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Academic literature on the topic 'Hierarchical skeletons'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Hierarchical skeletons"

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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.

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Hierarchical skeletons and shape components are important shape features, and they are useful for shape description and shape understanding. Techniques to extract shape components and hierarchical skeletons from volume data are analyzed in this paper based on multiple distance transformations. The application of volume decomposition for the extraction of hierarchical skeletons is emphasized and specified here. This work includes an establishment of the hierarchical structure of the object volume, a decomposition of the volume into simple sub-volumes, an extraction of compact skeleton segments corresponding to each independent sub-volume, and a connection of these skeleton segments into a hierarchical structure reflecting the organization the initial data
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Borgefors, 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.

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Yang, 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.

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ABLAMEYKO, 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.

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The distance labeled skeleton of a two-dimensional digital object is hierarchically decomposed into loops, branches and concatenations of branches. Decomposition is obtained by using an iterated tracing-and-deleting process alternately applied to more and more internal skeleton subsets. Every skeleton subset is associated a feature, called the relevance, which is related to the spatial extension of the object region represented by that subset. The relevance is used to decide on how to perform concatenations, as well as to rank skeleton decomposition components ascribed to the same hierarchy level. The decomposition is stable under object rotation and can be used in a recognition process accomplished via graph matching.
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Cornea, 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.

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Yang, 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.

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The ISTTWN algorithm overcame the defect of separating the production process of skeleton points and skeleton lines in tree branch point cloud skeleton extraction and improved the accuracy of the extracted initial skeletons, but the skeletons need further optimization. In the existing skeleton optimization, it is difficult to see the stump adjustment, and most of the bifurcation optimization and skeleton smoothness adopt fitting. Based on the characteristics of the initial skeletons extracted by the ISTTWN algorithm, this research optimizes the skeleton from four aspects. An algorithm for the stump adjustment for reconstructing the stump based on the layer and hierarchical relationship and an algorithm for the bifurcation optimization based on the local branch point cloud and cosine correlation are proposed, and an existing pruning method and a skeleton smoothing method are used. The results show that the skeleton optimization method proposed or used in this research has a high computational efficiency in general and can ultimately retain the necessary skeleton lines. In a visual analysis, the optimized skeleton is obviously much more natural and more in line with the actual topology of trees. In the quantitative analysis, the completeness, accuracy and effectiveness reached 97.82%, 95.72% and 89.47%, respectively. In this study, in addition to the existing tree parameters extracted by the skeleton or generalized cylinder model, the generated skeleton is used to extract the branch attributes. The R2 of the deflection angle of the branch tip, distance from branch tip and branch length are about 0.897, 0.986 and 0.988, respectively, which illustrates that their models are very good. This research can further expand the application of the skeleton.
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Matsushita, 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.

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Ozin, 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.

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Xu, 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.

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This paper proposes a novel method to reconstruct hierarchical 3D tree models from Mobile Laser Scanning (MLS) point clouds. Starting with a neighborhood graph from the tree point clouds, the method treats the root point of the tree as a source point and determines an initial tree skeleton by using the Dijkstra algorithm. The initial skeleton lines are then optimized by adjusting line connectivity and branch nodes based on morphological characteristics of the tree. Finally, combined with the tree point clouds, the radius of each branch skeleton node is estimated and flat cones are used to simulate tree branches. A local triangulation method is used to connect the gaps between two joint flat cones. Demonstrated by street trees of different sizes and point densities, the proposed method can extract street tree skeletons effectively, generate tree models with higher fidelity, and reconstruct trees with different details according to the skeleton level. It is found out the tree modeling error is related to the average point spacing, with a maximum error at the coarsest level 6 being about 0.61 times the average point spacing. The main source of the modeling error is the self-occlusion of trees branches. Such findings are both theoretically and practically useful for generating high-precision tree models from point clouds. The developed method can be an alternative to the current ones that struggle to balance modeling efficiency and modeling accuracy.
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Reniers, 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.

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Dissertations / Theses on the topic "Hierarchical skeletons"

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Genctav, Murat. "Global Skeleton." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12612729/index.pdf.

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A novel and unconventional shape description scheme is proposed which captures the hierarchy of parts and medial descriptors. Both the parts and the medial descriptors are extracted simultaneously, in a complementary fashion, using a real valued function defined over the shape domain. The function arises out of both global and local interactions within the shape domain and it is related to an extension of a linear elliptic PDE with an integral term. The part hierarchies, extracted via level sets and watersheds of the function, are organized into proper binary trees, and the medial descriptors, extracted via ridges and watersheds of the function, are organized as rooted depth-1 trees. The medial descriptors (we named global skeleton) consist of two distinct medial abstractions. The limbs and prominent boundary features are captured in the form of conventional skeletons. Secondly, the coarse structure of the shape is captured in the form of a watershed region, which is a powerful tool in respect to both stability and representation of prominent shape properties. Additionally, as an important technical contribution that addresses part matching, the randomized hierarchy tree is introduced that endows the part hierarchy tree with a probabilistic structure.
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Wei, 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.

