyong Shi, Yongyi Mou, Xuanqin Kalra, Mannudeep K. Zhang, Yi Sun, Ling Wang, Ge Department of Biomedical Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Department of Electrical and Computer Engineering University of Massachusetts Lowell LowellMA01854 United States Institute of Image Processing and Pattern Recognition Xian Jiaotong University Xian Shaanxi710049 China Department of Radiology Massachusetts General Hospital Harvard Medical School BostonMA United States College of Computer Science Sichuan Universiyt Chengdu610065 China Department of Radiology West China Hospital Sichuan University Chengdu610041 China

The continuous development and extensive use of CT in medical practice has raised a public concern over the associated radiation dose to the patient. Reducing the radiation dose may lead to increased noise and artifacts, which can adversely affect the radiologists judgement and confidence. Hence, advanced image reconstruction from low-dose CT data is needed to improve the diagnostic performance, which is a challenging problem due to its ill-posed nature. Over the past years, various low-dose CT methods have produced impressive results. However, most of the algorithms developed for this application, including the recently popularized deep learning techniques, aim for minimizing the mean-squared-error (MSE) between a denoised CT image and the ground truth under generic penalties. Although the peak signal-to-noise ratio (PSNR) is improved, MSE-or weighted-MSE-based methods can compromise the visibility of important structural details after aggressive denoising. This paper introduces a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity. The Wasserstein distance is a key concept of the optimal transport theory, and promises to improve the performance of GAN. The perceptual loss suppresses noise by comparing the perceptual features of a denoised output against those of the ground truth in an established feature space, while the GAN focuses more on migrating the data noise distribution from strong to weak statistically. Therefore, our proposed method transfers our knowledge of visual perception to the image denoising task and is capable of not only reducing the image noise level but also trying to keep the critical information at the same time. Promising results have been obtained in our experiments with clinical CT images. Copyright © 2017, The Authors. All rights reserved.

关键词： image denoising

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A simple but effective denoising algorithm in projection domain of CBCT 8th

A simple but effective denoising algorithm in projection dom...

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8th International Conference on image and Graphics, ICIG 2015

作者： Chang, Shaojie Zhang, Yanbo Bai, Ti Chen, Xi Xu, Qiong Mou, Xuanqin Institute of Image Processing and Pattern Recognition Xi’an Jiaotong University Xi’anShaanxi710049 China

ISBN: (纸本)9783319219776

There are growing concerns on the potential side effect of radiation, which could be decreased by lowering the tube current. However, this manner will lead to a degraded image since X-ray imaging is a quantum accumulation process. Great efforts have been devoted to alleviate this problem. In this paper, a simple Wiener filter was employed to denoise projection data in detector domain. And then an enhancement filter was exploited to strengthen small structures. As a consequence, this combination leaded to a reconstruction with good trade-off between noise and resolution. For the purpose of comparison, block-matching and 3D/4D denoising algorithm (BM3D/BM4D) were also adopted to denoise the projections. Experimental results demonstrated that the proposed algorithm could deliver a reconstruction with comparable quality as BM4D algorithm while better than that of BM3D denoising algorithm. Note that the propose algorithm has a much higher computation efficiency than BM3D/BM4D, and hence provides an insight into the clinical utility where real time is of high importance. © Springer International Publishing Switzerland 2015.

关键词： Noise abatement

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