Interactive Hierarchical Level of Detail Level Selection Algorithm for Point Based Rendering

2007 ◽  
pp. 59-69
Author(s):  
XueMei Lu ◽  
Ki-Jung Lee ◽  
Taeg-Keun Whangbo
Keyword(s):  
Level Of Detail ◽  
Hierarchical Level ◽  
Selection Algorithm

Hierarchical level of detail for varied animated crowds

2014 ◽  
Vol 30 (6-8) ◽  
pp. 949-961 ◽  
Author(s):  
Leonel Toledo ◽  
Oriam De Gyves ◽  
Isaac Rudomín
Keyword(s):  
Level Of Detail ◽  
Hierarchical Level

A Negative Selection Algorithm Based on Hierarchical Clustering of Self Set

2010 ◽  
Vol 121-122 ◽  
pp. 486-489
Author(s):  
Wen Chen ◽  
Tao Li ◽  
Xiao Jie Liu ◽  
Yuan Quan Shi
Keyword(s):  
Negative Selection ◽  
The Self ◽  
Hierarchical Level ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Data Set ◽  
Cluster Radius ◽  
Negative Selection Algorithm ◽  
Value Range ◽  
Level Cluster

In this article, we proposed a negative selection algorithm which based on hierarchical level cluster of self dataset CB-RNSA. First the self data set is clustered by different cluster radius, and then the self data are substituted by cluster centers to compare with candidate detectors to reduce the number of distance counting. In the detector creating process, the value of each detector property was restricted to a given value range so as to decrease the redundancy of detectors. The stimulation result shows that CB-RNSA is an effective algorithm for the creation of artificial immune detectors.

scholarly journals Automatic Hierarchical Level of Detail Optimization in Computer Animation

1997 ◽  
Vol 16 (3) ◽  
pp. C191-C199 ◽  
Author(s):  
Ashton E.W. Mason ◽  
Edwin H. Blake
Keyword(s):  
Computer Animation ◽  
Level Of Detail ◽  
Hierarchical Level

scholarly journals Local Shape Analysis of the Hippocampus using Hierarchical Level-of-Detail Representations

2004 ◽  
Vol 11A (7) ◽  
pp. 555-562
Author(s):  
Jeong-Sik Kim ◽  
Soo-Mi Choi ◽  
Yoo-Ju Choi ◽  
Myoung-Hee Kim
Keyword(s):  
Shape Analysis ◽  
Level Of Detail ◽  
Hierarchical Level ◽  
Local Shape

scholarly journals Scalable processing of massive geodata in the cloud: generating a level-of-detail structure optimized for web visualization

2020 ◽  
Vol 1 ◽  
pp. 1-20
Author(s):  
Michel Krämer ◽  
Ralf Gutbell ◽  
Hendrik M. Würz ◽  
Jannis Weil
Keyword(s):  
Input Data ◽  
Geometric Error ◽  
Level Of Detail ◽  
Divide And Conquer ◽  
Hierarchical Level ◽  
Web Browser ◽  
Hierarchical Tree ◽  
Levels Of Detail ◽  
Web Visualization ◽  
Different Levels

Abstract. We present a cloud-based approach to transform arbitrarily large terrain data to a hierarchical level-of-detail structure that is optimized for web visualization. Our approach is based on a divide-and-conquer strategy. The input data is split into tiles that are distributed to individual workers in the cloud. These workers apply a Delaunay triangulation with a maximum number of points and a maximum geometric error. They merge the results and triangulate them again to generate less detailed tiles. The process repeats until a hierarchical tree of different levels of detail has been created. This tree can be used to stream the data to the web browser. We have implemented this approach in the frameworks Apache Spark and GeoTrellis. Our paper includes an evaluation of our approach and the implementation. We focus on scalability and runtime but also investigate bottlenecks, possible reasons for them, as well as options for mitigation. The results of our evaluation show that our approach and implementation are scalable and that we are able to process massive terrain data.

One Million Direct Magnification Microscopy

1971 ◽  
Vol 29 ◽  
pp. 166-167
Author(s):  
J. A. Hugo ◽  
V. A. Phillips
Keyword(s):  
Electron Microscopy ◽  
High Resolution Electron Microscopy ◽  
Illumination Level ◽  
Level Of Detail ◽  
Photographic Emulsion ◽  
Low Contrast ◽  
Fluorescent Screen ◽  
Resolution Electron ◽  
Lattice Images ◽  
Electron Microscopes

A continuing problem in high resolution electron microscopy is that the level of detail visible to the microscopist while he is taking a picture is inferior to that obtainable by the microscope, readily readable on a photographic emulsion and visible in an enlargement made from the plate. Line resolutions, of 2Å or better are now achievable with top of the line 100kv microscopes. Taking the resolution of the human eye as 0.2mm, this indicates a need for a direct viewing magnification of at least one million. However, 0.2mm refers to optimum viewing conditions in daylight or the equivalent, and certainly does not apply to a (colored) image of low contrast and illumination level viewed on a fluorescent screen through a glass window by the dark-adapted eye. Experience indicates that an additional factor of 5 to 10 magnification is needed in order to view lattice images with line spacings of 2 to 4Å. Fortunately this is provided by the normal viewing telescope supplied with most electron microscopes.

The Nature of Oxide Surfaces: Topographic and Electronic Structure

1993 ◽  
Vol 51 ◽  
pp. 966-967
Author(s):  
Dawn A. Bonnell ◽  
Yong Liang
Keyword(s):  
Transition Metal Oxides ◽  
Electronic Structures ◽  
Recent Progress ◽  
Spatial Localization ◽  
Level Of Detail ◽  
Scanning Tunneling ◽  
Oxide Surfaces ◽  
Tunneling Microscopy ◽  
Considerable Effort ◽  
Complete Understanding

Recent progress in the application of scanning tunneling microscopy (STM) and tunneling spectroscopy (STS) to oxide surfaces has allowed issues of image formation mechanism and spatial resolution limitations to be addressed. As the STM analyses of oxide surfaces continues, it is becoming clear that the geometric and electronic structures of these surfaces are intrinsically complex. Since STM requires conductivity, the oxides in question are transition metal oxides that accommodate aliovalent dopants or nonstoichiometry to produce mobile carriers. To date, considerable effort has been directed toward probing the structures and reactivities of ZnO polar and nonpolar surfaces, TiO2 (110) and (001) surfaces and the SrTiO3 (001) surface, with a view towards integrating these results with the vast amount of previous surface analysis (LEED and photoemission) to build a more complete understanding of these surfaces. However, the spatial localization of the STM/STS provides a level of detail that leads to conclusions somewhat different from those made earlier.

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