Journal articles: '3D non-rigid shapes'

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Yang, Jingyu, Ke Li, Kun Li, and Yu-Kun Lai. "Sparse Non-rigid Registration of 3D Shapes." Computer Graphics Forum 34, no. 5 (August 2015): 89–99. http://dx.doi.org/10.1111/cgf.12699.

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Lladó, Xavier, Alessio Del Bue, Arnau Oliver, Joaquim Salvi, and Lourdes Agapito. "Reconstruction of non-rigid 3D shapes from stereo-motion." Pattern Recognition Letters 32, no. 7 (May 2011): 1020–28. http://dx.doi.org/10.1016/j.patrec.2011.02.010.

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Kuang, Zhenzhong, Zongmin Li, Xiaxia Jiang, Yujie Liu, and Hua Li. "Retrieval of non-rigid 3D shapes from multiple aspects." Computer-Aided Design 58 (January 2015): 13–23. http://dx.doi.org/10.1016/j.cad.2014.08.004.

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Liu, Bin, Weiming Wang, Jun Zhou, Bo Li, and Xiuping Liu. "Detail-Preserving Shape Unfolding." Sensors 21, no. 4 (February 8, 2021): 1187. http://dx.doi.org/10.3390/s21041187.

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Canonical extrinsic representations for non-rigid shapes with different poses are preferable in many computer graphics applications, such as shape correspondence and retrieval. The main reason for this is that they give a pose invariant signature for those jobs, which significantly decreases the difficulty caused by various poses. Existing methods based on multidimentional scaling (MDS) always result in significant geometric distortions. In this paper, we present a novel shape unfolding algorithm, which deforms any given 3D shape into a canonical pose that is invariant to non-rigid transformations. The proposed method can effectively preserve the local structure of a given 3D model with the regularization of local rigid transform energy based on the shape deformation technique, and largely reduce geometric distortion. Our algorithm is quite simple and only needs to solve two linear systems during alternate iteration processes. The computational efficiency of our method can be improved with parallel computation and the robustness is guaranteed with a cascade strategy. Experimental results demonstrate the enhanced efficacy of our algorithm compared with the state-of-the-art methods on 3D shape unfolding.

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Agudo, Antonio, Francesc Moreno-Noguer, Begoña Calvo, and J. M. M. Montiel. "Real-time 3D reconstruction of non-rigid shapes with a single moving camera." Computer Vision and Image Understanding 153 (December 2016): 37–54. http://dx.doi.org/10.1016/j.cviu.2016.05.004.

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Kuang, Zhenzhong, Zongmin Li, Xiaxia Jiang, and Yujie Liu. "Exploration in improving retrieval quality and robustness for deformable non-rigid 3D shapes." Multimedia Tools and Applications 74, no. 23 (August 14, 2014): 10335–66. http://dx.doi.org/10.1007/s11042-014-2170-4.

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Hu, Xiaobo, Dejun Zhang, Jinzhi Chen, Yiqi Wu, and Yilin Chen. "NrtNet: An Unsupervised Method for 3D Non-Rigid Point Cloud Registration Based on Transformer." Sensors 22, no. 14 (July 8, 2022): 5128. http://dx.doi.org/10.3390/s22145128.

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Self-attention networks have revolutionized the field of natural language processing and have also made impressive progress in image analysis tasks. Corrnet3D proposes the idea of first obtaining the point cloud correspondence in point cloud registration. Inspired by these successes, we propose an unsupervised network for non-rigid point cloud registration, namely NrtNet, which is the first network using a transformer for unsupervised large deformation non-rigid point cloud registration. Specifically, NrtNet consists of a feature extraction module, a correspondence matrix generation module, and a reconstruction module. Feeding a pair of point clouds, our model first learns the point-by-point features and feeds them to the transformer-based correspondence matrix generation module, which utilizes the transformer to learn the correspondence probability between pairs of point sets, and then the correspondence probability matrix conducts normalization to obtain the correct point set corresponding matrix. We then permute the point clouds and learn the relative drift of the point pairs to reconstruct the point clouds for registration. Extensive experiments on synthetic and real datasets of non-rigid 3D shapes show that NrtNet outperforms state-of-the-art methods, including methods that use grids as input and methods that directly compute point drift.

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Shen, Jiayan, Shutong Du, Ziyao Xu, Tiansheng Gan, Stephan Handschuh-Wang, and Xueli Zhang. "Anti-Freezing, Non-Drying, Localized Stiffening, and Shape-Morphing Organohydrogels." Gels 8, no. 6 (May 25, 2022): 331. http://dx.doi.org/10.3390/gels8060331.

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Artificial shape-morphing hydrogels are emerging toward various applications, spanning from electronic skins to healthcare. However, the low freezing and drying tolerance of hydrogels hinder their practical applications in challenging environments, such as subzero temperatures and arid conditions. Herein, we report on a shape-morphing system of tough organohydrogels enabled by the spatially encoded rigid structures and its applications in conformal packaging of “island–bridge” stretchable electronics. To validate this method, programmable shape morphing of Fe (III) ion-stiffened Ca-alginate/polyacrylamide (PAAm) tough organohydrogels down to −50 °C, with long-term preservation of their 3D shapes at arid or even vacuum conditions, was successfully demonstrated, respectively. To further illustrate the potency of this approach, the as-made organohydrogels were employed as a material for the conformal packaging of non-stretchable rigid electronic components and highly stretchable liquid metal (galinstan) conductors, forming a so-called “island–bridge” stretchable circuit. The conformal packaging well addresses the mechanical mismatch between components with different elastic moduli. As such, the as-made stretchable shape-morphing device exhibits a remarkably high mechanical durability that can withstand strains as high as 1000% and possesses long-term stability required for applications under challenging conditions.

