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International Joint Conference on Neural Networks

作者： Ke Wang Ping Guo Qian Yin A-Li Luo Xin Xin School of Computer Science and Technology Beijing Institute of Technology Image Processing and Pattern Recognition Laboratory Beijing Normal University Key Laboratory of Optical Astronomy National Astronomical Observatories Chinese Academy of Sciences

ISBN: (纸本)9781509006212

Deep learning scheme has received significant attention during these years, particularly as a way of building hierarchical representations from unlabeled data for a variety of signal and information processing tasks. However, deep neural networks suffer from slow learning speed since most used training algorithms are based on variations of the gradient descent algorithms which require iterative optimization and thus are time-consuming. In addition, a series of control parameters need to be specified empirically which lacks of the theoretical guidance, and current learning algorithms for deep networks are not very suitable to incremental learning scenario. To address these issues, we propose a fast learning scheme in this paper. The basic idea of our approach is to pre-train basic units such as auto-encoders of the deep architecture in an analytical way without any iterative optimization procedure. This scheme is also extended to an incremental learning version. The experimental result shows the superiority of our approach over the state-of-the-art gradient descent based algorithms. To demonstrate the impact of our algorithm on complicated real world applications, we give an example of its performance in astronomical spectra pattern recognition.

关键词： astronomical spectra Spectra pattern recognition learning (artificial intelligence) recognition (Psychology) Neural network algorithms control parameter Optimization Procedure descent Fasting Functional training Road

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Consensus Control for Second-Order Multi-Agent with Time-Delay on Antagonistic Networks

Consensus Control for Second-Order Multi-Agent with Time-Del...

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第35届中国控制会议

作者： Penglin LI Yue LIU Yadong ZHAO Jie YANG Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University State Key Laboratory of Industrial Control Technology Institute of Industrial Process ControlCollege of Control Science and EngineeringZhejiang University Key Laboratory of System Control and Information Processing Ministry of Education

ISBN: (纸本)9781509009107

In this paper,we discuss consensus problems for antagonistic networks with double integrator *** cases are analyzed:(1) undirected graphs with fixed topology on antagonistic networks;(2) undirected graphs with fixed topology and time-delay on antagonistic *** both cases,distributed consensus protocols are proposed,with sufficient and necessary conditions *** is proved that the largest tolerable time-delay is only related to the largest eigenvalue of the graph ***,simulations are provided to demonstrate the obtained theoretical results.

关键词： Consensus Fixed Topology Multi-agent Antagonistic Networks Time-delay

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Reeb graphs through local binary patterns 10

Reeb graphs through local binary patterns

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10th IAPR-TC-15 International Workshop on Graph-Based Representations in pattern recognition, GbRPR 2015

作者： Janusch, Ines Kropatsch, Walter G. Pattern Recognition and Image Processing Group Institute of Computer Graphics and Algorithms Vienna University of Technology Vienna Austria

ISBN: (纸本)9783319182230

This paper presents an approach to derive critical points of a shape, the basis of a Reeb graph, using a combination of a medial axis skeleton and features along this skeleton. A Reeb graph captures the topology of a shape. The nodes in the graph represent critical points (positions of change in the topology), while edges represent topological persistence. We present an approach to compute such critical points using Local Binary patterns. For one pixel the Local Binary pattern feature vector is derived comparing this pixel to its neighbouring pixels in an environment of a certain radius. We start with an initial segmentation and a medial axis representation. Along this axis critical points are computed using Local Binary patterns with the radius, defining the neighbouring pixels, set a bit larger than the radius according to the medial axis transformation. Critical points obtained in this way form the node set in a Reeb graph, edges are given through the connectivity of the skeleton. This approach aims at improving the representation of flawed segmented data. In the same way segmentation artefacts, as for example single pixels representing noise, may be corrected based on this analysis. © Springer International Publishing Switzerland 2015.

