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arXiv 2019年

作者： Gong, Chen Liu, Tongliang Tang, Yuanyan Yang, Jian Yang, Jie Tao, Dacheng School of Computer Science and Engineering Nanjing University of Science and Technology Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University School of Software Faculty of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia Faculty of Science and Technology University of Macau Macau999078 China College of Computer Science Chongqing University Chongqing400000 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China School of Information Technologies Faculty of Engineering and Information Technologies University of Sydney J12/318 Cleveland St DarlingtonNSW2008 Australia

Different from the traditional supervised learning in which each training example has only one explicit label, Superset Label Learning (SLL) refers to the problem that a training example can be associated with a set of candidate labels, and only one of them is correct. Existing SLL methods are either regularization-based or instance-based, and the latter of which has achieved state-of-the-art performance. This is because the latest instance-based methods contain an explicit disambiguation operation that accurately picks up the groundtruth label of each training example from its ambiguous candidate labels. However, such disambiguation operation does not fully consider the mutually exclusive relationship among different candidate labels, so the disambiguated labels are usually generated in a nondiscriminative way, which is unfavorable for the instance-based methods to obtain satisfactory performance. To address this defect, we develop a novel Regularization approach for Instancebased Superset Label (RegISL) learning so that our instancebased method also inherits the good discriminative ability possessed by the regularization scheme. Specifically, we employ a graph to represent the training set, and require the examples that are adjacent on the graph to obtain similar labels. More importantly, a discrimination term is proposed to enlarge the gap of values between possible labels and unlikely labels for every training example. As a result, the intrinsic constraints among different candidate labels are deployed, and the disambiguated labels generated by RegISL are more discriminative and accurate than those output by existing instance-based algorithms. The experimental results on various tasks convincingly demonstrate the superiority of our RegISL to other typical SLL methods in terms of both training accuracy and test accuracy. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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An improved convex programming model for the inverse problem in intensity-modulated radiation therapy

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International Journal of Performability Engineering 2018年第5期14卷 871-884页

作者： Lan, Yihua Zhang, Xingang Zhang, Jianyang Wang, Yang Hung, Chih-Cheng School of Computer and Information Technology Nanyang Normal University Nanyang473061 China Institute of Image Processing and Pattern Recognition Nanyang Normal University Nanyang473061 China Radiology Department Central Hospital of Nanyang Nanyang473061 China Laboratory for Machine Vision and Security Research College of Computing and Software Engineering Kennesaw State University - Marietta Campus 1100 South Marietta Parkway MariettaGA30067-2896 United States

Intensity modulated radiation therapy technology (IMRT) is one of the main approaches in cancer treatment because it can guarantee the killing of cancer cells while optimally protecting normal tissue from complications. Inverse planning, which is the core component of the entire IMRT system, is mainly based on accurate mathematical modeling and associated fast solving methods. In inverse planning, the fluence map optimization, which considers the multi-leaf collimator (MLC) modulation, is the current research focus. Although the hitting constrain problem with the unidirectional movement of leaf-sweeping has been solved, our goal is to solve the hitting constrain problem with the bidirectional movement of leaf-sweeping. In this study, we propose a non-synchronized type to solve the hitting constrain problem with the bidirectional movement of leaf-sweeping schemes for IMRT. In solving this problem, a new mathematical model is proposed under the framework of convex programming. The advantage of the convex model is to avoid the uncertainty and inaccuracy that occurs in the non-convex programming solving process. Experimental results for two clinical testing cases show that under the same condition of total number of monitoring units, the new proposed model produces better dose distribution than those of the total variance and quadratic models. © 2018 Totem Publisher, Inc. All rights reserved.

关键词： Convex optimization

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Robust Visual Tracking Revisited: From Correlation Filter to Template Matching

arXiv

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arXiv 2019年

作者： Liu, Fanghui Gong, Chen Huang, Xiaolin Zhou, Tao Yang, Jie Tao, Dacheng Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China UBTECH Sydney Artificial Intelligence Centre School of Information Technologies Faculty of Engineering and Information Technologies University of Sydney 6 Cleveland St DarlingtonNSW2008 Australia

In this paper, we propose a novel matching based tracker by investigating the relationship between template matching and the recent popular correlation filter based trackers (CFTs). Compared to the correlation operation in CFTs, a sophisticated similarity metric termed "mutual buddies similarity" (MBS) is proposed to exploit the relationship of multiple reciprocal nearest neighbors for target matching. By doing so, our tracker obtains powerful discriminative ability on distinguishing target and background as demonstrated by both empirical and theoretical analyses. Besides, instead of utilizing single template with the improper updating scheme in CFTs, we design a novel online template updating strategy named "memory filtering" (MF), which aims to select a certain amount of representative and reliable tracking results in history to construct the current stable and expressive template set. This scheme is beneficial for the proposed tracker to comprehensively "understand" the target appearance variations, "recall" some stable results. Both qualitative and quantitative evaluations on two benchmarks suggest that the proposed tracking method performs favorably against some recently developed CFTs and other competitive trackers. Copyright © 2019, The Authors. All rights reserved.