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Raju, Ashwin, Shun Miao, Dakai Jin, Le Lu, Junzhou Huang, and Adam P. Harrison. "Deep Implicit Statistical Shape Models for 3D Medical Image Delineation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (June 28, 2022): 2135–43. http://dx.doi.org/10.1609/aaai.v36i2.20110.

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3D delineation of anatomical structures is a cardinal goal in medical imaging analysis. Prior to deep learning, statistical shape models (SSMs) that imposed anatomical constraints and produced high quality surfaces were a core technology. Today’s fully-convolutional networks (FCNs), while dominant, do not offer these capabilities. We present deep implicit statistical shape models (DISSMs), a new approach that marries the representation power of deep networks with the benefits of SSMs. DISSMs use an implicit representation to produce compact and descriptive deep surface embeddings that permit statistical models of anatomical variance. To reliably fit anatomically plausible shapes to an image, we introduce a novel rigid and non-rigid pose estimation pipeline that is modelled as a Markov decision process (MDP). Intra-dataset experiments on the task of pathological liver segmentation demonstrate that DISSMs can perform more robustly than four leading FCN models, including nnU-Net + an adversarial prior: reducing the mean Hausdorff distance (HD) by 7.5-14.3 mm and improving the worst case Dice-Sørensen coefficient (DSC) by 1.2-2.3%. More critically, cross-dataset experiments on an external and highly challenging clinical dataset demonstrate that DISSMs improve the mean DSC and HD by 2.1-5.9% and 9.9-24.5 mm, respectively, and the worst-case DSC by 5.4-7.3%. Supplemental validation on a highly challenging and low-contrast larynx dataset further demonstrate DISSM’s improvements. These improvements are over and above any benefits from representing delineations with high-quality surfaces.

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Zhu, Mengru, and Jong Han Lee. "Deep Learning-Based 3D Shape Feature Extraction on Flash Animation Style." Wireless Communications and Mobile Computing 2022 (March 24, 2022): 1–9. http://dx.doi.org/10.1155/2022/7999312.

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Flash animation, as a kind of digital learning resource, is an important media for delivering information content, and more importantly, it is an important online learning resource with text, graphics, images, audio, video, interaction, dynamic effects, etc. Flash animation, with its powerful multimedia interaction and presentation capabilities, is widely used in distance education, high-quality course websites, Q&A platforms, etc. With the continuous development of deep learning, the 3D shape feature extraction method combined with deep learning has become a hot research topic. In this paper, we combine deep learning with traditional 3D shape feature extraction methods, so that we can not only break the bottleneck of nondeep learning methods but also improve the accuracy of 3D shape data classification and retrieval tasks, especially in the case of non-rigid 3D shapes. The scheme in this paper not only does not require a large number of training samples but also its feature extraction for flash animation is accurate. Experiments show that the success rate of accurate feature extraction of this paper’s scheme is higher than that of the state-of-the-art methods.

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Rodrigues, Simson Julian, Nicole Vorhauer-Huget, Thomas Richter, and Evangelos Tsotsas. "Influence of Particle Shape on Tortuosity of Non-Spherical Particle Packed Beds." Processes 11, no. 1 (December 20, 2022): 3. http://dx.doi.org/10.3390/pr11010003.

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Tortuosity in packed beds or porous media is of significant interest in many fields, from geoscience to the chemical industry. Tortuosity plays a significant role in the mass transport in porous media, but also in their residual thermal or electric conductivity when the particles are not conducting. Several predictive models have been proposed to evaluate tortuosity, but there is still a gap when it comes to considering various particle shapes. The preponderance of tortuosity models substantiated in the literature are porosity-dependent while only a few include shape parameters. In this work, we propose a new model with sphericity and porosity to predict the tortuosity based on thermal simulations carried out with non-conducting particles for domains with no wall effect. The beds generated from rigid body simulations are compared and studied for different particle shapes with a sphericity range of 0.65–1. Sphericity showed a significant effect on the tortuosity compared with other 3D shape parameters (numbers of faces, edges, and vertices); therefore, only sphericity has been considered in the new model. The proposed new model is well suited for the porosity range of 0.3 to 0.4. In said ranges, it is an upgrade of the classical Zehner–Bauer–Schlünder (ZBS) model for the effective thermal conductivity of packed beds, with superior performance.

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Zhang, Hao, Yuxiao Zhou, Yifei Tian, Jun-Hai Yong, and Feng Xu. "Single Depth View Based Real-Time Reconstruction of Hand-Object Interactions." ACM Transactions on Graphics 40, no. 3 (July 4, 2021): 1–12. http://dx.doi.org/10.1145/3451341.

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Reconstructing hand-object interactions is a challenging task due to strong occlusions and complex motions. This article proposes a real-time system that uses a single depth stream to simultaneously reconstruct hand poses, object shape, and rigid/non-rigid motions. To achieve this, we first train a joint learning network to segment the hand and object in a depth image, and to predict the 3D keypoints of the hand. With most layers shared by the two tasks, computation cost is saved for the real-time performance. A hybrid dataset is constructed here to train the network with real data (to learn real-world distributions) and synthetic data (to cover variations of objects, motions, and viewpoints). Next, the depth of the two targets and the keypoints are used in a uniform optimization to reconstruct the interacting motions. Benefitting from a novel tangential contact constraint, the system not only solves the remaining ambiguities but also keeps the real-time performance. Experiments show that our system handles different hand and object shapes, various interactive motions, and moving cameras.

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Chen, Panqi, Lei Cheng, Ting Zhang, Hangfang Zhao, and Jianlong Li. "Tensor dictionary learning for representing three-dimensional sound speed fields." Journal of the Acoustical Society of America 152, no. 5 (November 2022): 2601–16. http://dx.doi.org/10.1121/10.0015056.