关键词： image segmentation

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Robust weighted least squares for guided depth upsampling

Robust weighted least squares for guided depth upsampling

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IEEE International Conference on image processing

作者： Wei Liu Xiaogang Chen Jie Yang Qiang Wu Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China College of Communication and Art Design University of Shanghai for Science and Technology China School of Computing and Communications University of Technology Sydney Australia

ISBN: (纸本)9781467399623

In this paper, we propose a new guided depth upsampling method denoted as Robust Weighted Least Squares (RWLS). Our work is inspired by the connection between the Weighted Least Squares (WLS) and the Auto Regressive (AR) model. By adopting a new robust penalty function to model the smoothness of the proposed model, we show that the proposed method performs much better in preserving sharp depth discontinuities than previous work. Through both mathematical analysis and experimental results, we show that our method has promising performance on handling the inconsistency between the guidance image and the depth map in both preserving sharp depth discontinuities and suppressing the texture copy artifacts.

关键词： Robustness Interpolation Color image resolution Mathematical model Noise measurement image color analysis

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OUGENE： a disease associated over-expressed and under-expressed gene database

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Science Bulletin 2016年第10期61卷 752-754页

作者： Xiaoyong Pan Hong-Bin Shen Section for Animal Genetics Bioinformatics and Breeding Center for Non-Coding RNA in Technology and Health University of Copenhagen 1870 Frederiksberg Denmark Department of Disease Systems Biology Novo NordiskFoundation Center for Protein Research University ofCopenhagen 1870 Frederiksberg Denmark Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China

Gene over-expression or under-expression is closely associated with human diseases, which contributes to phenotypic variations and diversity. To our best knowledge, there is no single open specific resource available to provide the association information between gene over- or under-expression and various diseases. In this study, we presented a comprehensive disease-associated over- and under-expressed gene database （OUGene） based on our proposed text mining pipeline and several open curated databases. It contains total 41,269 unique associa- tions between 7,238 over- or under-expressed genes and 1,480 diseases, which are supported by 81,974 evidence sentences from 56,442 articles. The OUGene is compre- hensive and covers most important therapeutic areas. Meanwhile a new scoring system is designed to rank the associations based on benchmarking against hand-curated data. OUGene provides an easy-of-use web interface for researchers to analyze these data and visualize the associ- ated networks, which can give insights to the complex relationships between over- and under-expressed genes and diseases at a system level. It is available at ***. ***/bioinf/OUGene/.

关键词： Text mining Gene over- and under-expression Disease Database

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Patch-based Object Tracking via Locality-constrained Linear Coding

Patch-based Object Tracking via Locality-constrained Linear ...

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第35届中国控制会议

作者： Kunqi Gu Mingna Liu Tao Zhou Fanghui Liu Xiangjian He Jie Yang Yu Qiao Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai Institute of Spaceflight Control Technology Infrared Detection Technology Research and Development Center China Aerospace Science and Technology Corporation School of Computing and Communications University of Technology The Key Laboratory of Ministry of Education for System Control and Information Processing

ISBN: (纸本)9781509009107

In this paper,the Locality-constrained Linear Coding(LLC) algorithm is incorporated into the object tracking ***,we extract local patches within a candidate and then utilize the LLC algorithm to encode these *** on these codes,we exploit pyramid max pooling strategy to generate a richer feature *** feature histogram which integrates holistic and part-based features can be more discriminative and ***,an occlusion handling strategy is utilized to make our tracker more ***,an efficient graph-based manifold ranking algorithm is exploited to capture the relevance between target templates and *** tracking,target templates are taken as labeled nodes while target candidates are taken as unlabeled nodes,and the goal of tracking is to search for the candidate that is the most relevant to existing labeled nodes by manifold ranking *** on challenging video sequences have demonstrated the superior accuracy and robustness of the proposed method in comparison to other state-of-the-art baselines.

关键词： object tracking LLC pyramid max-pooling local appearance model manifold ranking

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Scene understanding based on Multi-Scale Pooling of deep learning features

Scene understanding based on Multi-Scale Pooling of deep lea...

