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IEEE Nuclear Science Symposium

作者： Lan Shi Martin Berger Bastian Bier Christopher Soell Juergen Roeber Rebecca Fahrig Bjoern Eskofier Andreas Maier Jennifer Maier Department of Electrical Engineering Friedrich-Alexander-University Erlangen Nuremberg Germany Department of Computer Science Pattern Recognition Lab Siemens Healthcare GmbH Radiological Sciences Laboratory Stanford University CA

Flat-Panel C-arm Computed Tomography (CT) suffers from pixel saturation due to the detector's limited dynamic range. We describe a novel approach of analog, non-linear tone mapping (TM) for preventing detector saturation. An analog TM operator (TMO) applies a non-linear transformation in a CMOS (complementary metal-oxide semiconductor) sensor and its inverse TMO based on 14-bit digital raw data. This is done in order to prevent overexposure and to enhance image quality to 32 bits. The method was applied to the cases of low-contrast head imaging and to that of imaging both knees. Cone-beam projection data with and without overexposure was simulated for a 200 degree short-scan of the knees and a 360 degree full-scan of a Forbild head phantom. The results show an increased correlation coefficient with respect to ground truth of 0.99 for TMO compared to 0.96 for overexposed knee data and a higher low-contrast visibility (CC=0.99) compared to linear quantization (CC=0.97).

关键词： Computed tomography Detectors Knee Image reconstruction Head Phantoms

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Corrigendum to “Road detection algorithm for Autonomous Navigation Systems based on dark channel prior and vanishing point in complex road scenes” [Robot. Auton. Syst. 85 (2016) 1–11]

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Robotics and Autonomous Systems 2017年 88卷 202-202页

作者： Yong Li Weili Ding Xuguang Zhang Zhaojie Ju Laboratory of Pattern Recognition and Intelligent Systems Key Laboratory of Industrial Computer Control Engineering of Hebei Province Department of Automation Institute of Electrical Engineering Yanshan University Qinghuangdao Hebei 066004 China College of Information Science and Engineering Northeastern University Shenyang Liaoning 110004 China School of Computing University of Portsmouth PO1 3HE UK

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LSSLP – Local structure sensitive label propagation

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Information Sciences 2016年 332卷 19-32页

作者： Zhenfeng Zhu Jian Cheng Yao Zhao Jieping Ye Institute of Information Science Beijing Jiaotong University Beijing 100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing 100044 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences (CAS) 100190 China Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI 48109-2218 USA

Label propagation is an approach to iteratively spread the prior state of label confidence associated with each of samples to its neighbors until achieving a global convergence state. Such process has been shown to hold close connection with a general graph-based regularization framework. Within this framework, a closed- form linear system can be built to carry out label propagation. In this paper, to address several issues inherent with previous graph-based label propagation framework, we propose a reformulated one, i.e., local structure sensitive label propagation ( LSSLP ). By associating each graph vertex with a local structure sensitive tuning factor, the empirical loss error on each vertex can be controlled preferably to keep consistent with the commonly preconditioned ‘ cluster assumption ’ of data structure. Out of consideration for information conservation, we relax the state conservation constraint of label confidence from labeled samples proposed by Belkin et al. (2004) to a more general form. Meanwhile, an inverse-warping procedure is incorporated into the proposed local structure sensitive label propagation framework to maintain large and stable enough classification margin. Based on the felicitous inversion technique for blocked matrix, we extend LSSLP to its incremental and inductive versions and also present computationally efficient implementation of it. Experimental results demonstrate the performance of the reformulated regularization framework for label propagation is much competitive.

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A convolutional neural network combined with aggregate channel feature for face detection 6

A convolutional neural network combined with aggregate chann...

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6th International Conference on Wireless, Mobile and Multi-Media, ICWMMN 2015

作者： Wang, Shuo Yang, Bin Lei, Zhen Wan, Jun Li, Stan Z. School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China China Research and Development Center for Internet of Thing China Center for Biometrics and Security Research National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781849199940

Face detection has been studied intensively over the past several decades and achieved great improvements via convolutional neural network (CNN) which has greatly improved the performance in image classification and object detection. In this paper, similar to the idea of R-CNN [1], we present a new method that combines the aggregate channel features (ACF) [2] and CNN for face detection. The proposed method uses ACF to select the possible human face regions and then trains a CNN model to filter out non-face candidates. Then we merge the results of ACF and CNN to get the final detection window(s). Evaluations on two popular face detection benchmark datasets show that our method outperforms the ACF method and has achieved competitive performance against the state-of-the-art algorithms.

