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Correction to: Internet gaming disorder: deficits in functional and structural connectivity in the ventral tegmental area-Accumbens pathway

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Brain imaging and behavior 2019年第4期13卷 1182页

作者： Ruonan Wang Min Li Meng Zhao Dahua Yu Yu Hu Corinde E Wiers Gene-Jack Wang Nora D Volkow Kai Yuan School of Life Science and Technology Xidian University Xi'an Shaanxi 710071 People's Republic of China. Engineering Research Center of Molecular and Neuro Imaging Ministry of Education Xi'an People's Republic of China. Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing School of Information Engineering Inner Mongolia University of Science and Technology Baotou Inner Mongolia 014010 People's Republic of China. National Institute on Alcoholism and Alcohol Abuse National Institutes of Health Bethesda MD 20892 USA. National Institute on Drug Abuse National Institutes of Health Bethesda MD 20892 USA. School of Life Science and Technology Xidian University Xi'an Shaanxi 710071 People's Republic of China. kyuan@***. Engineering Research Center of Molecular and Neuro Imaging Ministry of Education Xi'an People's Republic of China. kyuan@***. Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing School of Information Engineering Inner Mongolia University of Science and Technology Baotou Inner Mongolia 014010 People's Republic of China. kyuan@***. National Institute on Alcoholism and Alcohol Abuse National Institutes of Health Bethesda MD 20892 USA. kyuan@***. Guangxi Key Laboratory of Multi-Source Information Mining and Security Guangxi Normal University Guilin People's Republic of China. kyuan@***.

The original version of this article contained mistakes, and the authors would like to correct them. The correct details are given below.

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Nonconvex penalties with analytical solutions for one-bit compressive sensing

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arXiv 2017年

作者： Huang, Xiaolin Yan, Ming Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University MOE Key Laboratory of System Control and Information Processing Shanghai200240 China Department of Computational Mathematics Science and Engineering Department of Mathematics Michigan State University East LansingMI48824 United States

One-bit measurements widely exist in the real world and can be used to recover sparse signals. This task is known as one-bit compressive sensing (1bit-CS). In this paper, we propose novel algorithms based on both convex and nonconvex sparsity-inducing penalties for robust 1bit-CS. We consider the dual problem, which has only one variable and provides a sufficient condition to verify whether a solution is globally optimal or not. For positive homogeneous penalties, a globally optimal solution can be obtained in two steps: a proximal operator and a normalization step. For other penalties, we solve the dual problem, and it needs to evaluate the proximal operators for many times. Then we provide fast algorithms for finding analytical solutions for three penalties: minimax concave penalty (MCP), 0norm, and sorted 1penalty. Specifically, our algorithm is more than 200 times faster than the existing algorithm for MCP. Its efficiency is comparable to the algorithm for the 1penalty in time, while its performance is much better than 1. Among these penalties, sorted 1is most robust to noise in different settings. Copyright © 2017, The Authors. All rights reserved.

关键词： Compressed sensing

Mixed one-bit compressive sensing with application to overexposure correction for CT reconstruction

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arXiv 2017年

作者： Huang, Xiaolin Xia, Yan Shi, Lei Huang, Yixing Yan, Ming Hornegger, Joachim Maier, Andreas Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Department of Radiology Stanford University CA United States School of Mathematical Sciences Fudan University Shanghai China Pattern Recognition Lab of Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Computational Mathematics Science and Engineering Michigan State University MI United States

When a measurement falls outside the quantization or measurable range, it becomes saturated and cannot be used in classical reconstruction methods. For example, in C-arm angiography systems, which provide projection radiography, fluoroscopy, digital subtraction angiography, and are widely used for medical diagnoses and interventions, the limited dynamic range of C-arm flat detectors leads to overexposure in some projections during an acquisition, such as imaging relatively thin body parts (e.g., the knee). Aiming at overexposure correction for computed tomography (CT) reconstruction, we in this paper propose a mixed one-bit compressive sensing (M1bitCS) to acquire information from both regular and saturated measurements. This method is inspired by the recent progress on one-bit compressive sensing, which deals with only sign observations. Its successful applications imply that information carried by saturated measurements is useful to improve recovery quality. For the proposed M1bit-CS model, alternating direction methods of multipliers is developed and an iterative saturation detection scheme is established. Then we evaluate M1bit-CS on one-dimensional signal recovery tasks. In some experiments, the performance of the proposed algorithms on mixed measurements is almost the same as recovery on unsaturated ones with the same amount of measurements. Finally, we apply the proposed method to overexposure correction for CT reconstruction on a phantom and a simulated clinical image. The results are promising, as the typical streaking artifacts and capping artifacts introduced by saturated projection data are effectively reduced, yielding significant error reduction compared with existing algorithms based on extrapolation. Copyright © 2017, The Authors. All rights reserved.