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Ocean sound speed field (SSF) representation is often plagued with low resolution (i.e., the capability of explaining fine-scale fluctuations). This drawback, however, is inherent in a number of classical SSF basis functions, e.g., empirical orthogonal functions, Fourier basis functions, and more recent tensor-based basis functions learned via the higher-order orthogonal iterative algorithm. For two-dimensional depth-time SSF representation, recent attempts relying on dictionary learning have shown that fine-scale sound speed information can be well preserved by a large number of basis functions. They are learned from the historical data without imposing rigid constraints on their shapes, e.g., the orthogonal constraints. However, generalizing the dictionary learning idea to represent three-dimensional (3D) spatial ocean SSF is non-trivial, in terms of both problem formulation and algorithm development. It calls for integrating the dictionary learning framework and the tensor-based basis function learning framework, a recently proposed one that captures the 3D sound speed correlations well. To achieve this goal, we develop a 3D SSF-tailored tensor dictionary learning algorithm that learns a large number of tensor-based basis functions with flexible shapes in a data-driven fashion. Numerical results based on the South China Sea 3D SSF data have showcased the superiority of the proposed approach over the prior method.

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Azéma, Emilien, David Cantor, and Itthichai Preechawuttipong. "Independence of shear strength with particle size dispersity still valid in polyhedral particle assemblies." EPJ Web of Conferences 249 (2021): 06009. http://dx.doi.org/10.1051/epjconf/202124906009.

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A very staggering result that has been constantly highlighted in granular media is that the shear strength of granular assemblies is independent of the particle size dispersity. In other words, a packing composed of monodisperse particles has similar strength properties to those of polydisperse systems. This has been shown numerically for the simplified case of disc and polygon assemblies in 2D and spheres in 3D. In this paper, we use three-dimensional contact dynamics simulations to revisit these results for the more complex case of assemblies composed of highly polydisperse rigid polyhedra. Although non-spherical shapes induce more intricated spatial correlations than spherical shapes because of the multiple contact types (i.e., vertex-face, edge-edge, edge-face, face-face), our numerical data provide evidence that the shear strength independence as the particle size dispersity increases still holds up for assemblies of polyhedra. We explain this finding from compensation mechanisms at the micro-scale between geometrical and mechanical anisotropies developed within the assemblies.

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Lonhus, Kirill, Dalibor Štys, Mohammadmehdi Saberioon, and Renata Rychtáriková. "Segmentation of Laterally Symmetric Overlapping Objects: Application to Images of Collective Animal Behavior." Symmetry 11, no. 7 (July 3, 2019): 866. http://dx.doi.org/10.3390/sym11070866.

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Video analysis is currently the main non-intrusive method for the study of collective behavior. However, 3D-to-2D projection leads to overlapping of observed objects. The situation is further complicated by the absence of stall shapes for the majority of living objects. Fortunately, living objects often possess a certain symmetry which was used as a basis for morphological fingerprinting. This technique allowed us to record forms of symmetrical objects in a pose-invariant way. When combined with image skeletonization, this gives a robust, nonlinear, optimization-free, and fast method for detection of overlapping objects, even without any rigid pattern. This novel method was verified on fish (European bass, Dicentrarchus labrax, and tiger barbs, Puntius tetrazona) swimming in a reasonably small tank, which forced them to exhibit a large variety of shapes. Compared with manual detection, the correct number of objects was determined for up to almost 90 % of overlaps, and the mean Dice-Sørensen coefficient was around 0.83 . This implies that this method is feasible in real-life applications such as toxicity testing.

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Schudoma, Christian. "It's a loop world – single strands in RNA as structural and functional elements." BioMolecular Concepts 2, no. 3 (June 1, 2011): 171–81. http://dx.doi.org/10.1515/bmc.2011.016.

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AbstractUnpaired regions in RNA molecules – loops – are centrally involved in defining the characteristic three-dimensional (3D) architecture of RNAs and are of high interest in RNA engineering and design. Loops adopt diverse, but specific conformations stabilised by complex tertiary structural interactions that provide structural flexibility to RNA structures that would otherwise not be possible if they only consisted of the rigid A-helical shapes usually formed by canonical base pairing. By participating in sequence-non-local contacts, they furthermore contribute to stabilising the overall fold of RNA molecules. Interactions between RNAs and other nucleic acids, proteins, or small molecules are also generally mediated by RNA loop structures. Therefore, the function of an RNA molecule is generally dependent on its loops. Examples include intermolecular interactions between RNAs as part of the microRNA processing pathways, ribozymatic activity, or riboswitch-ligand interactions. Bioinformatics approaches have been successfully applied to the identification of novel RNA structural motifs including loops, local and global RNA 3D structure prediction, and structural and conformational analysis of RNAs and have contributed to a better understanding of the sequence-structure-function relationships in RNA loops.

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Bilir, S. C., and Y. Yemez. "Non-rigid 3D shape tracking from multiview video." Computer Vision and Image Understanding 116, no. 11 (November 2012): 1121–34. http://dx.doi.org/10.1016/j.cviu.2012.07.001.

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Pickup, D., X. Sun, P. L. Rosin, R. R. Martin, Z. Cheng, Z. Lian, M. Aono, et al. "Shape Retrieval of Non-rigid 3D Human Models." International Journal of Computer Vision 120, no. 2 (April 26, 2016): 169–93. http://dx.doi.org/10.1007/s11263-016-0903-8.

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Koh, Sung-shik. "Non-rigid 3D Shape Recovery from Stereo 2D Video Sequence." Journal of the Korea Institute of Information and Communication Engineering 20, no. 2 (February 29, 2016): 281–88. http://dx.doi.org/10.6109/jkiice.2016.20.2.281.

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Trabelsi, Nesrine, Mohamed Ali Cherni, and Dorra Ben Sellem. "Non-rigid Registration for 3D Active Shape Liver Modeling." Advances in Science, Technology and Engineering Systems Journal 3, no. 1 (February 2018): 366–72. http://dx.doi.org/10.25046/aj030145.

