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arXiv 2019年

作者： Qin, Yulei Chen, Mingjian Zheng, Hao Gu, Yun Shen, Mali Yang, Jie Huang, Xiaolin Zhu, Yue-Min Yang, Guang-Zhong Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Insa Lyon Lyon France Hamlyn Centre for Robotic Surgery Imperial College London London United Kingdom

Airway segmentation on CT scans is critical for pulmonary disease diagnosis and endobronchial navigation. Manual extraction of airway requires strenuous efforts due to the complicated structure and various appearance of airway. For automatic airway extraction, convolutional neural networks (CNNs) based methods have recently become the stateof-the-art approach. However, there still remains a challenge for CNNs to perceive the tree-like pattern and comprehend the connectivity of airway. To address this, we propose a voxel-connectivity aware approach named AirwayNet for accurate airway segmentation. By connectivity modeling, conventional binary segmentation task is transformed into 26 tasks of connectivity prediction. Thus, our AirwayNet learns both airway structure and relationship between neighboring voxels. To take advantage of context knowledge, lung distance map and voxel coordinates are fed into AirwayNet as additional semantic information. Compared to existing approaches, AirwayNet achieved superior performance, demonstrating the effectiveness of the network's awareness of voxel connectivity. Copyright © 2019, The Authors. All rights reserved.

关键词： Computerized tomography

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Adversarial attack type I: Cheat classifiers by significant changes

arXiv

引用

arXiv 2018年

作者： Tang, Sanli Huang, Xiaolin Chen, Mingjian Sun, Chengjin Yang, Jie Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China

Despite the great success of deep neural networks, the adversarial attack can cheat some well-trained classifiers by small permutations. In this paper, we propose another type of adversarial attack that can cheat classifiers by significant changes. For example, we can significantly change a face but well-trained neural networks still recognize the adversarial and the original example as the same person. Statistically, the existing adversarial attack increases Type II error and the proposed one aims at Type I error, which are hence named as Type II and Type I adversarial attack, respectively. The two types of attack are equally important but are essentially different, which are intuitively explained and numerically evaluated. To implement the proposed attack, a supervised variation autoencoder is designed and then the classifier is attacked by updating the latent variables using gradient information. Besides, with pretrained generative models, Type I attack on latent spaces is investigated as well. Experimental results show that our method is practical and effective to generate Type I adversarial examples on large-scale image datasets. Most of these generated examples can pass detectors designed for defending Type II attack and the strengthening strategy is only efficient with a specific type attack, both implying that the underlying reasons for Type I and Type II attack are different. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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Random Fourier features via fast surrogate leverage weighted sampling

arXiv

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arXiv 2019年

作者： Liu, Fanghui Huang, Xiaolin Chen, Yudong Yang, Jie Suykens, Johan A.K. Department of Electrical Engineering [ESAT-STADIUS KU Leuven Belgium Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China Institute of Medical Robotics Shanghai Jiao Tong University China School of Operations Research and Information Engineering Cornell University United States

In this paper, we propose a fast surrogate leverage weighted sampling strategy to generate refined random Fourier features for kernel approximation. Compared to the current state-of-the-art method that uses the leverage weighted scheme (Li et al. 2019), our new strategy is simpler and more effective. It uses kernel alignment to guide the sampling process and it can avoid the matrix inversion operator when we compute the leverage function. Given n observations and s random features, our strategy can reduce the time complexity for sampling from O(ns2 + s3) to O(ns2), while achieving comparable (or even slightly better) prediction performance when applied to kernel ridge regression (KRR). In addition, we provide theoretical guarantees on the generalization performance of our approach, and in particular characterize the number of random features required to achieve statistical guarantees in KRR. Experiments on several benchmark datasets demonstrate that our algorithm achieves comparable prediction performance and takes less time cost when compared to (Li et al. 2019). Copyright © 2019, The Authors. All rights reserved.