关键词： Aggregates

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A convolutional neural network combined with aggregate channel feature for face detection

A convolutional neural network combined with aggregate chann...

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6th International Conference on Wireless, Mobile and Multi-Media (ICWMMN 2015)

作者： Shuo Wang Bin Yang Zhen Lei Jun Wan Stan Z. Li School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China China Research and Development Center for Internet of Thing China

ISBN: (纸本)9781510822030

关键词： CNN ACF Face detection Facial recognition CLEFT PALATE, CARDIAC DEFECT, GENITAL ANOMALIES, AND ECTRODACTYLY aggregate channel Neural network Image classification Object detection aggregates

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Shape driven kernel adaptation in Convolutional Neural Network for robust facial trait recognition

Shape driven kernel adaptation in Convolutional Neural Netwo...

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Conference on computer Vision and pattern recognition (CVPR)

作者： Shaoxin Li Junliang Xing Zhiheng Niu Shiguang Shan Shuicheng Yan Department of Electrical and Computer Engineering National University of Singapore Singapore Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China National Laboratory of Pattern Recognition Institute of Automation CAS Beijing China

ISBN: (纸本)9781467369657

One key challenge of facial trait recognition is the large non-rigid appearance variations due to some irrelevant real world factors, such as viewpoint and expression changes. In this paper, we explore how the shape information, i.e. facial landmark positions, can be explicitly deployed into the popular Convolutional Neural Network (CNN) architecture to disentangle such irrelevant non-rigid appearance variations. First, instead of using fixed kernels, we propose a kernel adaptation method to dynamically determine the convolutional kernels according to the spatial distribution of facial landmarks, which helps learning more robust features. Second, motivated by the intuition that different local facial regions may demand different adaptation functions, we further propose a tree-structured convolutional architecture to hierarchically fuse multiple local adaptive CNN subnetworks. Comprehensive experiments on WebFace, Morph II and MultiPIE databases well validate the effectiveness of the proposed kernel adaptation method and tree-structured convolutional architecture for facial trait recognition tasks, including identity, age and gender recognition. For all the tasks, the proposed architecture consistently achieves the state-of-the-art performances.

关键词： Kernel Shape Face Face recognition Robustness Neural networks computer architecture

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When Face recognition Meets with Deep Learning: An Evaluation of Convolutional Neural Networks for Face recognition

When Face Recognition Meets with Deep Learning: An Evaluatio...

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International Conference on computer Vision Workshops (ICCV Workshops)

作者： Guosheng Hu Yongxin Yang Dong Yi Josef Kittler William Christmas Stan Z. Li Timothy Hospedales Centre for Vision Speech and Signal Processing University of Surrey UK Indicates equal contribution LEAR team Inria Grenoble Rhone-Alpes Montbonnot France Electronic Engineering and Computer Science Queen Mary University of London UK Chinese Academy of Sciences Center for Biometrics and Security Research & National Laboratory of Pattern Recognition China

Deep learning, in particular Convolutional Neural Network (CNN), has achieved promising results in face recognition recently. However, it remains an open question: why CNNs work well and how to design a 'good' architecture. The existing works tend to focus on reporting CNN architectures that work well for face recognition rather than investigate the reason. In this work, we conduct an extensive evaluation of CNN-based face recognition systems (CNN-FRS) on a common ground to make our work easily reproducible. Specifically, we use public database LFW (Labeled Faces in the Wild) to train CNNs, unlike most existing CNNs trained on private databases. We propose three CNN architectures which are the first reported architectures trained using LFW data. This paper quantitatively compares the architectures of CNNs and evaluates the effect of different implementation choices. We identify several useful properties of CNN-FRS. For instance, the dimensionality of the learned features can be significantly reduced without adverse effect on face recognition accuracy. In addition, a traditional metric learning method exploiting CNN-learned features is evaluated. Experiments show two crucial factors to good CNN-FRS performance are the fusion of multiple CNNs and metric learning. To make our work reproducible, source code and models will be made publicly available.

