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An image encryption scheme based on three-dimensional Brownian motion and chaotic system

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Chinese Physics B 2017年第2期26卷 99-113页

作者： Xiu-Li Chai Zhi-Hua Gan Ke Yuan l Yang Lu Yi-Ran Chen School of Computer and Information Engineering Institute of Image Processing and Pattern Recognition Henan University Kaifeng 475004 China Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh PA 15261 USA School of Software Henan University Kaifeng 475004 China Research Dep＇ ~tment Henan University Kaifeng 475004 China

At present, many chaos-based image encryption algorithms have proved to be unsafe, few encryption schemes permute the plain images as three-dimensional（3D） bit matrices, and thus bits cannot move to any position, the movement range of bits are limited, and based on them, in this paper we present a novel image encryption algorithm based on 3D Brownian motion and chaotic systems. The architecture of confusion and diffusion is adopted. Firstly, the plain image is converted into a 3D bit matrix and split into sub blocks. Secondly, block confusion based on 3D Brownian motion（BCB3DBM）is proposed to permute the position of the bits within the sub blocks, and the direction of particle movement is generated by logistic-tent system（LTS）. Furthermore, block confusion based on position sequence group（BCBPSG） is introduced, a four-order memristive chaotic system is utilized to give random chaotic sequences, and the chaotic sequences are sorted and a position sequence group is chosen based on the plain image, then the sub blocks are confused. The proposed confusion strategy can change the positions of the bits and modify their weights, and effectively improve the statistical performance of the algorithm. Finally, a pixel level confusion is employed to enhance the encryption effect. The initial values and parameters of chaotic systems are produced by the SHA 256 hash function of the plain image. Simulation results and security analyses illustrate that our algorithm has excellent encryption performance in terms of security and speed.

关键词： image encryption logistic-tent system（LTS） memristive chaotic system three-dimensional（3D） Brownian motion

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

A FAST RADIO BURST DISCOVERED IN FAST DRIFT SCAN SURVEY

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arXiv 2020年

作者： Zhu, Weiwei Li, Di Luo, Rui Miao, Chenchen Zhang, Bing Spitler, Laura Lorimer, Duncan Kramer, Michael Champion, David Yue, Youling Cameron, Andrew Cruces, Marilyn Duan, Ran Feng, Yi Han, Jun Hobbs, George Niu, Chenhui Niu, Jiarui Pan, Zhichen Qian, Lei Shi, Dai Tang, Ningyu Wang, Pei Wang, Hongfeng Yuan, Mao Zhang, Lei Zhang, Xinxin Cao, Shuyun Feng, Li Gan, Hengqian Gao, Long Gu, Xuedong Guo, Minglei Hao, Qiaoli Huang, Lin Huang, Menglin Jiang, Peng Jin, Chengjin Li, Hui Li, Qi Li, Qisheng Liu, Hongfei Pan, Gaofeng Peng, Bo Qian, Hui Shi, Xiangwei Song, Jinyuo Song, Liqiang Sun, Caihong Sun, Jinghai Wang, Hong Wang, Qiming Wang, Yi Xie, Xiaoyao Yan, Jun Yang, Li Yang, Shimo Yao, Rui Yu, Dongjun Yu, Jinglong Zhang, Chengmin Zhang, Haiyan Zhang, Shuxin Zheng, Xiaonian Zhou, Aiying Zhu, Boqin Zhu, Lichun Zhu, Ming Zhu, Wenbai Zhu, Yan CAS Key Laboratory of FAST NAOC Chinese Academy of Sciences Beijing100101 China University of Chinese Academy of Sciences Beijing100049 China CSIRO Astronomy and Space Science PO Box 76 EppingNSW1710 Australia Department of Physics and Astronomy University of Nevada Las Vegas Las VegasNV89154 United States National Astronomical Observatories Chinese Academy of Sciences Beijing100012 China Max-Planck-Institut fr Radioastronomie Auf dem Hgel 69 BonnD-53121 Germany Department of Physics and Astronomy West Virginia University P.O. Box 6315 MorgantownWV26506 United States Center for Gravitational Waves and Cosmology Chestnut Ridge Research Building MorgantownWV26505 United States Image Processing and Pattern Recognition Laboratory School of Artificial Intelligence Beijing Normal University Beijing100875 China School of Information Management Dezhou University Dezhou253023 China Institute for Astronomical Science Dezhou University Dezhou253023 China

