Key independent encrypted face clustering

Author(s):  
Kannan Karthik ◽  
Harshit Balaraman
Keyword(s):  
2018 ◽  
Vol 79 ◽  
pp. 279-289 ◽  
Author(s):  
Xiaoshuang Shi ◽  
Zhenhua Guo ◽  
Fuyong Xing ◽  
Jinzheng Cai ◽  
Lin Yang

Author(s):  
Biswaroop Palit ◽  
Rakesh Nigam ◽  
Keren Perlmutter ◽  
Sharon Perlmutter
Keyword(s):  

2015 ◽  
Vol 45 (8) ◽  
pp. 1681-1691 ◽  
Author(s):  
Xiaobing Pei ◽  
Zehua Lyu ◽  
Changqing Chen ◽  
Chuanbo Chen

Author(s):  
Jun Doi ◽  
Atsushi Yamada ◽  
Keisuke Inoue

Finite element analysis has become a key technology for a design process of manufacturing industry. A hexahedral mesh is focused, because using a hexahedral mesh increases the quality of analysis. However it is very difficult problem to generate high quality hexahedral meshes, and there are many challenging research topics. Our goal is to develop a method to generate hexahedral meshes automatically to general volumes. Our method uses an intermediate model to recognize the input volume. The intermediate model is defined in the integer 3-dimensional space, and faces of the intermediate model are vertical to coordinate axes. Hexahedral mesh is generated by dividing the intermediate model into integer grids, and blocks of grids are projected into original volume. In this paper, we describe the method to generate a topology of the intermediate model. We use face clustering technique to generate the topology of the intermediate model. The faces of the input volume are clustered into 6 types; according to 3 coordinate axes and its direction, and clustered faces will be the faces of the intermediate model.


2020 ◽  
Vol 6 (4) ◽  
pp. 431-443
Author(s):  
Xiaolong Yang ◽  
Xiaohong Jia

AbstractWe present a simple yet efficient algorithm for recognizing simple quadric primitives (plane, sphere, cylinder, cone) from triangular meshes. Our approach is an improved version of a previous hierarchical clustering algorithm, which performs pairwise clustering of triangle patches from bottom to top. The key contributions of our approach include a strategy for priority and fidelity consideration of the detected primitives, and a scheme for boundary smoothness between adjacent clusters. Experimental results demonstrate that the proposed method produces qualitatively and quantitatively better results than representative state-of-the-art methods on a wide range of test data.


2020 ◽  
Vol 2 (2) ◽  
pp. 145-157 ◽  
Author(s):  
Vivek Sharma ◽  
Makarand Tapaswi ◽  
M. Saquib Sarfraz ◽  
Rainer Stiefelhagen

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