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Lian, Zhouhui, Afzal Godil, Benjamin Bustos, Mohamed Daoudi, Jeroen Hermans, Shun Kawamura, Yukinori Kurita, et al. "A comparison of methods for non-rigid 3D shape retrieval." Pattern Recognition 46, no. 1 (January 2013): 449–61. http://dx.doi.org/10.1016/j.patcog.2012.07.014.

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Pickup, David, Xianfang Sun, Paul L. Rosin, and Ralph R. Martin. "Skeleton-based canonical forms for non-rigid 3D shape retrieval." Computational Visual Media 2, no. 3 (April 14, 2016): 231–43. http://dx.doi.org/10.1007/s41095-016-0045-5.

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Wang, Ya Ming, Z. Zhang, Jun Bao Zheng, and L. L. Tong. "Recovery of 3D Non-Rigid Structure from Images Based on Trajectories." Applied Mechanics and Materials 651-653 (September 2014): 2081–85. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.2081.

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In this paper, we address the problem of recovering the 3D structure of a non-rigid object based on trajectories throughout an image sequence. We formulate the 3D non-rigid shape as a linear combination of basis trajectories and compute the rectification matrix using generic algorithm with orthogonal constrains. In order to improve the reconstruction ability, trajectory filters are introduced to eliminate the need for choosing basis size. Experimental results from a human motion image sequences show that the proposed approach is more efficient for recovery of 3D non-rigid structure. Furthermore, the adjustment of basis size is avoided through the success use of trajectory filters.

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Han, Li, Yuning Tong, Jingyu Piao, Shengsi Xu, Xiaomin Wang, Pengyan Lan, and Bing Yu. "Non Rigid 3D Shape Partial Matching Based on Deep Feature Fusion." Journal of Computer-Aided Design & Computer Graphics 33, no. 3 (March 1, 2021): 475–86. http://dx.doi.org/10.3724/sp.j.1089.2021.18446.

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Pickup, David, Xianfang Sun, Paul L. Rosin, and Ralph R. Martin. "Euclidean-distance-based canonical forms for non-rigid 3D shape retrieval." Pattern Recognition 48, no. 8 (August 2015): 2500–2512. http://dx.doi.org/10.1016/j.patcog.2015.02.021.

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Wang, Huibing, Haohao Li, Jinjia Peng, and Xianping Fu. "Multi-feature distance metric learning for non-rigid 3D shape retrieval." Multimedia Tools and Applications 78, no. 21 (May 10, 2019): 30943–58. http://dx.doi.org/10.1007/s11042-019-7670-9.

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Pickup, David, Juncheng Liu, Xianfang Sun, Paul L. Rosin, Ralph R. Martin, Zhiquan Cheng, Zhouhui Lian, et al. "An evaluation of canonical forms for non-rigid 3D shape retrieval." Graphical Models 97 (May 2018): 17–29. http://dx.doi.org/10.1016/j.gmod.2018.02.002.

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Cho, Jungchan, Minsik Lee, and Songhwai Oh. "Complex Non-rigid 3D Shape Recovery Using a Procrustean Normal Distribution Mixture Model." International Journal of Computer Vision 117, no. 3 (October 5, 2015): 226–46. http://dx.doi.org/10.1007/s11263-015-0860-7.

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Li, Haohao, Zhixun Su, Nannan Li, Ximin Liu, Shengfa Wang, and Zhongxuan Luo. "Non-rigid 3D shape retrieval based on multi-scale graphical image and joint Bayesian." Computer Aided Geometric Design 81 (August 2020): 101910. http://dx.doi.org/10.1016/j.cagd.2020.101910.

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Zeng, Hui, Qi Wang, and Jiwei Liu. "Multi-Feature Fusion Based on Multi-View Feature and 3D Shape Feature for Non-Rigid 3D Model Retrieval." IEEE Access 7 (2019): 41584–95. http://dx.doi.org/10.1109/access.2019.2907609.

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Garro, Valeria, and Andrea Giachetti. "Scale Space Graph Representation and Kernel Matching for Non Rigid and Textured 3D Shape Retrieval." IEEE Transactions on Pattern Analysis and Machine Intelligence 38, no. 6 (June 1, 2016): 1258–71. http://dx.doi.org/10.1109/tpami.2015.2477823.

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Li, Bo, Afzal Godil, and Henry Johan. "Hybrid shape descriptor and meta similarity generation for non-rigid and partial 3D model retrieval." Multimedia Tools and Applications 72, no. 2 (April 23, 2013): 1531–60. http://dx.doi.org/10.1007/s11042-013-1464-2.

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Palmer, R. L., P. Helmholz, and G. Baynam. "CLINIFACE: PHENOTYPIC VISUALISATION AND ANALYSIS USING NON-RIGID REGISTRATION OF 3D FACIAL IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (August 12, 2020): 301–8. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-301-2020.

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Abstract. Facial appearance has long been understood to offer insight into a person’s health. To an experienced clinician, atypical facial features may signify the presence of an underlying rare or genetic disease. Clinicians use their knowledge of how disease affects facial appearance along with the patient’s physiological and behavioural traits, and their medical history, to determine a diagnosis. Specialist expertise and experience is needed to make a dysmorphological facial analysis. Key to this is accurately assessing how a face is significantly different in shape and/or growth compared to expected norms. Modern photogrammetric systems can acquire detailed 3D images of the face which can be used to conduct a facial analysis in software with greater precision than can be obtained in person. Measurements from 3D facial images are already used as an alternative to direct measurement using instruments such as tape measures, rulers, or callipers. However, the ability to take accurate measurements – whether virtual or not – presupposes the assessor’s facility to accurately place the endpoints of the measuring tool at the positions of standardised anatomical facial landmarks. In this paper, we formally introduce Cliniface – a free and open source application that uses a recently published highly precise method of detecting facial landmarks from 3D facial images by non-rigidly transforming an anthropometric mask (AM) to the target face. Inter-landmark measurements are then used to automatically identify facial traits that may be of clinical significance. Herein, we show how non-experts with minimal guidance can use Cliniface to extract facial anthropometrics from a 3D facial image at a level of accuracy comparable to an expert. We further show that Cliniface itself is able to extract the same measurements at a similar level of accuracy – completely automatically.

