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Data consistent artifact reduction for limited angle tomography with deep learning prior

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arXiv 2019年

作者： Huang, Yixing Preuhs, Alexander Lauritsch, Günter Manhart, Michael Huang, Xiaolin Maier, Andreas Pattern Recognition Lab Friedrich-Alexander-Universitt Erlangen-Nrnberg Erlangen91058 Germany Siemens Healthcare GmbH Forchheim91301 Germany Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Erlangen91058 Germany

Robustness of deep learning methods for limited angle tomography is challenged by two major factors: a) due to insufficient training data the network may not generalize well to unseen data;b) deep learning methods are sensitive to noise. Thus, generating reconstructed images directly from a neural network appears inadequate. We propose to constrain the reconstructed images to be consistent with the measured projection data, while the unmeasured information is complemented by learning based methods. For this purpose, a data consistent artifact reduction (DCAR) method is introduced: First, a prior image is generated from an initial limited angle reconstruction via deep learning as a substitute for missing information. Afterwards, a conventional iterative reconstruction algorithm is applied, integrating the data consistency in the measured angular range and the prior information in the missing angular range. This ensures data integrity in the measured area, while inaccuracies incorporated by the deep learning prior lie only in areas where no information is acquired. The proposed DCAR method achieves significant image quality improvement: for 120° cone-beam limited angle tomography more than 10% RMSE reduction in noise-free case and more than 24% RMSE reduction in noisy case compared with a state-of-the-art U-Net based method. Copyright © 2019, The Authors. All rights reserved.

关键词： image enhancement

Microarray camera image segmentation with Faster-RCNN

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Microarray camera image segmentation with Faster-RCNN

International Conference on Applied System Innovation (ICASI)

作者： Jiancheng Zou Rui Song Institute of Image Processing and Pattern Recognition North China University of Technology Shijingshan District Beijing China

A novel method of image segmentation based on Faster-RCNN and microarray camera (3 × 3) is proposed in this paper, we use the microarray camera to obtain nine images in the same scene and use nine array images to achieve image segmentation. Through the microarray camera, we can obtain images quickly. The result of microarray camera and high-speed camera is similar, the shooting interval is short and the disparity information between array images is small. We use the Faster-RCNN algorithm and the GrabCut algorithm to segment target for the array images. Because of the size of microarray camera is small, it can be widely used on portable devices in the further. In comparison to Faster-RCNN network, the experimental results show that the proposed method is more efficient.

关键词： Cameras Lenses image segmentation Calibration Convolution Proposals Feature extraction

EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks

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arXiv 2020年

作者： Meng, Lubin Huang, Jian Zeng, Zhigang Jiang, Xue Yu, Shan Jung, Tzyy-Ping Lin, Chin-Teng Chavarriaga, Ricardo Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Brainnetome Center National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China La Jolla CA United States Center for Advanced Neurological Engineering Institute of Engineering in Medicine Ucsd La Jolla CA United States Centre of Artificial Intelligence Faculty of Engineering and Information Technology University of Technology Sydney Australia Zhaw DataLab Zürich University of Applied Sciences Winterthur8401 Switzerland

Research and development of electroencephalogram (EEG) based brain-computer interfaces (BCIs) have advanced rapidly, partly due to deeper understanding of the brain and wide adoption of sophisticated machine learning approaches for decoding the EEG signals. However, recent studies have shown that machine learning algorithms are vulnerable to adversarial attacks. This article proposes to use narrow period pulse for poisoning attack of EEG-based BCIs, which is implementable in practice and has never been considered before. One can create dangerous backdoors in the machine learning model by injecting poisoning samples into the training set. Test samples with the backdoor key will then be classified into the target class specified by the attacker. What most distinguishes our approach from previous ones is that the backdoor key does not need to be synchronized with the EEG trials, making it very easy to implement. The effectiveness and robustness of the backdoor attack approach is demonstrated, highlighting a critical security concern for EEG-based BCIs and calling for urgent attention to address it. Copyright © 2020, The Authors. All rights reserved.

关键词： Electroencephalography

image synthesis with adversarial networks: A comprehensive survey and case studies

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arXiv 2020年

作者： Shamsolmoali, Pourya Zareapoor, Masoumeh Granger, Eric Zhou, Huiyu Wang, Ruili Emre Celebi, M. Yang, Jie Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Laboratoire d’imagerie De vision et d’intelligence artificielle École de technologie supérieure Montreal Canada School of Informatics University of Leicester United Kingdom School of Natural and Computational Sciences Massey University Auckland New Zealand Department of Computer Science University of Central Arkansas United States

Generative Adversarial Networks (GANs) have been extremely successful in various application domains such as computer vision, medicine, and natural language processing. Moreover, transforming an object or person to a desired shape become a well-studied research in the GANs. GANs are powerful models for learning complex distributions to synthesize semantically meaningful samples. However, there is a lack of comprehensive review in this field, especially lack of a collection of GANs loss-variant, evaluation metrics, remedies for diverse image generation, and stable training. Given the current fast GANs development, in this survey, we provide a comprehensive review of adversarial models for image synthesis. We summarize the synthetic image generation methods, and discuss the categories including image-to-image translation, fusion image generation, label-to-image mapping, and text-to-image translation. We organize the literature based on their base models, developed ideas related to architectures, constraints, loss functions, evaluation metrics, and training datasets. We present milestones of adversarial models, review an extensive selection of previous works in various categories, and present insights on the development route from the model-based to data-driven methods. Further, we highlight a range of potential future research directions. One of the unique features of this review is that all software implementations of these GAN methods and datasets have been collected and made available in one place at https://***/pshams55/GAN-CaseStudy. © 2020, CC BY-NC-ND.