关键词： Benchmarking

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Deep model-based feature extraction for predicting protein subcellular localizations from bio-images

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Frontiers of Computer Science 2017年第2期11卷 243-252页

作者： Wei SHAO Yi DING Hong-Bin SHEN Daoqiang ZHANG School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing 211106 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China

Protein subcellular localization prediction is im- portant for studying the function of proteins. Recently, as significant progress has been witnessed in the field of mi- croscopic imaging, automatically determining the subcellular localization of proteins from bio-images is becoming a new research hotspot. One of the central themes in this field is to determine what features are suitable for describing the pro- tein images. Existing feature extraction methods are usually hand-crafted designed, by which only one layer of features will be extracted, which may not be sufficient to represent the complex protein images. To this end, we propose a deep model based descriptor （DMD） to extract the high-level fea- tures from protein images. Specifically, in order to make the extracted features more generic, we firstly trained a convolu- tion neural network （i.e., AlexNe0 by using a natural image set with millions of labels, and then used the partial parame- ter transfer strategy to fine-tnne the parameters from natural images to protein images. After that, we applied the Lasso model to select the most distinguishing features from the last fully connected layer of the CNN （Convolution Neural Net- work）, and used these selected features for final classifica- tions. Experimental results on a protein image dataset vali- date the efficacy of our method.

关键词： partial parameter transfer subcellular location classification feature extraction deep model convolution neural network

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Indefinite kernel spectral learning 30

Indefinite kernel spectral learning

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30th Benelux Conference on Artificial Intelligence, BNAIC 2018

作者： Mehrkanoon, Siamak Huang, Xiaolin Suykens, Johan A.K. Dept. of Data Science and Knowledge Engineering Maastricht University Netherlands Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China KU Leuven ESAT-STADIUS Kasteelpark Arenberg 10 LeuvenB-3001 Belgium

This paper introduces the indefinite learning in the framework of least squares support vector machines (LS-SVM). Here the analysis of the Multi-Class Semi-Supervised Kernel Spectral Clustering (MSS-KSC) model with indefinite kernels is provided. In indefinite MSS-KSC one finds the solution by solving a linear system of equations in the dual. In addition the use of the propped indefinite model for large scale data using Nyström approximation technique as well as unsupervised learning using Kernel Spectral Clustering are also discussed in the published full paper1. Experimental results on several real-life datasets are given to illustrate the efficiency of the proposed indefinite kernel spectral learning. © 2018 University of Groningen. All rights reserved.

关键词： Clustering algorithms

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Motion Object Detection Method based on Real-time Background Update under Complex Environment 2

Motion Object Detection Method based on Real-time Background...

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2017 IEEE 第2届先进信息技术,电子与自动化控制国际会议(IAEAC 2017)

作者： Zixiao Pan Mei Wang School of Automation Wuhan University of Technology Laboratory of Image Processing and Pattern Recognition Yantai Vocational College

For the fast requirement of motion object detection under complex environment, a background subtraction motion object detection method based on real-time background update is presented in this paper.

ISBN: (纸本)9781467389808

For the fast requirement of motion object detection under complex environment, a background subtraction motion object detection method based on real-time background update is presented in this paper.

关键词： Real-time systems Object detection Color Streaming media Buildings Morphology Surveillance

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Pulsar candidate selection using ensemble networks for FAST drift-scan survey

arXiv

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arXiv 2019年

作者： Wang, Hongfeng Zhu, Weiwei Guo, Ping Li, Di Feng, Sibo Yin, Qian Miao, Chenchen Tao, Zhenzhao Pan, Zhichen Wang, Pei Zheng, Xin Deng, Xiaodan Liu, Zhijie Xie, Xiaoyao Yu, Xuhong You, Shanping Zhang, Hui Image Processing and Pattern Recognition Laboratory College of Information Science and Technology Beijing Normal University Beijing100875 China CAS Key Laboratory of FAST Chinese Academy of Science Beijing100101 China School of Information Management Dezhou University Dezhou253023 China Image Processing and Pattern Recognition Laboratory School of Systems Science Beijing Normal University Beijing100875 China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Information and Computing Science Guizhou Province Guizhou Normal University Guiyang550001 China School of Physics and Electronic Science Guizhou Normal University Guiyang550001 China