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HSIEH, SHU-HUI, and 謝淑惠. "Hierarchical Identification based on Skeleton and Face Recognition." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/9rry3z.

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碩士
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%.
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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.

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碩士
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。
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Book chapters on the topic "Hierarchical skeletons"

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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.

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Nebelsick, 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.

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Couprie, 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.

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Huang, 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.

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Chen, 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.

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He, 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.

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"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.

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Thomas, 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.

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"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.

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General analytical expressions are obtained for calculating the dimension of multi-shell clusters with a common center of shells in those cases when there is a metal atom in the center of the shells and when it is not. The shells can be in the shape of any body of Plato. It has been established that the gamma-copper cluster has the form of a cross-polytope of high dimension. The forms of clusters with ligands of the core of which is a chain of metal atoms or a metal polyhedron are geometrically investigated. It is shown that if the skeleton is a chain of metal atoms, then the cluster is polytope composed of two polytopes of higher dimension adjacent to each other along a flat section containing a chain. If the skeleton is a metal polyhedron, then a cluster of higher dimension has several ligand shells.
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Bunker, 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.

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Nature is capable of building magnificently intricate and detailed structures out of otherwise boring materials such as calcium carbonate and silica. Anyone who has taken their children to see dinosaurs at a Natural History museum or visited natural wonders such as the Petrified Forest in Arizona are familiar with the natural process called fossilization by which the tissues of dead organisms are eventually replicated by objects of stone. Most living organisms (including humans) are critically dependent on more deliberate and controlled biomineralization phenomena that lead to the production of all hard tissues, including our teeth and bones, seashells and diatom skeletons, egg shells, and the magnetic nanoparticles that provide homing devices from bacteria to birds. All these processes are nothing more than specific examples of highly controlled nucleation and growth phenomena such as those described in generic terms in Chapter 7. At a molecular level, these processes are controlled by the same reaction mechanisms involving oxide surfaces, which were outlined in Chapter 6. However, biomineralization is orders of magnitude more sophisticated than standard nucleation and growth processes. The unique features of biomineralization involve the interplay between organic biomolecules and the nucleation and growth of inorganic phases such as oxides. This interplay is of critical importance in both biology and emerging nanotechnologies, providing specific examples that illustrate many of the concepts of oxide chemistry introduced in Chapters 5 through 7. In this chapter, we highlight the key concepts of biomineralization and provide examples of how researchers can now produce complex nanostructured oxides via biomimetic nucleation and growth strategies that replicate some of the key features used to make hard tissues in living systems. These strategies include the use of (1) molecular complexation and compartmentalization to control supersaturation levels, (2) specific ligands and surface structures to mediate nucleation phenomena, (3) hierarchical self-assembled organic architectures as templates for oxide formation, (4) functionalization to stimulate desired heterogeneous nucleation and growth processes on those templates, and (5) organic surfactants to manipulate both crystal-phase preferences and growth habits.
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Conference papers on the topic "Hierarchical skeletons"

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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.

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Zhao, 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.

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In natural images, the scales (thickness) of object skeletons may dramatically vary among objects and object parts. Thus, robust skeleton detection requires powerful multi-scale feature integration ability. To address this issue, we present a new convolutional neural network (CNN) architecture by introducing a novel hierarchical feature integration mechanism, named Hi-Fi, to address the object skeleton detection problem. The proposed CNN-based approach intrinsically captures high-level semantics from deeper layers, as well as low-level details from shallower layers. By hierarchically integrating different CNN feature levels with bidirectional guidance, our approach (1) enables mutual refinement across features of different levels, and (2) possesses the strong ability to capture both rich object context and high-resolution details. Experimental results show that our method significantly outperforms the state-of-the-art methods in terms of effectively fusing features from very different scales, as evidenced by a considerable performance improvement on several benchmarks.
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Yang, 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.

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Li, 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.

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Skeleton-based human action recognition has recently drawn increasing attentions with the availability of large-scale skeleton datasets. The most crucial factors for this task lie in two aspects: the intra-frame representation for joint co-occurrences and the inter-frame representation for skeletons' temporal evolutions. In this paper we propose an end-to-end convolutional co-occurrence feature learning framework. The co-occurrence features are learned with a hierarchical methodology, in which different levels of contextual information are aggregated gradually. Firstly point-level information of each joint is encoded independently. Then they are assembled into semantic representation in both spatial and temporal domains. Specifically, we introduce a global spatial aggregation scheme, which is able to learn superior joint co-occurrence features over local aggregation. Besides, raw skeleton coordinates as well as their temporal difference are integrated with a two-stream paradigm. Experiments show that our approach consistently outperforms other state-of-the-arts on action recognition and detection benchmarks like NTU RGB+D, SBU Kinect Interaction and PKU-MMD.
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Drechsler, 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.

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Li, 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.

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Reniers, 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.

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Leborgne, 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.

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Telea, 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.

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de 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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