The two stages hierarchical unsupervised learning system for complex dynamic scene recognition

2013 ◽  
Author(s):  
James Graham ◽  
Alan O'Connor ◽  
Igor V. Ternovskiy ◽  
Roman Ilin
Keyword(s):  
Unsupervised Learning ◽  
Scene Recognition ◽  
Learning System ◽  
Dynamic Scene ◽  
Complex Dynamic ◽  
Two Stages

scholarly journals BRLO-Tree: A Data Structure Used for 3D GIS Dynamic Scene Rendering

2015 ◽  
Vol 15 (4) ◽  
pp. 124-137 ◽  
Author(s):  
Wenju Wang ◽  
Zhang Xuan ◽  
Liujie Sun ◽  
Zhongmin Jiang ◽  
Jingjing Shang
Keyword(s):  
Data Structure ◽  
Large Scale ◽  
Three Dimensional ◽  
Level Of Detail ◽  
Dynamic Scene ◽  
3D Gis ◽  
Dynamic Scenes ◽  
Complex Dynamic ◽  
3D Objects ◽  
System A

Abstract BRLO-Tree (Block-R-Tree-Loose-Octree) is presented in this paper based on the R-Tree and Loose-Octree. The aim of the structure is to visualize the large scale and complex dynamic scenes in a 3D (three-dimensional) GIS (Geographic Information System). A new method of clustering rectangles to construct R-tree based on an improved K-means algorithm is put forward. Landform in 3D GIS is organized by R-Tree. The block is used as the basic rendering unit. The 3D objects of each block are respectively organized by a Loose-Octree. A series of techniques, based on this data structure, such as LOD (Level of Detail), relief impostors are integrated. The results of the tests show that BRLO-Tree cannot only support the large scale 3D GIS scene exhibition with wandering and fighting, but it can also efficiently manage the models in a dynamic scene. At the same time, a set of integrated techniques based on BRLO-Tree can make the rendering pictures more fluence and the rendering time vastly improved.

A Super Descriptor Tensor Decomposition for Dynamic Scene Recognition

2019 ◽  
Vol 29 (4) ◽  
pp. 1063-1076 ◽  
Author(s):  
Muhammad Rizwan Khokher ◽  
Abdesselam Bouzerdoum ◽  
Son Lam Phung
Keyword(s):  
Tensor Decomposition ◽  
Scene Recognition ◽  
Dynamic Scene
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