The Commensal Radio Astronomy Five-hundred-meter Aperture Spherical radio Telescope (FAST) Survey (CRAFTS) utilizes the novel drift-scan commensal survey mode of FAST and can generate billions of pulsar candidate signals. The human experts are not likely to thoroughly examine these signals, and various machine sorting methods are used to aid the classification of the FAST candidates. In this study, we propose a new ensemble classification system for pulsar candidates. This system denotes the further development of the pulsar image-based classification system (PICS), which was used in the Arecibo Telescope pulsar survey, and has been retrained and customized for the FAST drift-scan survey. In this study, we designed a residual network model comprising 15 layers to replace the convolutional neural networks (CNNs) in PICS. The results of this study demonstrate that the new model can sort >96% of real pulsars to belong the top 1% of all candidates and classify > 1.6 million candidates per day using a dual-GPU and 24-core computer. This increased speed and efficiency can help to facilitate real-time or quasi-real-time processing of the pulsar-search data stream obtained from CRAFTS. In addition, we have published the labeled FAST data used in this study online, which can aid in the development of new deep learning techniques for performing pulsar searches. Copyright © 2019, The Authors. All rights reserved.

关键词： Pulsars

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An Adaptive Regulation Problem and Its Application

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International Journal of Automation and computing 2017年第2期14卷 221-228页

作者： Yuan Jiang Ji-Yang Dai Key Laboratory of Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang 330063 China School of Information Engineering Nanchang Hangkong University Nanchang 330063 China

This paper studies an adaptive regulation problem for the modified FitzHugh-Nagumo neuron model under external electrical stimulation. We first present the solution of the global robust output regulation problem for output feedback system with an uncertain exosystem which models the external electrical stimulation with unknown frequency and amplitude. Then, we show that the robust control problem for the modified FitzHugh-Nagumo neuron model can be formulated as the global robust output regulation problem and the solvability conditions for the output regulation problem for the modified FitzHugh-Nagumo neuron model are all satisfied. Then, we apply the obtained output regulation result to constructing an output feedback control law for the modified FitzHugh-Nagumo neuron model to achieve global stability of the closed-loop system in the presence of uncertain parameters and external stimulus. An example is given to show that the proposed algorithm can completely reject the external electrical stimulation.

关键词： Control theory Lyapunov methods internal model principle modified FitzHugh-Nagumo model.

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Enlightening the relationship between distribution and regression fitting 2nd

Enlightening the relationship between distribution and regre...

引用

2nd IFIP TC 12 International Conference on Intelligence Science, ICIS 2017

作者： Yu, Hang Yin, Qian Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing100875 China

ISBN: (纸本)9783319681207

Statistical distribution fitting and regression fitting are both classic methods to model data. There are slight connections and differences between them, as a result they outperform each other in different cases. A analysis model for processing natural data, say astronomical pulsar data in this paper, is proposed to improve data fitting method performance. Then the insight behind the comprehensive fitting model is given and discussed. © IFIP International Federation for Information processing 2017.

关键词： Data handling

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Weighting features before applying machine learning methods to pulsar search 2nd

Weighting features before applying machine learning methods ...

引用

2nd IFIP TC 12 International Conference on Intelligence Science, ICIS 2017

作者： Wang, Dayang Yin, Qian Wang, Hongfeng Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing100875 China

ISBN: (纸本)9783319681207

In recent years, different Artificial Intelligence methods have been applied to pulsar search, such as Artificial Neural Network method, PEACE Sorting Algorithm, Real-time Classification method. In this paper, Weighting Feature method before applying machine learning (ML) was proposed. We give weight to each feature according to its ability to distinguish pulsar and non-pulsar candidates. The ability is determined by the separation degree of the distribution of pulsars and non-pulsars on particular feature. And then use the ML methods to classify different types of candidates. The results show that this method is significant. The accuracy of identifying pulsars and modeling time were both improved after weighting. © IFIP International Federation for Information processing 2017.

关键词： Pulsars

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