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Han, Li, Bing Yu, Jingyu Piao, Yuning Tong, Pengyan Lan, and Shuning Liu. "Multi-channel Joint Sparse Learning Model for Non-rigid Three-dimensional Object Classification." Journal of Imaging Science and Technology 64, no. 3 (May 1, 2020): 30503–1. http://dx.doi.org/10.2352/j.imagingsci.technol.2020.64.3.030503.

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Abstract In order to solve the issues of inadequate feature description and inefficient feature learning model existing in current classification methods, this article proposes a multi-channel joint sparse learning model for three-dimensional (3D) non-rigid object classification. First, the authors adopt a multi-level measurement of intrinsic properties to create complementary shape descriptors. Second, they build independent and informative bag of features (BoF) by embedding these shape descriptors into the visual vocabulary space. Third, a max-dependency and min-redundancy criterion is applied for optimal feature filtering on each BoF dictionary based on mutual information; meanwhile, each dictionary is learned and weighted according to its contribution to the classification task, and then a compact multi-channel joint sparse learning model is constructed. Finally, the authors train the joint sparse learning model followed by a Softmax classifier to implement efficient shape classification. The experimental results show that the proposed method has stronger feature representation ability and promotes greatly the discrimination of sparse coding coefficients. Thus, the promising classification performance and the powerful robustness can be obtained compared to the state-of-the-art methods.

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Li, Ying, Dongdong Weng, and Junyu Chen. "Non-Rigid Point Cloud Matching Based on Invariant Structure for Face Deformation." Electronics 12, no. 4 (February 6, 2023): 828. http://dx.doi.org/10.3390/electronics12040828.

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In this paper, we present a non-rigid point cloud matching method based on an invariant structure for face deformation. Our work is guided by the realistic needs of 3D face reconstruction and re-topology, which critically need support for calculating the correspondence between deformable models. Our paper makes three main contributions: First, we propose an approach to normalize the global structure features of expressive faces using texture space properties, which decreases the variation magnitude of facial landmarks. Second, we make a modification to the traditional shape context descriptor to solve the problem of regional cross-mismatch. Third, we collect a dataset with various expressions. Ablation studies and comparative experiments were conducted to investigate the performance of the above work. In face deformable cases, our method achieved 99.89% accuracy on our homemade face dataset, showing superior performance over some other popular algorithms. In this way, it can help modelers to build digital humans more easily based on the estimated correspondence of facial landmarks, saving a lot of manpower and time.

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Ermolai, Vasile, Gheorghe Nagîţ, Alexandru Sover, and Ioan Surugiu. "Design and Testing of Multi-Material Shape-Changing Flexure Hinges for Fused Filament Fabrication." Bulletin of the Polytechnic Institute of Iași. Machine constructions Section 68, no. 3 (September 1, 2022): 19–30. http://dx.doi.org/10.2478/bipcm-2022-0022.

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Abstract Living hinges are non-assemblable flexible joints that allow the relative rotation of two adjacent rigid parts through bending. Conventionally, living hinges are single-material designs made through injection moulding, for example. In order to reduce mould complexity, flexure hinges have a restrictive design. However, 3D printing technologies, such as Fused Filament Fabrication - FFF, can provide new opportunities for hinge development, allowing more design freedom and a wide range of materials. This paper focused on exploring and testing various multi-material hinges designs with bidirectional folding made of compatible and low-compatible thermoplastic materials. The resulting designs have corrugated structures and shape-changing interfaces, each with multiple solutions. They were printed as samples made of acrylonitrile butadiene styrene (ABS) and polylactic acid (PLA) for the rigid bodies and with thermoplastic co-polyesters (TPC) for the hinge. The results show that corrugated and shapechanging structures can be used as a design solution for flexure hinges.

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Kermarrec, Gaël, Niklas Schild, and Jan Hartmann. "Fitting Terrestrial Laser Scanner Point Clouds with T-Splines: Local Refinement Strategy for Rigid Body Motion." Remote Sensing 13, no. 13 (June 26, 2021): 2494. http://dx.doi.org/10.3390/rs13132494.

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T-splines have recently been introduced to represent objects of arbitrary shapes using a smaller number of control points than the conventional non-uniform rational B-splines (NURBS) or B-spline representatizons in computer-aided design, computer graphics and reverse engineering. They are flexible in representing complex surface shapes and economic in terms of parameters as they enable local refinement. This property is a great advantage when dense, scattered and noisy point clouds are approximated using least squares fitting, such as those from a terrestrial laser scanner (TLS). Unfortunately, when it comes to assessing the goodness of fit of the surface approximation with a real dataset, only a noisy point cloud can be approximated: (i) a low root mean squared error (RMSE) can be linked with an overfitting, i.e., a fitting of the noise, and should be correspondingly avoided, and (ii) a high RMSE is synonymous with a lack of details. To address the challenge of judging the approximation, the reference surface should be entirely known: this can be solved by printing a mathematically defined T-splines reference surface in three dimensions (3D) and modeling the artefacts induced by the 3D printing. Once scanned under different configurations, it is possible to assess the goodness of fit of the approximation for a noisy and potentially gappy point cloud and compare it with the traditional but less flexible NURBS. The advantages of T-splines local refinement open the door for further applications within a geodetic context such as rigorous statistical testing of deformation. Two different scans from a slightly deformed object were approximated; we found that more than 40% of the computational time could be saved without affecting the goodness of fit of the surface approximation by using the same mesh for the two epochs.