关键词： Compressed sensing

Fast deep mating for portrait animation on mobile phone

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arXiv 2017年

作者： Zhu, Bingke Chen, Yingying Wang, Jinqiao Liu, Si Zhang, Bo Tang, Ming National Lab of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China North China University of Technology Beijing China

image matting plays an important role in image and video editing. However, the formulation of image matting is inherently ill-posed. Traditional methods usually employ interaction to deal with the image matting problem with trimaps and strokes, and cannot run on the mobile phone in real-time. In this paper, we propose a real-time automatic deep matting approach for mobile devices. By leveraging the densely connected blocks and the dilated convolution, a light full convolutional network is designed to predict a coarse binary mask for portrait image. And a feathering block, which is edge-preserving and matting adaptive, is further developed to learn the guided flter and transform the binary mask into alpha matte. Finally, an automatic portrait animation system based on fast deep matting is built on mobile devices, which does not need any interaction and can realize real-time matting with 15 fps. The experiments show that the proposed approach achieves comparable results with the state-of-the-art matting solvers. Copyright © 2017, The Authors. All rights reserved.

关键词： Cellular telephones

Constructing multi-modality and multi-classifier radiomics predictive models through reliable classifier fusion

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arXiv 2017年

作者： Zhou, Zhiguo Zhou, Zhi-Jie Hao, Hongxia Li, Shulong Chen, Xi Zhang, You Folkert, Michael Wang, Jing Department of Radiation Oncology UT Southwestern Medical Center DallasTX United States High-Tech Institute of Xi'an Xi'an China Department of Information and Control Engineering Xi'an University of Technology Xi'an China School of Computer Science and Technology Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an China School of Biomedical Engineering Southern Medical Unversity Guangzhou China Institute of Image Processing and Pattern Recognition Xi'an Jiaotong Unversity Xi'an China

Radiomics aims to extract and analyze large numbers of quantitative features from medical images and is highly promising in staging, diagnosing, and predicting outcomes of cancer treatments. Nevertheless, several challenges need to be addressed to construct an optimal radiomics predictive model. First, the predictive performance of the model may be reduced when features extracted from an individual imaging modality (e.g. PET, CT, MRI) are blindly combined into a single predictive model. Second, because many different types of classifiers are available to construct a predictive model, selecting an "optimal" classifier for a particular application is still challenging. In this work, we developed multi-modality and multi-classifier radiomics predictive models that address the aforementioned issues in currently available models. Specifically, a new reliable classifier fusion strategy was proposed to optimally combine output from different modalities and classifiers. In this strategy, modality-specific classifiers were first trained, and an analytic evidential reasoning (ER) rule was developed to fuse the output score from each modality to construct an optimal predictive model. One public datasets and two clinical case studies were performed to validate model performance. The experimental results indicated that the proposed ER rule based radiomics models outperformed the traditional models that rely on a single classifier or simply use combined features from different modalities. Copyright © 2017, The Authors. All rights reserved.

关键词： Diagnosis

Fault detection and isolation of sensor in time-delay systems based on space geometry method

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Fault detection and isolation of sensor in time-delay system...

IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Dianfeng Qiao Qianshuai Cheng Yandong Hou Institute of Image Processing and Pattern Recognition Henan University Kaifeng China

ISBN: (纸本)9781467386456

This paper investigates sensor fault problems in time-delay systems with uncertain disturbances. Using the measurement equation, the sensor fault can be translated into the state inputs. Subsequently, a cluster of residual generators is designed by employing the space geometric method. The corresponding filter parameters are obtained based on the space geometric approach, then using H ∞ optimization technique reduce the effects of disturbance inputs on the residuals, at the same time, the residual generator is designed so that the residual signals and sensor faults satisfy one to one correspondence, which can be used to detect and isolate the sensor faults in time-delay systems with disturbance. Simulation results suggest the effectiveness and robustness of our proposed approach.

