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Your search keyword '"School of Computer Science and Technology [Jinan]"' showing total 4 results
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4 results on '"School of Computer Science and Technology [Jinan]"'

1. SHREC 2020: Classification in cryo-electron tomograms

Author

Remco C. Veltkamp, Min Xu, Nguyen P. Nguyen, Xuefeng Cui, Filiz Bunyak, Yu Hao, Xiangrui Zeng, Gijs van der Schot, Tommi A. White, Fa Zhang, Ilja Gubins, Xiao Wang, Daisuke Kihara, Marten L. Chaillet, Friedrich Förster, Xiaohua Wan, Emmanuel Moebel, Department of Information and Computing Sciences [Utrecht], Utrecht University [Utrecht], Department of Chemistry [Utrecht], CAS Institute of Computing Technology (ICT), Chinese Academy of Sciences [Beijing] (CAS), School of Computer Science and Technology [Jinan], Shandong University, Space-timE RePresentation, Imaging and cellular dynamics of molecular COmplexes (SERPICO), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Department of Computer Science [Purdue], Purdue University [West Lafayette], Computational Biology Department [Pittsburgh], Carnegie Mellon University [Pittsburgh] (CMU), Department of Computer Science Electrical Engineering [Kansas City], University of Missouri [Kansas City] (UMKC), University of Missouri System-University of Missouri System, University of Missouri [St. Louis], and University of Missouri System

Subjects
Artificial neural network, business.industry, Computer science, Template matching, Resolution (electron density), General Engineering, 020207 software engineering, Pattern recognition, Context (language use), 02 engineering and technology, Function (mathematics), Computer Graphics and Computer-Aided Design, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Human-Computer Interaction, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], 0202 electrical engineering, electronic engineering, information engineering, Benchmark (computing), 020201 artificial intelligence & image processing, Artificial intelligence, Tomography, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Focus (optics), business, [PHYS.PHYS.PHYS-DATA-AN]Physics [physics]/Physics [physics]/Data Analysis, Statistics and Probability [physics.data-an]
Abstract

International audience; Cryo-electron tomography (cryo-ET) is an imaging technique that allows us to three-dimensionally visualize both the structural details of macro-molecular assemblies under near-native conditions and its cellular context. Electrons strongly interact with biological samples, limiting electron dose. The latter limits the signal-to-noise ratio and hence resolution of an individual tomogram to about 50 (5 nm). Biological molecules can be obtained by averaging volumes, each depicting copies of the molecule, allowing for resolutions beyond 4 (0.4 nm). To this end, the ability to localize and classify components is crucial, but challenging due to the low signal-to-noise ratio. Computational innovation is key to mine biological information from cryo-electron tomography.To promote such innovation, we provide a novel simulated dataset to benchmark different methods of localization and classification of biological macromolecules in cryo-electron tomograms. Our publicly available dataset contains ten tomographic reconstructions of simulated cell-like volumes. Each volume contains twelve different types of complexes, varying in size, function and structure.In this paper, we have evaluated seven different methods of finding and classifying proteins. Six research groups present results obtained with learning-based methods and trained on the simulated dataset, as well as a baseline template matching, a traditional method widely used in cryo-ET research. We find that method performance correlates with particle size, especially noticeable for template matching which performance degrades rapidly as the size decreases. We learn that neural networks can achieve significantly better localization and classification performance, in particular convolutional networks with focus on high-resolution details such as those based on U-Net architecture.

Published
2020
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2. Inserting virtual pedestrians into pedestrian groups video with behavior consistency

Author

Zhiguo Ren, Wenjing Gai, Fan Zhong, Qunsheng Peng, Julien Pettré, State Key Lab CAD&CG [HangZhou], Zhejiang University, School of Computer Science and Technology [Jinan], Shandong University, Analysis-Synthesis Approach for Virtual Human Simulation (MIMETIC), Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-MEDIA ET INTERACTIONS (IRISA-D6), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Université de Rennes 2 (UR2)-Inria Rennes – Bretagne Atlantique, Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)

