Academic literature on the topic 'Hierarchical skeletons'
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Journal articles on the topic "Hierarchical skeletons"
Zhang, Xiaopeng, Jianfei Liu, Marc Jaeger, and Zili Li. "Volume Decomposition for Hierarchical Skeletonization." International Journal of Virtual Reality 8, no. 1 (January 1, 2009): 89–97. http://dx.doi.org/10.20870/ijvr.2009.8.1.2716.
Full textBorgefors, G., G. Ramella, and G. Sanniti Di Baja. "Hierarchical decomposition of multiscale skeletons." IEEE Transactions on Pattern Analysis and Machine Intelligence 23, no. 11 (2001): 1296–312. http://dx.doi.org/10.1109/34.969119.
Full textYang, Cong, Oliver Tiebe, Kimiaki Shirahama, and Marcin Grzegorzek. "Object matching with hierarchical skeletons." Pattern Recognition 55 (July 2016): 183–97. http://dx.doi.org/10.1016/j.patcog.2016.01.022.
Full textABLAMEYKO, SERGEY V., CARLO ARCELLI, and GABRIELLA SANNITI DI BAJA. "HIERARCHICAL DECOMPOSITION OF DISTANCE LABELED SKELETONS." International Journal of Pattern Recognition and Artificial Intelligence 10, no. 08 (December 1996): 957–70. http://dx.doi.org/10.1142/s0218001496000542.
Full textCornea, Nicu D., Deborah Silver, Xiaosong Yuan, and Raman Balasubramanian. "Computing hierarchical curve-skeletons of 3D objects." Visual Computer 21, no. 11 (September 14, 2005): 945–55. http://dx.doi.org/10.1007/s00371-005-0308-0.
Full textYang, Jie, Xiaorong Wen, Qiulai Wang, Jin-Sheng Ye, Yanli Zhang, and Yuan Sun. "A Novel Scheme about Skeleton Optimization Designed for ISTTWN Algorithm." Remote Sensing 14, no. 23 (December 1, 2022): 6097. http://dx.doi.org/10.3390/rs14236097.
Full textMatsushita, Hiroshi, Yoshihiro Mori, and Toshio Inui. "Hierarchical shape description using skeletons and hierarchical shape discrimination by neural networks." Systems and Computers in Japan 22, no. 7 (1991): 75–83. http://dx.doi.org/10.1002/scj.4690220708.
Full textOzin, Geoffrey A., and Scott Oliver. "Skeletons in the beaker: Synthetic hierarchical inorganic materials." Advanced Materials 7, no. 11 (November 1995): 943–47. http://dx.doi.org/10.1002/adma.19950071117.
Full textXu, Jingzhong, Jie Shan, and Ge Wang. "Hierarchical Modeling of Street Trees Using Mobile Laser Scanning." Remote Sensing 12, no. 14 (July 19, 2020): 2321. http://dx.doi.org/10.3390/rs12142321.
Full textReniers, Dennie, and Alexandru Telea. "Hierarchical part-type segmentation using voxel-based curve skeletons." Visual Computer 24, no. 6 (April 23, 2008): 383–95. http://dx.doi.org/10.1007/s00371-008-0220-5.
Full textDissertations / Theses on the topic "Hierarchical skeletons"
Genctav, Murat. "Global Skeleton." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12612729/index.pdf.
Full textWei, Jiangong. "Surface Integral Equation Methods for Multi-Scale and Wideband Problems." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1408653442.
Full textHSIEH, SHU-HUI, and 謝淑惠. "Hierarchical Identification based on Skeleton and Face Recognition." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/9rry3z.