关键词： Template matching

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Multi-Stage Suture Detection for Robot Assisted Anastomosis based on Deep Learning

Multi-Stage Suture Detection for Robot Assisted Anastomosis ...

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IEEE International Conference on Robotics and Automation

作者： Yang Hu Yun Gu Jie Yang Guang-Zhong Yang Imperial College London Hamlyn Centre for Robotic Surgery UK School of Biomedical Engineering and Institute of Image Processing & Pattern Recognition Imperial College London UK

The technique of robust suture detection is vital in many applications including trainee suturing skill evaluation, suture augmentation in robotic-assisted surgery and suture recognition for automatic suturing. Due to the complicated environment of surgery, the detection of a suture is challenged by high deformation and frequent occlusion. In this paper, we propose a deep multi-stage framework for suture detection. The fully convolutional neural networks are firstly used to predict a gradient map which not only serves as a segmentation mask, but also provides useful structure information for the following thread centerline reconstruction. An overlapping map is also predicted to improve the quality of the gradient map in self-intersection area. Based on the gradient map, multiple segments of the thread are extracted and linked to form the whole thread using a curvilinear structure detector. Experiments on two types of threads demonstrate that the proposed method is able to detect the thread with human level performance when the thread is no occlusion or under finite self-intersection.

关键词： Yarn Instruction sets Surgery Splines (mathematics) image reconstruction Robots Task analysis

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NCEM: Network structural similarity metric-based clustering for noisy cryo-EM single particle images

NCEM: Network structural similarity metric-based clustering ...

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Chinese Automation Congress

作者： Yin Shuo Biao Zhang Hong-Bin Shen Yang Yang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Department of Computer Science Shanghai Jiao Tong University Shanghai China

Cryo-EM single particle image reconstruction is currently a powerful technique for revealing the structure of biomacromolecules. Compared to traditional structural biology techniques like X-Ray, it requires fewer restrictions on specimens and is highly efficient with image processing tools. In this single particle reconstruction protocol, the ultimate goal is to identify different particle projection orientations. Since the picked single particle images are highly noisy, clustering is an important step to refrain noise by dividing images with similar projection angles into groups and averaging these images. The goal of clustering analysis is to assign similar particles into same class, so similarity measurement between particles is an important part in all clustering algorithms. Directly measuring the similarity of two particle images will be unreliable due to their low SNR. In this study, we propose a novel network structural similarity metric-based clustering algorithm NCEM for clustering the single particle images. We first construct a complex network for all particle images, where each node represents a particle. Then calculating the similarity between two nodes using structural similarity. This new network-based single particle image similarity metric has advantages over direct measurement for its noise resistance by using the structural information of the network. Our experiments on both artificial and experimental datasets demonstrate its effectiveness.

关键词： Cryo-EM Single particle image clustering Complex network Structural similarity

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Embedding bilateral filter in least squares for efficient edge-preserving image smoothing

arXiv

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arXiv 2018年

作者： Liu, Wei Zhang, Pingping Chen, Xiaogang Shen, Chunhua Huang, Xiaolin Yang, Jie Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China Dalian University of Technology China University of Shanghai for Science and Technology China University of Adelaide Australia

Edge-preserving smoothing is a fundamental procedure for many computer vision and graphic applications. This can be achieved with either local methods or global methods. In most cases, global methods can yield superior performance over local ones. However, local methods usually run much faster than global ones. In this paper, we propose a new global method that embeds the bilateral filter in the least squares model for efficient edge-preserving smoothing. The proposed method can show comparable performance with the state-of-the-art global method. Meanwhile, since the proposed method can take advantages of the efficiency of the bilateral filter and least squares model, it runs much faster. In addition, we show the flexibility of our method which can be easily extended by replacing the bilateral filter with its variants. They can be further modified to handle more applications. We validate the effectiveness and efficiency of the proposed method through comprehensive experiments in a range of applications. Copyright © 2018, The Authors. All rights reserved.