关键词： Face recognition Face Databases Measurement Training Object recognition Convolutional codes

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Feature extraction and visual feature fusion for the detection of concurrent prefix hijacks

IFIP Advances in Information and Communication Technology

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IFIP Advances in Information and Communication Technology 2014年 437卷 310-319页

作者： Papadopoulos, Stavros Votis, Konstantinos Alexakos, Christos Tzovaras, Dimitrios Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom Information Technologies Institute Centre for Research and Technology Hellas P.O. Box 361 Thermi-Thessaloniki57001 Greece Pattern Recognition Laboratory Computer Engineering and Informatics University of Patras Patras Greece

This paper presents a method for visualizing and analyzing Multiple Origin Autonomous System (MOAS) incidents on Border Gateway Protocol (BGP), for the purpose of detecting concurrent prefix hijack. Concurrent prefix hijacks happen when an unauthorized network originates prefixes that belong to multiple other networks. Towards the goal of accurately identifying such events, multiple features are extracted from the BGP records and visualized using parallel coordinates enhanced with visual querying capabilities. The proposed visual queries enable the analyst to select a significant subset of the initial dataset for further analysis, based on the values of multiple features. This procedure allows for the efficient visual fusion of the proposed features and the accurate identification of prefix hijacks. Most of the previous approaches on BGP hijack detection depend on static methods in order to fuse the information from multiple features and identify anomalies. The proposed visual feature fusion, however, allows the human operator to incorporate his expert knowledge into the analysis, so as to dynamically investigate the observed events, and accurately identify anomalies. The efficiency of the proposed approach is demonstrated on state-of-the-art BGP events. © IFIP International Federation for Information Processing 2014.

关键词： Border Gateway Protocol

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Towards Multi-view and Partially-occluded Face Alignment

Towards Multi-view and Partially-occluded Face Alignment

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IEEE Conference on computer Vision and pattern recognition

作者： Junliang Xing Zhiheng Niu Junshi Huang Weiming Hu Shuicheng Yan National Laboratory of Pattern Recognition Institute of Automation Department of Electrical and Computer Engineering National University of Singapore

ISBN: (纸本)9781479951192

We present a robust model to locate facial landmarks under different views and possibly severe occlusions. To build reliable relationships between face appearance and shape with large view variations, we propose to formulate face alignment as an l_1-induced Stagewise Relational Dictionary (SRD) learning problem. During each training stage, the SRD model learns a relational dictionary to capture consistent relationships between face appearance and shape, which are respectively modeled by the pose-indexed image features and the shape displacements for current estimated landmarks. During testing, the SRD model automatically selects a sparse set of the most related shape displacements for the testing face and uses them to refine its shape iteratively. To locate facial landmarks under occlusions, we furtherpropose to learn an occlusion dictionary to model different kinds of partial face occlusions. By deploying the occlusion dictionary into the SRD model, the alignment performance for occluded faces can be further improved. Our algorithm is simple, effective, and easy to implement. Extensive experiments on two benchmark datasets and two newly built datasets have demonstrated its superior performances over the state-of-the-art methods, especially for faces with large view variations and/or occlusions.

关键词： Face Shape Dictionaries Training Testing Robustness Optimization

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Compressed sensing ensemble classifier for human detection

Compressed sensing ensemble classifier for human detection

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4th International Conference on Intelligence Science and Big Data engineering, IScIDE 2013

作者： Zhang, Baochang Liu, Juan Gao, Yongsheng Liu, Jianzhuang Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering BeiHang University Beijing 100191 China School of Engineering Griffith University Australia Shenzhen Key Lab for Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Department of Information Engineering Chinese University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9783642420566

This paper proposes a novel Compressed Sensing Ensemble Classifier (CSEC) for human detection. The proposed CSEC employs the compressed sensing technique to get a more sparse model with a more reasonable selection of base classifiers. The major contributions of this paper are: 1) a novel principled framework for ensemble classifier design based on compressed sensing;2) a new concept of considering both the simplicity of ensemble classifier and irrelevance of base classifiers towards optimal classifier design;and 3) a quadratic function for CSEC optimization which includes a new optimizable positive semi-definite relevance matrix to simultaneously select appropriate base classifiers with minimized relevance. Experimental results on INRIA and SDL databases show that the performance of CSEC is better than two most popular classifiers SVM and AdaBoost, as well as a most recent method CLML. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Compressed sensing

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