Full text開南大學
資訊學院碩士在職專班
105
The main purpose of this paper is to study the use of Hierarchical Identification Method in order to use in a way to detect and enhance the speed of face recognition. Presently, the Hierarchical Identification Method can be used in different algorithms to identify and provide an effective identification safety system protection mechanism. This custom fast clearance identification system and auxiliary identification system can have an identification distance from 4.5 meters to 1 meter within the body of the skeleton and color image information. The first layer classification is based on the skeleton information. Then use the second layer of HOG face feature extraction with SVM face recognition of the hierarchical identity system. The purpose is to narrow the second layer of face recognition database comparison range. This will be based on the second layer with the same rate of face recognition, and then enhance the speed of its identification. The important results of this study, the identification speed can be increased to between 13% and 176%.
Hsieh, Ming-Hsun, and 謝明勳. "Using Hierarchical Modified Self-Organizing Map in Skeleton Extraction." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/12725168864478341551.
Full text南台科技大學
電子工程系
92
骨格抽出の技術はたくさん応用領域に広く採用されています。たどえばオブジェクト指向モデリング技術 (object modeling)、文字識別(character recognition)及びコンピュータアニメショーン(computer animation)などいろいろな応用領域に採用されています。しかし、伝統のthinningで抽出された骨格はいつも要らないの支線を分岐し、交差点の場合も変形を出ってくる事がありますから、特徴の獲得は不安定になります。だから、本論文では、「階層式修正型自己組織化マップ」を提出してこのようなの問題を解決する。 「階層式修正型自己組織化マップ(HMSOM)」はSOMとMSOMの二つの層から構成する。それで、第1、2層のネットワークとコホネンのSOMは同じ構造であり、二つの入力層と出力層から構成する。第1層では、SOMによってパターンの画素を入力データに。そして、前処理でニューロンのN個の数を概算し、出力ニューロンと画素の位置関係を考慮して、競合学習の基礎から、パターンをサブパターンにN個分割する。第2層では、1つづづのサブパターンをMSOMネットワークに入力させるし、ニューロンの数を1つにする。最後は、MSOMの可増加ニューロン数の特性によって、第1層の出力ニューロン数の不足を訂正する。それは、ニューロン数の不足は骨格の不完全させるの原因の1つ。しかし、ここで分枝がない骨格を抽出するだけ。だから、後処理で隣接のニューロンは互いに繋がっているの骨格は分枝が出って来る。しかし、交差点の所は変形のノードになるという問題が現れる。これは個別組合性の最適な問題が出って来る。だから、このような問題のために、変形のノードが新しいのノードに代わって。その問題を改善します。 本論文では工具、漢字、数字とか、英語文字などの画像を実験の対象にし、ノイズの影響、パターンを撮るの環境の光の不足と印刷の品質の劣化などの情況も考えている。実験の結果からを見ると、本論文の方法は確かに伝統のthinningより良いです。それで、SOMがノイズの影響を対抗が出来るのもはきり分かりました。しかし、システムの処理時間は長い時間がかかりますので、将来はこの問題を解決しる目標を目指します。
Book chapters on the topic "Hierarchical skeletons"
Park, Jong-Seung. "Hierarchical Shape Description Using Skeletons." In Lecture Notes in Computer Science, 709–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-69423-6_69.
Full textNebelsick, James H., Janina F. Dynowski, Jan Nils Grossmann, and Christian Tötzke. "Echinoderms: Hierarchically Organized Light Weight Skeletons." In Biologically-Inspired Systems, 141–55. Dordrecht: Springer Netherlands, 2015. http://dx.doi.org/10.1007/978-94-017-9398-8_8.
Full textCouprie, Michel. "Hierarchic Euclidean Skeletons in Cubical Complexes." In Discrete Geometry for Computer Imagery, 141–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19867-0_12.
Full textHuang, Linjiang, Yan Huang, Wanli Ouyang, and Liang Wang. "Hierarchical Graph Convolutional Network for Skeleton-Based Action Recognition." In Lecture Notes in Computer Science, 93–102. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34120-6_8.
Full textChen, Yuxiao, Long Zhao, Jianbo Yuan, Yu Tian, Zhaoyang Xia, Shijie Geng, Ligong Han, and Dimitris N. Metaxas. "Hierarchically Self-supervised Transformer for Human Skeleton Representation Learning." In Lecture Notes in Computer Science, 185–202. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19809-0_11.