关键词： Conferences Industrial electronics Decision support systems Fault detection Optimization

Unsupervised feature selection algorithm based on sparse representation 3

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Unsupervised feature selection algorithm based on sparse rep...

2016 3rd International Conference on Systems and Informatics, ICSAI 2016

作者： Cui, Guoqing Yang, Jie Zareapoor, Masoumeh Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Minhang District Shanghai China Jiechen Wang Complex Network and Control Lab Shanghai Jiao Tong University Minhang District Shanghai China

ISBN: (纸本)9781509055210

Feature selection, as a preprocessing step to machine learning, plays a pivotal role in removing irrelevant data, reducing dimensionality and improving performance evaluations. Recent years, sparse representation has become a useful tool for both supervised and unsupervised feature selection. So far, most of these algorithms still have many problems such as large computation load, performance with poor stability. Thus, this paper proposes a new unsupervised feature selection algorithm via sparse representation (UFSSR), with respect to efficiency and effectiveness. Firstly, this paper reconstructs part of data matrix via sparse representation, which makes the proposed algorithm be robust and independent of domain knowledge. Then, to reduce the reconstruction error, a new feature evaluation function is given to rank all features. Theoretical analysis and experiments compared with many popular algorithms on a set of datasets demonstrate the improvements brought by UFSSR. © 2016 IEEE.

关键词： Feature Selection

Correlation filter tracking via bootstrap learning

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Correlation filter tracking via bootstrap learning

IEEE International Conference on image processing

作者： Kunqi Gu Tao Zhou Fanghui Liu Jie Yang Yu Qiao Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

In recent years, correlation filter based trackers outperform better than other trackers. Nevertheless, they only employ one feature and a single kernel, so they are usually not robust in complex scenes. In this paper, we derive a multi-feature and multi-kernel correlation filter based tracker which fully takes advantage of the invariance-discriminative power spectrums of various features and kernels to further improve the performance. A novel bootstrap learning method is utilized to obtain a strong classifier by fusing these weak kernel correlation filters (KCFs). Moreover, a new target scale estimation strategy is incorporated into our framework. The efficient and effective scale estimation method is based on target dictionary representation. The proposed method is tested on several videos and compared with seven state-of-the-art methods. Experimental results have provided further support to the effectiveness and robustness of the proposed method.

关键词： Kernel Target tracking Correlation Computational modeling Boosting Robustness Object tracking

An interactive visual user interface model for multimedia open annotation tools

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International Journal of Multimedia and Ubiquitous Engineeri...

International Journal of Multimedia and Ubiquitous Engineering 2016年第7期11卷 103-118页

作者： Xu, Zhuoming Guo, Wenping Wu, Ting Ni, Lixian College of Computer and Information Hohai University Nanjing210098 China Institute of Image Processing & Pattern Recognition Taizhou University Taizhou317000 China

The W3C Open Annotation Community Group’s Open Annotation (OA) Data Model is emerging as a standardized Web Annotation Data Model. We argue that a friendly interactive visual user interface for OA Data Model based annotation tools, which is largely based on an effective user interface model, plays an essential role in the usability of the tools. In this paper we propose an Interactive Visual User Interface Model for Open Annotations (or simply IVUIM4OA), which allows the annotating user to dynamically transform OA data into a visual representation and to interactively manipulate the visual objects to modify the OA data or produce new one. IVUIM4OA is an adaptation of a generic information visualization model, the Data State Reference Model, for the specifics of the visualization of OA data;it is further extended with view operators that enable the user to interact with the visual objects. We implemented a prototype multimedia annotation tool on the basis of IVUIM4OA and conducted case study experiments with the prototype tool. The implementation and experimental results show that the proposed IVUIM4OA is feasible and works well with the OA Data Model, in such a way that the annotating user can obtain a better understanding of and more intuitive interaction with the annotation data. © 2016 SERSC.

关键词： Human computer interaction