关键词： Nonlinear filtering

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Overexposure correction by mixed one-bit compressive sensing for C-Arm CT 1

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Workshops on image processing for the medicine, 2017

作者： Huang, Xiaolin Xia, Yan Huang, Yixing Hornegger, Joachim Maier, Andreas Pattern Recognition Lab Friedrich-Alexander-University Erlangen-Nürnberg Germany Department of Radiology Stanford University United States Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

ISBN: (数字)9783662543450

ISBN: (纸本)9783662543443

This paper proposes a novel method to deal with overexposure for C-arm CT reconstruction. The proposed method is based on recent progress of one bit compressive sensing (1bit-CS), which is to recover sparse signals from sign measurements. Overexposure could be regarded as a kind of sign information, thus the application of 1bit-CS to overexposure correction in CT reconstruction is expected. This method is evaluated on a phantom and its promising performance implies potential application on clinical data. © Springer-Verlag GmbH Deutschland 2017.

关键词： Compressed sensing

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Super-resolution reconstruction of color image based on microarray lens

Super-resolution reconstruction of color image based on micr...

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2017 IEEE International Conference on Applied System Innovation, ICASI 2017

作者： Zou, Jiancheng Zhang, Honggen Institute of Image Processing and Pattern Recognition North China University of Technology No. 5 Jinyuanzhuang Road Shijingshan District Beijing China

ISBN: (纸本)9781509048977

In this paper, we propose a novel method which combines with color microarray cameras and super-resolution algorithms. Compared with ordinary cameras, the array camera can capture more details of the image, so better quality low resolution images can be obtained. Array camera has been a great application in depth map extraction and image super-resolution reconstruction [3,4,5]. Pelican and Columbia University present Picam(4 × 4 microarray camera) synthesizes high resolution images along with a range monochrome image[3]. The images produced by most popular camera is color images, so the super resolution algorithm of the Picam can't be applied directly. According to the symmetry of the lens, we use 3 × 3 microarray light field camera which can capture color images. The color images component is extracted for super-resolution reconstruction. The algorithm more efficient compared with the traditional super-resolution algorithm. The advantages of hardware and algorithms can be applied to mobile devices such as mobile phone cameras. © 2017 IEEE.

关键词： Cameras

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Face recognition with Convolutional Neural Networks and subspace learning 2

Face Recognition with Convolutional Neural Networks and subs...

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2nd International Conference on image, Vision and Computing, ICIVC 2017

作者： Wan, Lihong Liu, Na Huo, Hong Tao, Fang Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China

ISBN: (纸本)9781509062379

Deep learning is widely used in computer vision. In this study, we present a new method based on Convolutional Neural Networks (CNN) and subspace learning for face recognition under two circumstances. A very deep CNN architecture called VGG-Face, which learned on a large scale database, is used as feature extractor to extract the activation vector of the fully connected layer in the CNN architecture. Then, two types of subspace learning methods, namely, linear discriminate analysis (LDA) and whitening principal component analysis (WPCA), are respectively introduced to learn the subspace of the activation vectors for face recognition under multiple samples per subject and single sample per subject circumstances. The goals of applying subspace learning to the activation vectors are obtaining compact representation (dimensionality reduction) and performance improvement. Experiments on two face databases (CMU PIE and FERET) demonstrate the effectiveness of VGG-Face + LDA and VGG-Face + WPCA, compared with state-of-the-art methods. © 2017 IEEE.

关键词： Principal component analysis

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Distributed cubature Kalman filter based on observation bootstrap sampling

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High Technology Letters 2016年第2期22卷 142-147页

作者：胡振涛 Hu Yumei Zheng Shanshan Li Xian Guo Zhen Institute of Image Processing and Pattern Recognition Henan University

Aiming at the adverse effect caused by observation noise on system state estimation precision,a novel distributed cubature Kalman filter(CKF) based on observation bootstrap sampling is ***,combining with the extraction and utilization of the latest observation information and the prior statistical information from observation noise modeling,an observation bootstrap sampling strategy is *** objective is to deal with the adverse influence of observation uncertainty by increasing observations ***,the strategy is dynamically introduced into the cubature Kalman filter,and the distributed fusion framework of filtering realization is *** filtering precision is obtained by promoting observation reliability without increasing the hardware cost of observation *** analysis and simulation results show the proposed algorithm feasibility and effectiveness.

关键词： state estimation cubature Kalman filter （CKF） observation bootstrap sampling distributed weighted fusion

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