We report the discovery of a highly dispersed fast radio burst, FRB 181123, from an analysis of ∼1500 hr of drift-scan survey data taken using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The pulse has three distinct emission components, which vary with frequency across our 1.0–1.5 GHz observing band. We measure the peak flux density to be > 0.065 Jy and the corresponding fluence > 0.2 Jy ms. Based on the observed dispersion measure of 1812 cm−3 pc, we infer a redshift of ∼ 1.9. From this, we estimate the peak luminosity and isotropic energy to be . 2 × 1043 erg s−1 and . 2 × 1040 erg, respectively. With only one FRB from the survey detected so far, our constraints on the event rate are limited. We derive a 95% confidence lower limit for the event rate of 900 FRBs per day for FRBs with fluences > 0.025 Jy ms. We performed follow-up observations of the source with FAST for four hours and have not found a repeated burst. We discuss the implications of this discovery for our understanding of the physical mechanisms of FRBs. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

MMFNet: A Multi-modality MRI Fusion Network for Segmentation of Nasopharyngeal Carcinoma

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arXiv 2018年

作者： Chen, Huai Qi, Yuxiao Yin, Yong Li, Tengxiang Liu, Xiaoqing Li, Xiuli Gong, Guanzhong Wang, Lisheng Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai200240 China Shandong Cancer Hospital Affiliated to Shandong University Jinan250117 China Deepwise AI Lab China

Segmentation of nasopharyngeal carcinoma (NPC) from Magnetic Resonance images (MRI) is a crucial prerequisite for NPC radiotherapy. However, manually segmenting of NPC is time-consuming and labor-intensive. Additionally, single-modality MRI generally cannot provide enough information for its accurate delineation. Therefore, a multi-modality MRI fusion network (MMFNet), which is a novel framework to fuse information from multi-modality medical images, is proposed to utilize MRI of T1, T2 and contrast-enhanced T1 to complete accurate segmentation of NPC. The backbone of MMFNet is designed as a multi-encoder-based network, consisting of several encoders to capture modality-specific features and one decoder to obtain fused features for NPC segmentation. A fusion block is presented to effectively fuse multi-source features. It contains a 3D Convolutional Block Attention Module (3D-CBAM), recalibrating low-level features captured from modality-specific encoders to highlight both informative features and regions of interest (ROIs), and a residual fusion block (RFBlock), which fuses re-weighted features to keep balance between fused ones and high-level features from decoder. Moreover, in order to make full mining of individual information from multi-modality MRI, a training strategy named self-transfer is proposed to utilize pre-trained modality-specific encoders to initialize multi-encoder-based network. The proposed method based on multi-modality MRI can effectively segment NPC and its advantages are validated by extensive experiments. Copyright © 2018, The Authors. All rights reserved.

关键词： Convolution

Face recognition with Convolutional Neural Networks and subspace learning 2

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Face Recognition with Convolutional Neural Networks and subs...

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

A novel panoramic image stitching algorithm based on ORB

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A novel panoramic image stitching algorithm based on ORB

2017 IEEE International Conference on Applied System Innovation, ICASI 2017

作者： Wang, Maosen Niu, Shaozhang Yang, Xuan Beijing Key Lab of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications Beijing100876 China Institute of Image Processing and Pattern Recognition North China University of Technology No.5 Jinyuanzhuang Road Shijingshan District Beijing China

ISBN: (纸本)9781509048977

image stitching technique is to integrate multiple images with overlapping regions into a complete image with a wide viewing angle, less distortion, and no obvious suture. image stitching could be used for global positioning and robot autonomous navigation without changing the hardware. SIFT feature and SURF feature are the classical algorithm in the image stitching. But they have the long time-consuming problem. Facing this problem, a novel fast image stitching algorithm based on ORB (Oriented FAST and Rotated BRIEF) features is proposed in this paper. The algorithm firstly selects ORB algorithm which adds the direction information to the FAST detector for image feature extracting and matching. Secondly, RASANC (Random Sample Consensus) algorithm is used to eliminate false matching points. Finally, weighted average method is used to speed up the image fusion. Experimental results show that the stitching effect of the algorithm in this paper is the same as that of SIFT algorithm and SURF algorithm. The two obvious advantages of our algorithm include: (1) stitching speed is much faster than that of Shift and Surf;(2)The algorithm is robust to external parameters of camera. So the proposed algorithm is hopefully applied to some handed electrical devices. © 2017 IEEE.