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Xiao, Di, David Zahra, Pierrick Bourgeat, Paula Berghofer, Oscar Acosta Tamayo, Catriona Wimberley, Marie Claude Gregoire, and Olivier Salvado. "An improved 3D shape context based non-rigid registration method and its application to small animal skeletons registration." Computerized Medical Imaging and Graphics 34, no. 4 (June 2010): 321–32. http://dx.doi.org/10.1016/j.compmedimag.2009.12.003.

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Fries, David, and Chase StarrGeran Barton. "2D PCB WITH 3D PRINT FABRICATIONS FOR RIGID-CONFORMAL PACKAGING OF MICROSENSOR IMAGING ARRAYS BASED ON BIOINSPIRED ARCHITECTURES." Additional Conferences (Device Packaging, HiTEC, HiTEN, and CICMT) 2014, DPC (January 1, 2014): 001012–45. http://dx.doi.org/10.4071/2014dpc-tp33.

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Macro sensor systems typically measure a localized space above a single sensor element. Expanding these single sensor elements into arrays permits spatial distribution measurements of a particular parameter and allow flux visualizations. Furthermore, applying microsystems technology to macro sensor systems yields imaging arrays and high resolution spatial/temporal sensing functions. Extending the high spatial resolution imaging over large areas is a desirable feature for new “vision” modes on autonomous robotic systems and for deployable environmental sensors. Rigid-flexible PCB's are desirable for miniaturization and integration of systems for mobile technology. The hybrid substrates provide substantial flexibility in systems design and integration of multiple functions into limited spaces. Using this design and construction approach allows lightweight, complex, and space efficient systems. Flex microsystems based on structured, fiber or non-fiber filled PCB laminates permits packaging to occur at two levels, at the local (micro) substrate scale and at the macro scale with the ability to flex the system across millimeter to centimeter lengths on real everyday systems. We continue to explore the use of PCB and PCBMEMS technology for new sensing concepts. In order to create rigid-conformal, large area imaging “camera” systems we have merged flexible PCB substrates with rigid constructions from 3D printing. This approach merges the 2D flexible electronics world of printed circuits with the 3D printed packaging world. Furthermore employing architectures used by biology as a basis for our imaging systems we explored naturally and biologically inspired designs, and their relationships to non-visible imagery, and alternate mechanical systems of perception. Radiolaria are extremely tiny ocean organisms that utilize a similar additive construction process to 3D printing. Their cell bodies secrete a substance mainly composed of silica to form intricate exoskeletons used as a system of protection. A correlation can be made between the radiolaria's construction process and the plastic extrusion system of the 3D fused deposition model printer. The benefits of additive construction are efficient use of materials, reduced cost and energy, and ability to customize forms. Through the use of bio-inspiration, a framework is laid out to base further research on (DFP)-design for packaging. Radiolarian exoskeletons take on a grid-like pattern while creating a cage around each microsensor interior and producing strong scaffolding. Using the 3D printed exoskeleton's form and function with flexible printed circuits one can create systems that are both rigid and form fitting with three-dimensional shape and enable new camera systems for various sensory applications.

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ZHOU, HUIYU, XUELONG LI, TANGWEI LIU, FAQUAN LIN, YUSHENG PANG, JI WU, JUNYU DONG, and JIAHUA WU. "RECOVERY OF NONRIGID STRUCTURES FROM 2D OBSERVATIONS." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 02 (March 2008): 279–94. http://dx.doi.org/10.1142/s0218001408006259.

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We present a new method for simultaneously determining three-dimensional (3D) motion and structure of a nonrigid object from its uncalibrated two-dimensional (2D) data with Gaussian or non-Gaussian distributions. A nonrigid motion can be treated as a combination of a rigid component and a nonrigid deformation. To reduce the high dimensionality of the deformable structure or shape, we estimate the probability distribution function (PDF) of the structure through random sampling, integrating an established probabilistic model. The fitting between the observations and the estimated 3D structure will be evaluated using the pooled variance estimator. The recovered structure is only available when the 2D feature points have been properly corresponded over two image frames. Applications of the proposed method to both synthetic and real image sequences are demonstrated with promising results.

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Oh, Donggeon, Bohyoung Kim, Jeongjin Lee, and Yeong-Gil Shin. "Unsupervised Deep Learning Network with Self-Attention Mechanism for Non-Rigid Registration of 3D Brain MR Images." Journal of Medical Imaging and Health Informatics 11, no. 3 (March 1, 2021): 736–51. http://dx.doi.org/10.1166/jmihi.2021.3345.

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In non-rigid registration for medical imaging analysis, computation is complicated, and the high accuracy and robustness needed for registration are difficult to obtain. Recently, many studies have been conducted for nonrigid registration via unsupervised learning networks. This study proposes a method to improve the performance of this unsupervised learning network approach, through the use of a self-attention mechanism. In this paper, the self-attention mechanism is combined with deep learning networks to identify information of higher importance, among large amounts of data, and thereby solve specific tasks. Furthermore, the proposed method extracts both local and non-local information so that the network can create feature vectors with more information. As a result, the limitation of the existing network is addressed: alignment based solely on the entire silhouette of the brain is mitigated in favor of a network which also learns to perform registration of the parts of the brain that have internal structural characteristics. To the best of our knowledge, this is the first such utilization of the attention mechanism in this unsupervised learning network for non-rigid registration. The proposed attention network performs registration that takes into account the overall characteristics of the data, thus yielding more accurate matching results than those of the existing methods. In particular, matching is achieved with especially high accuracy in the gray matter and cortical ventricle areas, since these areas contain many of the structural features of the brain. The experiment was performed on 3D magnetic resonance images of the brains of 50 people. The measured average dice similarity coefficient after registration was 70.40%, which is an improvement of 17.48% compared to that before registration. This improvement indicates that application of the attention block can further improve the performance by an additional 8.5%, as relative to that without attention block. Ultimately, through implementation of non-rigid registration via the attention block method, the internal structure and overall shape of the brain can be addressed, without additional data input. Additionally, attention blocks have the advantage of being able to easily connect to existing networks without a significant computational overhead. Furthermore, by producing an attention map, the area of the brain around which registration was more performed can be visualized. This approach can be used for non-rigid registration with various types of medical imaging data.