Full textHe, Changxiang, Shuting Liu, Ying Zhao, Xiaofei Qin, Jiayuan Zeng, and Xuedian Zhang. "Structure-Aware Multi-scale Hierarchical Graph Convolutional Network for Skeleton Action Recognition." In Lecture Notes in Computer Science, 293–304. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86365-4_24.
Full text"Hierarchical Integration of Modular Structures in the Evolution of Animal Skeletons." In Modularity. The MIT Press, 2005. http://dx.doi.org/10.7551/mitpress/4734.003.0017.
Full textThomas, Roger D. K. "Hierarchical Integration of Modular Structures in the Evolution of Animal Skeletons." In Modularity, 239–58. The MIT Press, 2005. http://dx.doi.org/10.7551/mitpress/4734.003.0020.
Full text"Clusters of Chemical Compounds as Polytopes of the Highest Dimension." In Normal Partitions and Hierarchical Fillings of N-Dimensional Spaces, 27–51. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-6768-5.ch002.
Full textBunker, Bruce C., and William H. Casey. "Bio-inspired Synthesis of Oxide Nanostructures." In The Aqueous Chemistry of Oxides. Oxford University Press, 2016. http://dx.doi.org/10.1093/oso/9780199384259.003.0015.
Full textConference papers on the topic "Hierarchical skeletons"
Eberhardt, Sebastian, Steffen Weissmann, Ulrich Pinkall, and Nils Thuerey. "Hierarchical vorticity skeletons." In SCA '17: The ACM SIGGRAPH / Eurographics Symposium on Computer Animation. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3099564.3099569.
Full textZhao, Kai, Wei Shen, Shanghua Gao, Dandan Li, and Ming-Ming Cheng. "Hi-Fi: Hierarchical Feature Integration for Skeleton Detection." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/166.
Full textYang, Su-Lin, Peter D. Scott, and Cesar Bandera. "Hierarchical top-down shape classification based on multiresolution skeletons." In IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, edited by Raj S. Acharya and Dmitry B. Goldgof. SPIE, 1993. http://dx.doi.org/10.1117/12.148701.
Full textLi, Chao, Qiaoyong Zhong, Di Xie, and Shiliang Pu. "Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/109.
Full textDrechsler, Klaus, and Cristina Oyarzun Laura. "Hierarchical decomposition of vessel skeletons for graph creation and feature extraction." In 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2010. http://dx.doi.org/10.1109/bibm.2010.5706609.
Full textLi, Chong, Frederic Gava, and Gaetan Hains. "Implementation of Data-Parallel Skeletons: A Case Study Using a Coarse-Grained Hierarchical Model." In 2012 11th International Symposium on Parallel and Distributed Computing (ISPDC). IEEE, 2012. http://dx.doi.org/10.1109/ispdc.2012.12.
Full textReniers, Dennie, and Alexandru Telea. "Skeleton-based Hierarchical Shape Segmentation." In IEEE International Conference on Shape Modeling and Applications 2007 (SMI '07). IEEE, 2007. http://dx.doi.org/10.1109/smi.2007.33.
Full textLeborgne, Aurelie, Julien Mille, and Laure Tougne. "Hierarchical skeleton for shape matching." In 2016 IEEE International Conference on Image Processing (ICIP). IEEE, 2016. http://dx.doi.org/10.1109/icip.2016.7533031.
Full textTelea, A., C. Sminchisescu, and S. Dickinson. "Optimal inference for hierarchical skeleton abstraction." In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. IEEE, 2004. http://dx.doi.org/10.1109/icpr.2004.1333695.
Full textde Belen, Ryan Anthony J., and Rowel O. Atienza. "Automatic skeleton generation using hierarchical mesh segmentation." In SA '16: SIGGRAPH Asia 2016. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2992138.2992150.
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