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2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018

作者： Lixin, He Can, Wang Bin, Kong Jing, Yang CAS Hefei Research Center for Biomimetic Vision Hefei Institute of Intelligent Machines Anhui China Department of Automation University of Science and Technology of China Hefei Anhui China Key Lab of Network and Intelligent Information Processing Hefei University Hefei Anhui China

ISBN: (纸本)9781538660577

A method of depth measurement based on SURF without camera calibration and parameter adjustment is presented in this paper. In this method, two images of a same scene are captured by a monocular camera. Then the objects in the two images are matched automatically after the images have been segmented and SURF (Speeded Up Robust Features) points have been extracted from the images respectively. Finally, a pair of straight line segments in the matched objects is chose and depth of the object is calculated according to the lengths of the pair of straight line segments. The results of the experiments show the method is effective. © 2018 IEEE.

关键词： Cameras

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Efficient and adaptive kernelization for nonlinear max-margin multi-view learning

arXiv

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arXiv 2019年

作者： Du, Changying Jia He, B. Du, Changde Zhuang, Fuzhen He, Qing Long, Guoping Huawei Noah's Ark Lab Beijing100085 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Institute of Software Chinese Academy of Sciences Beijing100190 China Research Center for Brain-Inspired Intelligence Institute of Automation CAS Beijing China University of Chinese Academy of Sciences Beijing100049 China

Existing multi-view learning methods based on kernel function either require the user to select and tune a single predefined kernel or have to compute and store many Gram matrices to perform multiple kernel learning. Apart from the huge consumption of manpower, computation and memory resources, most of these models seek point estimation of their parameters, and are prone to overfitting to small training data. This paper presents an adaptive kernel nonlinear max-margin multi-view learning model under the Bayesian framework. Specifically, we regularize the posterior of an efficient multi-view latent variable model by explicitly mapping the latent representations extracted from multiple data views to a random Fourier feature space where max-margin classification constraints are imposed. Assuming these random features are drawn from Dirichlet process Gaussian mixtures, we can adaptively learn shift-invariant kernels from data according to Bochners theorem. For inference, we employ the data augmentation idea for hinge loss, and design an efficient gradient-based MCMC sampler in the augmented space. Having no need to compute the Gram matrix, our algorithm scales linearly with the size of training set. Extensive experiments on real-world datasets demonstrate that our method has superior performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Bayesian networks

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de novo sequencing with mirror image protease of LysargiNase and trypsin for large scale proteomics

de novo sequencing with mirror image protease of LysargiNase...

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中国生物化学与分子生物学会第十二届全国会员代表大会暨2018年全国学术会议

作者：徐平杨皓李衍常赵明治张俊令王一豪迟浩贺思敏 National Center for Protein Sciences Beijing Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing TechnologyCAS

De novo peptide sequencing for an individual pure protein has improved remarkably with the progress of mass spectrometry but there still exists incomplete peptide fragmentation and indistingushiable series of fragmented ions,which interrupted the interpretation of amino acid sequence of *** is even worse at proteome ***,we developed a series of high efficiency proteases,including LysargiNase with super activity and acetylated trypsin with high *** advantage of these two enzymes,we developed a novel algorithm,pNovoM for automated de novo sequencing of the mirrored peptides generated from the digestion of trypsin and LysargiNase *** combination of paired mirror image spectra results nearly complete series of product ions,which facilitate the de novo sequencing on both of purified protein and proteasome samples.

关键词： de novo sequencing proteomics mirror enzyme LysargiNase

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Harmonized multimodal learning with Gaussian process latent variable models

arXiv

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arXiv 2019年

作者： Song, Guoli Wang, Shuhui Huang, Qingming Tian, Qi School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Noah's Ark Lab Shenzhen518129 China Department of Computer Science University of Texas at San Antonio San AntonioTX78249 United States

Multimodal learning aims to discover the relationship between multiple modalities. It has become an important research topic due to extensive multimodal applications such as cross-modal retrieval. This paper attempts to address the modality heterogeneity problem based on Gaussian process latent variable models (GPLVMs) to represent multimodal data in a common space. Previous multimodal GPLVM extensions generally adopt individual learning schemes on latent representations and kernel hyperparameters, which ignore their intrinsic relationship. To exploit strong complementarity among different modalities and GPLVM components, we develop a novel learning scheme called Harmonization, where latent model parameters are jointly learned from each other. Beyond the correlation fitting or intra-modal structure preservation paradigms widely used in existing studies, the harmonization is derived in a model-driven manner to encourage the agreement between modality-specific GP kernels and the similarity of latent representations. We present a range of multimodal learning models by incorporating the harmonization mechanism into several representative GPLVM-based approaches. Experimental results on four benchmark datasets show that the proposed models outperform the strong baselines for cross-modal retrieval tasks, and that the harmonized multimodal learning method is superior in discovering semantically consistent latent representation. Copyright © 2019, The Authors. All rights reserved.

关键词： Gaussian distribution

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Scene Text Detection via Deep Semantic Feature Fusion and Attention-based Refinement

Scene Text Detection via Deep Semantic Feature Fusion and At...