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Xue, Yun-Shan, Zhuo-Lin Chen, Youzhen Dong, and Wei-Wei Cheng. "Two Lanthanide Metal–Organic Frameworks Based on Semi-Rigid T-Shaped Tricarboxylate Ligand: Syntheses, Structures, and Properties." Polymers 11, no. 5 (May 13, 2019): 868. http://dx.doi.org/10.3390/polym11050868.

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By using a semi-rigid tripodal ligand 5-(4-carboxybenzyloxy)isophthalic acid (H3L) and lanthanide metal ions (Nd3+, Tb3+), two novel lanthanide metal–organic frameworks, namely, {[Nd2L2(DMF)4] DMF}n (1), and {TbL(DMF)(H2O)}n (2), were synthesized under mild solvothermal conditions and structurally characterized by X-ray single crystal diffraction. Compounds 1 and 2 are isostructural, in which L3– ligands linked dinuclear lanthanide metal–carboxylate units to form non-interpenetrated 3D network with (3,6)-connected topology. Luminescent investigations reveal that compound 1 displays the near-infrared emission at room temperature, and compound 2 can be employed as selective probe for Cr2O72− anion in aqueous solution based on luminescence quenching. Moreover, compound 2 exhibits catalytic activity for cyclo-addition of CO2 and epoxides under relatively mild conditions.

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Bahri, Mehdi, Eimear O’ Sullivan, Shunwang Gong, Feng Liu, Xiaoming Liu, Michael M. Bronstein, and Stefanos Zafeiriou. "Shape My Face: Registering 3D Face Scans by Surface-to-Surface Translation." International Journal of Computer Vision 129, no. 9 (July 10, 2021): 2680–713. http://dx.doi.org/10.1007/s11263-021-01494-4.

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AbstractStandard registration algorithms need to be independently applied to each surface to register, following careful pre-processing and hand-tuning. Recently, learning-based approaches have emerged that reduce the registration of new scans to running inference with a previously-trained model. The potential benefits are multifold: inference is typically orders of magnitude faster than solving a new instance of a difficult optimization problem, deep learning models can be made robust to noise and corruption, and the trained model may be re-used for other tasks, e.g. through transfer learning. In this paper, we cast the registration task as a surface-to-surface translation problem, and design a model to reliably capture the latent geometric information directly from raw 3D face scans. We introduce Shape-My-Face (SMF), a powerful encoder-decoder architecture based on an improved point cloud encoder, a novel visual attention mechanism, graph convolutional decoders with skip connections, and a specialized mouth model that we smoothly integrate with the mesh convolutions. Compared to the previous state-of-the-art learning algorithms for non-rigid registration of face scans, SMF only requires the raw data to be rigidly aligned (with scaling) with a pre-defined face template. Additionally, our model provides topologically-sound meshes with minimal supervision, offers faster training time, has orders of magnitude fewer trainable parameters, is more robust to noise, and can generalize to previously unseen datasets. We extensively evaluate the quality of our registrations on diverse data. We demonstrate the robustness and generalizability of our model with in-the-wild face scans across different modalities, sensor types, and resolutions. Finally, we show that, by learning to register scans, SMF produces a hybrid linear and non-linear morphable model. Manipulation of the latent space of SMF allows for shape generation, and morphing applications such as expression transfer in-the-wild. We train SMF on a dataset of human faces comprising 9 large-scale databases on commodity hardware.

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Corsia, M., T. Chabardès, H. Bouchiba, and A. Serna. "LARGE SCALE 3D POINT CLOUD MODELING FROM CAD DATABASE IN COMPLEX INDUSTRIAL ENVIRONMENTS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (August 12, 2020): 391–98. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-391-2020.

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Abstract. In this paper, we present a method to build Computer Aided Design (CAD) representations of dense 3D point cloud scenes by queries in a large CAD model database. This method is applied to real world industrial scenes for infrastructure modeling. The proposed method firstly relies on a region growing algorithm based on novel edge detection method. This algorithm is able to produce geometrically coherent regions which can be agglomerated in order to extract the objects of interest of an industrial environment. Each segment is then processed to compute relevant keypoints and multi-scale features in order to be compared to all CAD models from the database. The best fitting model is estimated together with the rigid six degree of freedom (6 DOF) transformation for positioning the CAD model on the 3D scene. The proposed novel keypoints extractor achieves robust and repeatable results that captures both thin geometrical details and global shape of objects. Our new multi-scale descriptor stacks geometrical information around each keypoint at short and long range, allowing non-ambiguous matching for object recognition and positioning. We illustrate the efficiency of our method in a real-world application on 3D segmentation and modeling of electrical substations.

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Niu, Hui, Takahiro Ito, Damien Desclaux, Ko Ayusawa, Yusuke Yoshiyasu, Ryusuke Sagawa, and Eiichi Yoshida. "Estimating Muscle Activity from the Deformation of a Sequential 3D Point Cloud." Journal of Imaging 8, no. 6 (June 13, 2022): 168. http://dx.doi.org/10.3390/jimaging8060168.

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Estimation of muscle activity is very important as it can be a cue to assess a person’s movements and intentions. If muscle activity states can be obtained through non-contact measurement, through visual measurement systems, for example, muscle activity will provide data support and help for various study fields. In the present paper, we propose a method to predict human muscle activity from skin surface strain. This requires us to obtain a 3D reconstruction model with a high relative accuracy. The problem is that reconstruction errors due to noise on raw data generated in a visual measurement system are inevitable. In particular, the independent noise between each frame on the time series makes it difficult to accurately track the motion. In order to obtain more precise information about the human skin surface, we propose a method that introduces a temporal constraint in the non-rigid registration process. We can achieve more accurate tracking of shape and motion by constraining the point cloud motion over the time series. Using surface strain as input, we build a multilayer perceptron artificial neural network for inferring muscle activity. In the present paper, we investigate simple lower limb movements to train the network. As a result, we successfully achieve the estimation of muscle activity via surface strain.