关键词： Generative adversarial networks

Microarray Camera image Super-Resolution with Neural Network and Fusion of V-System

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Microarray Camera Image Super-Resolution with Neural Network...

International Conference on Computer Science & Education (ICCSE)

作者： Jiancheng Zou Honggen Zhang Institute of Image Processing and Pattern Recognition orth China University of Technology Beijing China

With the rapid development of artificial intelligence technology, more and more intelligent devices are beginning to be manufactured. At the same time, the demand of the image resolution is getting higher. Super-resolution of the image has become a research hotspot in the field of artificial intelligence. The same time, convolution neural networks have been successful in many fields of computer vision, including image super-resolution. Single image super-resolution has received a lot of attention and made good progress recently. However, there are few neural network models for multi-image super-resolution. Array images are a completely new experience and application for super-resolution. In this paper, we use the microarray camera (3*3) to get nine pictures in the same scene and use nine images to realize multiple images super-resolution. A super-resolution model combining software with hardware is proposed. The structure of image registration, V- system image fusion and convolution operation are constructed. The super-resolution model can extract the feature of the target image more efficiently, and the array camera has the characteristic of trading space for the time change. The model can be more accurate and efficient than single image super-resolution. In the PSNR and SSIM indexes, 1.01dB and 0.03 are increased respectively. We use ISO12233 camera resolution chart to detect the experimental pictures. In the experimental results, the resolution of the algorithm is the best.

关键词： image resolution Cameras Convolution Feature extraction Interpolation Neural networks image reconstruction

Learning In-Place Residual Homogeneity for image Detail Enhancement

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Learning In-Place Residual Homogeneity for Image Detail Enha...

IEEE International Conference on Acoustics, Speech and Signal processing

作者： He Jiang HuangKai Cai Jie Yang Institute of Pattern Recognition and Image Processing Shanghai Jiaotong University China

ISBN: (纸本)9781538646595

In this paper, we put forward and demonstrate a novel method in image and video detail enhancement-- in-place residual homogeneity (IP). In-place residual homogeneity is a regular law we find in testing different blocks in database, that is, residual blocks with slight different resolutions hold homogenous structures. By learning this homogeneity, we guess that it might be a good description of image's detail layer. Then images are enhanced by designed framework and accelerated by proposed fast in-place search method. Unlike most algorithms that need to adjust parameters by manual to get best performance, our approach is adaptive. Besides, many algorithms will change images' intensity, but our IP can keep natural images from over enhancement. Moreover, IP is also robust to low bit rate H.265 encoder and decoder system and runs faster than most popular methods. The last but not least, it can be easily FPGA implemented as well. Numbers of experiments testify that our algorithm is robust with good performance both subjectively and objectively.

关键词： In-place residual homogeneity detail enhancement fast in-place search FPGA implemented images Learning EIS IMPLEMENTATION PLANS algorithms homogeneity imagery (Psychotherapy) RESIDUAL optimum performance Postoperative Residual Curarization manual image Refractive surgery enhancement

Oracle character recognition by nearest neighbor classification with deep metric learning 15

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Oracle character recognition by nearest neighbor classificat...

15th IAPR International Conference on Document Analysis and recognition, ICDAR 2019

作者： Zhang, Yi-Kang Zhang, Heng Liu, Yong-Ge Yang, Qing Liu, Cheng-Lin National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences 95 Zhongguancun East Road Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China CAS Center for Excellence of Brain Science and Intelligence Technology Beijing China School of Computer & Information Engineering Anyang Normal University Henan China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education Henan China

ISBN: (纸本)9781728128610

Oracle character is one kind of the earliest hieroglyphics, which can be dated back to Shang Dynasty in china. Oracle character recognition is important for modern archaeology, ancient text understanding, and historical chronology, etc. To overcome the limitation and class imbalance of training data in oracle character recognition, we propose a classification method based on deep metric learning. We use a convolutional neural network (CNN) to map the character images to an Euclidean space where the distance between different samples can measure their similarities such that classification can be performed by the Nearest Neighbor (NN) rule. Because new categories are still being discovered in reality, our model enables the rejection of unseen categories and the configuration of new categories. To accelerate NN classification, we also propose a prototype pruning method with little loss of accuracy. The proposed method exceeds the state of the art on the public dataset Oracle-20K and outperforms CNN with softmax layer on a new dataset Oracle-AYNU. © 2019 IEEE.

关键词： Character recognition

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

Random Fourier features via fast surrogate leverage weighted sampling

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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