3DOR 19

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https://diglib.eg.org/handle/10.2312/2632755

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Browsing 3DOR 19 by Subject "Information systems"

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    Classification in Cryo-Electron Tomograms
    (The Eurographics Association, 2019) Gubins, Ilja; Schot, Gijs van der; Veltkamp, Remco C.; Förster, Friedrich; Du, Xuefeng; Zeng, Xiangrui; Zhu, Zhenxi; Chang, Lufan; Xu, Min; Moebel, Emmanuel; Martinez-Sanchez, Antonio; Kervrann, Charles; Lai, Tuan M.; Han, Xusi; Terashi, Genki; Kihara, Daisuke; Himes, Benjamin A.; Wan, Xiaohua; Zhang, Jingrong; Gao, Shan; Hao, Yu; Lv, Zhilong; Wan, Xiaohua; Yang, Zhidong; Ding, Zijun; Cui, Xuefeng; Zhang, Fa; Biasotti, Silvia and Lavoué, Guillaume and Veltkamp, Remco
    Different imaging techniques allow us to study the organization of life at different scales. Cryo-electron tomography (cryo-ET) has the ability to three-dimensionally visualize the cellular architecture as well as the structural details of macro-molecular assemblies under near-native conditions. Due to beam sensitivity of biological samples, an inidividual tomogram has a maximal resolution of 5 nanometers. By averaging volumes, each depicting copies of the same type of a molecule, resolutions beyond 4 Å have been achieved. Key in this process is the ability to localize and classify the components of interest, which is challenging due to the low signal-to-noise ratio. Innovation in computational methods remains key to mine biological information from the tomograms. To promote such innovation, we organize this SHREC track and provide a simulated dataset with the goal of establishing a benchmark in localization and classification of biological particles in cryo-electron tomograms. The publicly available dataset contains ten reconstructed tomograms obtained from a simulated cell-like volume. Each volume contains twelve different types of proteins, varying in size and structure. Participants had access to 9 out of 10 of the cell-like ground-truth volumes for learning-based methods, and had to predict protein class and location in the test tomogram. Five groups submitted eight sets of results, using seven different methods. While our sample size gives only an anecdotal overview of current approaches in cryo-ET classification, we believe it shows trends and highlights interesting future work areas. The results show that learning-based approaches is the current trend in cryo-ET classification research and specifically end-to-end 3D learning-based approaches achieve the best performance.
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    Feature Curve Extraction on Triangle Meshes
    (The Eurographics Association, 2019) Moscoso Thompson, Elia; Arvanitis, G.; Moustakas, Konstantinos; Hoang-Xuan, N.; Nguyen, E. R.; Tran, M.; Lejemble, T.; Barthe, L.; Mellado, N.; Romanengo, C.; Biasotti, S.; FALCIDIENO, BIANCA; Biasotti, Silvia and Lavoué, Guillaume and Veltkamp, Remco
    This paper presents the results of the SHREC'19 track: Feature curve extraction on triangle meshes. Given a model, the challenge consists in automatically extracting a subset of the mesh vertices that jointly represent a feature curve. As an optional task, participants were requested to send also a similarity evaluation among the feature curves extracted. The various approaches presented by the participants are discussed, together with their results. The proposed methods highlight different points of view of the problem of feature curve extraction. It is interesting to see that it is possible to deal with this problem with good results, despite the different approaches.
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    mpLBP: An Extension of the Local Binary Pattern to Surfaces based on an Efficient Coding of the Point Neighbours
    (The Eurographics Association, 2019) Moscoso Thompson, Elia; Biasotti, Silvia; Digne, Julie; Chaine, Raphaëlle; Biasotti, Silvia and Lavoué, Guillaume and Veltkamp, Remco
    The description of surface textures in terms of repeated colorimetric and geometric local surface variations is a crucial task for several applications, such as object interpretation or style identification. Recently, methods based on extensions to the surface meshes of the Local Binary Pattern (LBP) or the Scale-Invariant Feature Transform (SIFT) descriptors have been proposed for geometric and colorimetric pattern retrieval and classification. With respect to the previous works, we consider a novel LBPbased descriptor based on the assignment of the point neighbours into sectors of equal area and a non-uniform, multiple ring sampling. Our method is able to deal with surfaces represented as point clouds. Experiments on different benchmarks confirm the competitiveness of the method within the existing literature, in terms of accuracy and computational complexity.