关键词： image fusion

Convolution neural network for automatic facial expression recognition

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Convolution neural network for automatic facial expression r...

2017 IEEE International Conference on Applied System Innovation, ICASI 2017

作者： Chen, Xiaoguang Yang, Xuan Wang, Maosen Zou, Jiancheng Institute of Image Processing and Pattern Recognition North China University of Technology No.5 Jinyuanzhuang Road Shijingshan District Beijing China School of Compute Science and Techonology Beijing University of Posts and Telecommunications No.10 Xitucheng Road Haidian District Beijing China

ISBN: (纸本)9781509048977

Facial expression recognition is a hot research direction of pattern recognition and computer vision. It has been increasingly used in artificial intelligence, human-computer interaction and security monitoring in recent years. Convolution neural network (CNN) as a depth learning architecture can extract the essential features of the image, and in the case of large changes in shooting conditions, its effect is better than the traditional methods of Support Vector Machines(SVM) and Principal Component Analysis(PCA). Therefore, an improved method of facial expression recognition based on CNN is proposed in this paper. The purpose is to classify each facial image as one of the seven facial expressions considered in this study. According to the characteristics of facial expression recognition, a new convolution neural network structure is designed which uses convolution kernel to extract implicit features and max pooling to reduce the dimensions of the extracted implicit features. In comparison to AlexNet network, we can improve the recognition accuracy about 4% higher on the CK+ facial expression database by the aid of Batch Normalization (BN) layer to our network. A facial expression recognition system is constructed in this paper for the convenience of application, and the experimental results show that the system could reach the real-Time needs. © 2017 IEEE.

关键词： Principal component analysis

Fast signal recovery from saturated measurements by linear loss and nonconvex penalties

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arXiv 2018年

作者： He, Fan Huang, Xiaolin Liu, Yipeng Yan, Ming Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University The MOE Key Laboratory of System Control and Information Processing Shanghai200240 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China The Department of Computational Mathematics Science and Engineering Michigan State University MI United States

Sign information is the key to overcoming the inevitable saturation error in compressive sensing systems, which causes information loss and results in bias. For sparse signal recovery from saturation, we propose to use a linear loss to improve the effectiveness from existing methods that utilize hard constraints/hinge loss for sign consistency. Due to the use of linear loss, an analytical solution in the update progress is obtained, and some nonconvex penalties are applicable, e.g., the minimax concave penalty, the 0 norm, and the sorted 1 norm. Theoretical analysis reveals that the estimation error can still be bounded. Generally, with linear loss and nonconvex penalties, the recovery performance is significantly improved, and the computational time is largely saved, which is verified by the numerical experiments. Copyright © 2018, The Authors. All rights reserved.

关键词： Compressed sensing

PILAE: A non-gradient descent learning scheme for deep feedforward neural networks

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arXiv 2018年

作者： Guo, Ping Wang, Ke Zhou, XiuLing The Image Processing & Pattern Recognition Lab. School of Systems Science Beijing Normal University Beijing100875 China The School of Information Engineering Zhengzhou University Zhengzhou450001 China The Department of Technology and Industry Development Beijing City University Beijing100083 China

In this work, a non-gradient descent learning (NGDL) scheme was proposed for deep feedforward neural networks (DNN). It is known that an autoencoder can be used as the building blocks of the multi-layer perceptron (MLP) DNN, the MLP is taken as an example to illustrate the proposed scheme of pseudoinverse learning algorithm for autoencoder (PILAE) in this paper. The PILAE with low rank approximation is a NGDL algorithm, and the encoder weight matrix is set to be the low rank approximation of the pseudoinverse of the input matrix, while the decoder weight matrix is calculated by the pseudoinverse learning algorithm. It is worth to note that only very few network structure hyper-parameters need to be tuned compared with classical gradient descent learning algorithm. Hence, the proposed algorithm could be regarded as a quasi-automated training algorithm which could be utilized in automated machine learning field. The experimental results show that the proposed learning scheme for DNN could achieve better performance on considering the tradeoff between training efficiency and classification accuracy. Copyright © 2018, The Authors. All rights reserved.

关键词： Feedforward neural networks