Subjects
Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, Pedestrian, Computer graphics, Consistency (database systems), 0202 electrical engineering, electronic engineering, information engineering, Computer vision, Motion planning, Mixed reality, Path planning, ComputingMethodologies_COMPUTERGRAPHICS, Agent-based simulation, business.industry, Frame (networking), 020207 software engineering, Grid, Data structure, Computer Graphics and Computer-Aided Design, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], Steering methods, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Artificial intelligence, business, Software
Abstract

International audience; In this paper, we propose a novel approach to integrate virtual pedestrians into a scene of real pedestrian groups with behavior consistency, and this is achieved by dynamic path planning of virtual pedestrians. Rather than accounting for the local collision avoidance only, our approach is capable of finding an optimized path for each virtual pedestrian on his way based on the current global distribution of the real groups in the scene. The big challenge is due to the information of both position and velocity of real pedestrians in the video being unavailable; also the distribution of the groups in the scene may vary dynamically. We therefore need to detect and track real pedestrians on each frame of the video to acquire their distribution and motion information. We save this information by an efficient data structure, called environment grid. During the way of a virtual pedestrian, the respective agent frequently emits the detection rays through the environment cells to find the situation of the real pedestrians ahead of him and adjust the original path if necessary. Virtual pedestrians are merged into the video finally with the occlusion between virtual characters and the real pedestrians correctly presented. Experiment results on several scenarios demonstrate the effectiveness of the proposed approach.

Published
2013
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3. Speckle attenuation by adaptive singular value shrinking with generalized likelihood matching in optical coherence tomography.

Author

Chen H, Fu S, Wang H, Lv H, and Zhang C

Subjects
Algorithms, Databases, Factual, Humans, Retina diagnostic imaging, Image Processing, Computer-Assisted methods, Tomography, Optical Coherence methods
Abstract

As a high-resolution imaging mode of biological tissues and materials, optical coherence tomography (OCT) is widely used in medical diagnosis and analysis. However, OCT images are often degraded by annoying speckle noise inherent in its imaging process. Employing the bilateral sparse representation an adaptive singular value shrinking method is proposed for its highly sparse approximation of image data. Adopting the generalized likelihood ratio as similarity criterion for block matching and an adaptive feature-oriented backward projection strategy, the proposed algorithm can restore better underlying layered structures and details of the OCT image with effective speckle attenuation. The experimental results demonstrate that the proposed algorithm achieves a state-of-the-art despeckling performance in terms of both quantitative measurement and visual interpretation., ((2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).)

Published
2018
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4. Instantaneous brain dynamics mapped to a continuous state space.

Author

Billings JCW, Medda A, Shakil S, Shen X, Kashyap A, Chen S, Abbas A, Zhang X, Nezafati M, Pan WJ, Berman GJ, and Keilholz SD

Subjects
Connectome, Humans, Magnetic Resonance Imaging, Rest, Brain physiology, Brain Mapping methods
Abstract

Measures of whole-brain activity, from techniques such as functional Magnetic Resonance Imaging, provide a means to observe the brain's dynamical operations. However, interpretation of whole-brain dynamics has been stymied by the inherently high-dimensional structure of brain activity. The present research addresses this challenge through a series of scale transformations in the spectral, spatial, and relational domains. Instantaneous multispectral dynamics are first developed from input data via a wavelet filter bank. Voxel-level signals are then projected onto a representative set of spatially independent components. The correlation distance over the instantaneous wavelet-ICA state vectors is a graph that may be embedded onto a lower-dimensional space to assist the interpretation of state-space dynamics. Applying this procedure to a large sample of resting-state and task-active data (acquired through the Human Connectome Project), we segment the empirical state space into a continuum of stimulus-dependent brain states. Upon observing the local neighborhood of brain-states adopted subsequent to each stimulus, we may conclude that resting brain activity includes brain states that are, at times, similar to those adopted during tasks, but that are at other times distinct from task-active brain states. As task-active brain states often populate a local neighborhood, back-projection of segments of the dynamical state space onto the brain's surface reveals the patterns of brain activity that support many experimentally-defined states., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Published
2017
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