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Lo, Der-Chang, and Yuan-Shiang Tsai. "A 3D Fully Non-Hydrostatic Model for Free-Surface Flows with Complex Immersed Boundaries." Water 14, no. 23 (November 22, 2022): 3803. http://dx.doi.org/10.3390/w14233803.

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A fully non-hydrostatic hydrodynamic model is developed to simulate a three-dimensional, incompressible, and viscous free-surface flow passing downstream rigid rectangular and circular cylinders. A direct numerical simulation (DNS) based on the volume of fluid (VOF) and immersed boundary (IB) method is presented for solving the Navier–Stokes equations. The numerical scheme provides accurate solutions with high efficiency using the novel computational procedure to model severe surface deformations. A staggered finite difference method with a Cartesian mesh coordinate system is used to discretize the governing equations with the complexity of the deformed free-surface flow, for which the numerical schemes include a free-surface tracking technique based on the VOF and a VOS-based IB method to simulate 3D dam-break flows passing the slender objects. Additionally, the case studies demonstrate the accuracy and flexibility of the proposed model to predict the impact forces of the surface flow against the different configurations of structures. The results reveal that the temporal variation of the impact force acted on the rectangular obstacle is dominated by the aspect ratio. The force increases with the increase in the shape parameter. The resistance caused by a thin obstacle is considerably less than the blunt shape.

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Park, Byung-Seo, Sol Lee, Jung-Tak Park, Jin-Kyum Kim, Woosuk Kim, and Young-Ho Seo. "Dynamic Reconstruction and Mesh Compression of 4D Volumetric Model Using Correspondence-Based Deformation for Streaming Service." Sensors 22, no. 22 (November 15, 2022): 8815. http://dx.doi.org/10.3390/s22228815.

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A sequence of 3D models generated using volumetric capture has the advantage of retaining the characteristics of dynamic objects and scenes. However, in volumetric data, since 3D mesh and texture are synthesized for every frame, the mesh of every frame has a different shape, and the brightness and color quality of the texture is various. This paper proposes an algorithm to consistently create a mesh of 4D volumetric data using dynamic reconstruction. The proposed algorithm comprises remeshing, correspondence searching, and target frame reconstruction by key frame deformation. We make non-rigid deformation possible by applying the surface deformation method of the key frame. Finally, we propose a method of compressing the target frame using the target frame reconstructed using the key frame with error rates of up to 98.88% and at least 20.39% compared to previous studies. The experimental results show the proposed method’s effectiveness by measuring the geometric error between the deformed key frame and the target frame. Further, by calculating the residual between two frames, the ratio of data transmitted is measured to show a compression performance of 18.48%.

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Acosta, Oscar, Jurgen Fripp, Vincent Doré, Pierrick Bourgeat, Jean-Marie Favreau, Gaël Chételat, Andrea Rueda, et al. "Cortical surface mapping using topology correction, partial flattening and 3D shape context-based non-rigid registration for use in quantifying atrophy in Alzheimer's disease." Journal of Neuroscience Methods 205, no. 1 (March 2012): 96–109. http://dx.doi.org/10.1016/j.jneumeth.2011.12.011.

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Rahman, Mustafizur, Evgeny V. Morozov, Krishna Shankar, and Alan Fien. "Computational Analysis of Low Velocity Impact Response of Composite Panels." Applied Mechanics and Materials 157-158 (February 2012): 1135–38. http://dx.doi.org/10.4028/www.scientific.net/amm.157-158.1135.

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The present work deals with the finite element modelling of low velocity impact response of different types of composite panels for body armour application. The response of these composites panels including bonded, unbonded and partially bonded laminates has been simulated using non-linear finite element package LS-DYNA. 2D shell elements in LS-DYNA have been used to represent both resin bonded glass fabric targets and dry woven glass fabric panels. The hemispherical shaped projectile is being modelled with 3D solid elements. The results of the numerical analysis showed that the value of contact force for the fully bonded composites panels was significantly higher than that observed for the panels consisting of dry woven glass fabric. However, the corresponding displacement was substantially lower. The similar simulation of the partially bonded composite panels has shown a reduction of both the contact force and the displacement. In addition, it has been shown that the partially bonded composite panels are capable of absorbing higher levels of energy than the rigid panels.

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Steinbruecker, F., A. Meyer-Baese, T. Schlossbauer, and D. Cremers. "Evaluation of a Nonrigid Motion Compensation Technique Based on Spatiotemporal Features for Small Lesion Detection in Breast MRI." Advances in Artificial Neural Systems 2012 (September 6, 2012): 1–10. http://dx.doi.org/10.1155/2012/808602.

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Motion-induced artifacts represent a major problem in detection and diagnosis of breast cancer in dynamic contrast-enhanced magnetic resonance imaging. The goal of this paper is to evaluate the performance of a new nonrigid motion correction algorithm based on the optical flow method. For each of the small lesions, we extracted morphological and dynamical features describing both global and local shape, and kinetics behavior. In this paper, we compare the performance of each extracted feature set under consideration of several 2D or 3D motion compensation parameters for the differential diagnosis of enhancing lesions in breast MRI. Based on several simulation results, we determined the optimal motion compensation parameters. Our results have shown that motion compensation can improve the classification results. The results suggest that the computerized analysis system based on the non-rigid motion compensation technique and spatiotemporal features has the potential to increase the diagnostic accuracy of MRI mammography for small lesions and can be used as a basis for computer-aided diagnosis of breast cancer with MR mammography.

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Journal articles on the topic '3D non-rigid shapes'

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