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International Conference on Pattern Recognition

作者： Yu Song Yuanshun Cui Hu Han Shiguang Shan Xilin Chen Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China

Despite tremendous progress in scene text detection in the past few years, efficient text detection in the wild remains challenging, particularly for the texts have large rotations, and the complicated background areas that are easily confused with text. In this paper, we propose an effective approach for scene text detection, which consists of initial text detection using the proposed deep semantic feature fusion of a fully convolutional network (FCN), and text detection refinement by our attention based text vs. non-text classifier learned in a fine-to-coarse fashion. The proposed approach outperforms the state-of-the-art scene text detection algorithms on the public-domain ICDAR2015 dataset, achieving an accuracy of 0.83 in terms of F-measure.

关键词： Feature extraction Semantics Text recognition Proposals Task analysis Object detection

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The propositional normal default logic and the finite/infinite injury priority method

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Science China(information Sciences) 2017年第9期60卷 99-108页

作者： Wei LI Yuefei SUI Yuhui WANG State Key Laboratory of Software Development Environment Beihang University Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences

In propositional normal default logic, given a default theory(?, D) and a well-defined ordering of D, there is a method to construct an extension of(?, D) without any injury. To construct a strong extension of(?, D) given a well-defined ordering of D, there may be finite injuries for a default δ∈ D. With approximation deduction ?s in propositional logic, we will show that to construct an extension of(?, D) under a given welldefined ordering of D, there may be infinite injuries for some default δ∈ D.

关键词： default extension strong extension finite/infinite injury priority method recursively enumerable sets

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The B4-valued propositional logic with unary logical connectives ～1 /～2/┐

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Frontiers of Computer Science 2017年第5期11卷 887-894页

作者： Wei LI Yuefei SUI State Key Laboratory of Software Development Environment Beijing University of Aeronautics and Astronautics Beijing 100191 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

A B4-valued propositional logic will be proposed in this paper which there are three unary logical connectives ～1, ～2, ┐ and two binary logical connectives A, v, and a Gentzen-typed deduction system will be given s... 详细信息

关键词： the Belnap logic modality the soundness thecompleteness

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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SUPERPIXEL-GUIDED SPARSE UNMIXING FOR REMOTELY SENSED HYPERSPECTRAL IMAGERY

SUPERPIXEL-GUIDED SPARSE UNMIXING FOR REMOTELY SENSED HYPERS...

引用

IEEE International Geoscience and Remote Sensing Symposium

作者： Shaoquan Zhang Chengzhi Deng Jun Li Shengqian Wang Fan Li Chenguang Xu Antonio Plaza Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Nanchang Institute of Technology Nanchang China Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation School of Geography and Planning Sun Yat-sen University Guangzhou China Hyperspectral Computing Laboratory Escuela Politécnica University of Extremadura Cáceres Spain

Sparse representation-based approaches have been successfully applied to remotely sensed hyperspectral image unmixing. In recent years, sparse unmixing techniques have incorporated spatial information into the sparse unmixing model, achieving improved fractional abundance results. Most spatial-based sparse unmixing methods utilize regular-shaped neighborhoods (e.g., a cross or a square window) to characterize the spatial-contextual information around each pixel. However, the spatial characteristics of natural scenes are not always uniform, but vary according to the observed objects. Therefore, assuming uniform spatial neighborhoods may not be consistent with real spatial structures in the scene. Super-pixels offer a good solution to this problem since they can better characterize such spatial structures. Based on this observation, in this paper we develop a new superpixel-guided sparse unmixing (SPGSU) method for hyperspectral scenes. The proposed SPGSU includes the spatial correlation through a superpixel-based technique rather than assuming predefined pixel grids. Each superpixel can be regarded as a small spatial region, whose shape and size can be adaptively changed to accommodate different spatial structures. Our experimental results, conducted using simulated data sets, quantitatively indicate that our newly proposed method produces better results than other advanced spectral unmixing methods.

关键词： Hyperspectral imaging Libraries Shape Signal to noise ratio Sensors

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GLA: Global-Local Attention for Image Description

GLA: Global-Local Attention for Image Description

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作者： Li, Linghui Tang, Sheng Zhang, Yongdong Deng, Lixi Tian, Qi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of the ChineseAcademy of Sciences BeijingTX100049 China Department of Computer Science University of Texas at San Antonio San Antonio78249-1604 United States

In recent years, the task of automatically generating image description has attracted a lot of attention in the field of artificial intelligence. Benefitting from the development of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), many approaches based on the CNN-RNN framework have been proposed to solve this task and achieved remarkable process. However, two problems remain to be tackled in which the most existing methods use only the image-level representation. One problem is object missing, in which some important objects may be missing when generating the image description and the other is misprediction, when one object may be recognized in a wrong category. In this paper, to address these two problems, we propose a new method called global-local attention (GLA) for generating image description. The proposed GLA model utilizes an attention mechanism to integrate object-level features with image-level feature. Through this manner, our model can selectively pay attention to objects and context information concurrently. Therefore, our proposed GLA method can generate more relevant image description sentences and achieve the state-of-The-Art performance on the well-known Microsoft COCO caption dataset with several popular evaluation metrics-CIDEr, METEOR, ROUGE-L and BLEU-1, 2, 3, 4. © 1999-2012 IEEE.

关键词： Recurrent neural networks

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