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2015 International Conference on Automation,Mechanical Control and Computational Engineering(AMCCE 2015)

作者： DongYang Li Yue Zhou Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University

ISBN: (纸本)9781510803084

Deep convolutional neural networks(CNNs) have recently shown impressive performance as generic representation for recognition. However, the feature extracted from global CNNs lack geometric invariance, which limits their robustness for classification and detection of highly variable *** improve the invariance of the features without degrading their discriminative power and speed up the calculation, we follow the next two method. Firstly, we adopt the scheme called multi-scale orderless pooling(MOP-CNN) which extracts CNNs activation from local patches of the image at multiple scale levels, performs orderless VLAD pooling of these activations at each level separately, and concatenates the result. Second, to speed up the calculation, we adapt the SPP-net as the CNNs architecture. Using SPP-net, we compute the feature maps from the entire image only once, and then pool features in arbitrary regions(sub-images) to generate fixed-length representations for training the detectors. This method avoids repeatedly computing the convolutional features. On the challenging SUN397 Scenes classification datasets, our method achieves competitive classification results.

关键词： CNNs MOP-CNN SPP-net Scenes understanding

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A nearest neighbor search method for image matching based on ORB

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Journal of Information and Computational Science 2015年第7期12卷 2691-2700页

作者： Zou, Jiancheng Wang, Xiaofei Institute of Image Processing and Pattern Recognition North China University of Technology Beijing China

ORB algorithm is one of the widely used local image feature matching methods. In order to increase the speed of ORB matching, this paper uses a Nearest Neighbor (NN) search method to replace the Hamming distance and proposes a Nearest Neighbor search method for image matching based on ORB. Firstly, the image feature points are detected by FAST and then the corners orientations are determined. Meanwhile, the corresponding binary descriptors with BRIEF are obtained. Finally the feature points can be matched by finding the nearest Euclidean distances. Experimental results show that the new algorithm significantly enhances the matching speed. Copyright © 2015 Binary Information Press.

关键词： image matching

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UPSAMPLING THE DEPTH MAP WITH ITS OWN PROPERTIES

UPSAMPLING THE DEPTH MAP WITH ITS OWN PROPERTIES

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IEEE International Conference on image processing

作者： Wei Liu Penglin Li Jie Yang Pengfei Shi Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University

ISBN: (纸本)9781479983407

In this paper, we present a novel method to upsample the depth map obtained by the Time-of-Flight (ToF) camera with the guidance of the companion high resolution color image. The problem is modeled with an optimization framework where we use a novel exponential function as the error norm. By using this novel error norm, our model could take the properties of the depth map itself into account. Depth discontinuity cues are obtained not only from the color image but also the depth map itself. To further enhance the performance, we perform a data driven selection of the parameter in the model to better fit the property of the depth map. Experimental results show that our method has excellent performance in smoothing the noise, preserving sharp depth discontinuities and suppressing the texture copy effect.

关键词： Bilateral filter Optimization Depth map upsampling ToF

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CROSS-MODALITY HASHING WITH PARTIAL CORRESPONDENCE

CROSS-MODALITY HASHING WITH PARTIAL CORRESPONDENCE

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IEEE International Conference on image processing

作者： Yun Gu Haoyang Xue Jie Yang Pengfei Shi Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University

ISBN: (纸本)9781479983407

Learning a hashing function for cross-media search is very desirable due to its low storage cost and fast query speed. However, the data crawled from Internet cannot always guarantee good correspondence among different modalities which affects the learning for hashing function. In this paper, we focus on cross-modal hashing with partially corresponded data. The data without full correspondence are made in use to enhance the hashing performance. The experiments on Wiki and NUS-WIDE datasets demonstrates that the proposed method outperforms some state-of-the-art hashing approaches with fewer correspondence information.

关键词： Cross-modality Hashing Partial Correspondence Multimedia Search

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