Volume and complexity bounded simplification of solid model represented by binary space partition

2010 ◽  
Author(s):  
Pu Huang ◽  
Charlie C. L. Wang
Keyword(s):  
Solid Model ◽  
Binary Space Partition ◽  
Space Partition

scholarly journals Improved Binary Space Partition merging

2008 ◽  
Vol 40 (12) ◽  
pp. 1113-1120 ◽  
Author(s):  
Mikola Lysenko ◽  
Roshan D’Souza ◽  
Ching-Kuan Shene
Keyword(s):  
Binary Space Partition ◽  
Space Partition

PARALLEL SOLID MODELING USING BSP DATAFLOW

2008 ◽  
Vol 18 (05) ◽  
pp. 441-467 ◽  
Author(s):  
GIORGIO SCORZELLI ◽  
ALBERTO PAOLUZZI ◽  
VALERIO PASCUCCI
Keyword(s):  
Geometric Modeling ◽  
Large Scale ◽  
Solid Modeling ◽  
Complex Objects ◽  
Dataflow Graph ◽  
Object Space ◽  
Binary Space Partition ◽  
Space Partition ◽  
Hidden Surface Removal ◽  
Computational Processes

We introduce a parallel approach to geometric modeling of complex objects and scenes, combining a dataflow streaming of BSP trees with a partition of the object space into independent portions, to be evaluated in parallel with minimal interprocess communication. Binary Space Partition (BSP) is a space index used in graphics for hidden-surface removal and animation. We use BSP trees with fuzzy leaves as a progressive representation of solid meshes. Our approach is implemented as a dataflow with processes that progress concurrently, where each refinement of the input to a process is mapped instantly to a refinement of the output, so that the result is also a stream of progressive refinements. This framework allows for progressive generation of complex geometric parts and large-scale assemblies. We have adapted several graphics techniques, including BSP, boundary polygons, CSG, splines and subdivision methods, to fit into our dataflow graph, where four types of processes produce, transform, combineor consume mesh cells. This approach is scalable over different kinds of HPC hardware and different number of computing nodes, by way of the decomposition of the object space and of the distribution of computational processes. Compiling a generative geometric expression into a dataflow graph is well suited to SMP machines, whereas a space decomposition into independent portions fits well with computing clusters and grids.

scholarly journals Steganography: Securing Message in wireless network

2013 ◽  
Vol 4 (3) ◽  
pp. 797-801 ◽  
Author(s):  
A H M Kamal
Keyword(s):  
Wireless Network ◽  
True Positive Rate ◽  
Modulus Function ◽  
Secret Message ◽  
Least Significant Bit ◽  
Lsb Matching ◽  
Survey Paper ◽  
Binary Space Partition ◽  
Space Partition ◽  
Noise Margin

Steganography is the process of hiding a secret message with in a cover medium. However eavesdropper may guess the embedding algorithm like least significant bit (LSB) replacement of Chan et al, 2004; Wang et al, 2001; Wu et al, 2005, LSB matching of Mielikainen, 2006, addition and/or subtraction of Andead wastfield, 2001; F. Huang et al in 2011, Exploiting Modification Direction by Zhang and Wang, 2006, Binary Space Partition by Tsai and Wang, 2007, modulus function of Chin et al, 2011 and thus can apply the respective extraction method to detect the secret message. So challenges lies in the methodologies of embedding message. Capacity, security and robustness are the services to be demanded by users. Again the true-positive rate of secret message detection by eavesdropper should be lessened by applying firm technique. Thirdly operating domain should be less sensitive to the noise, margin level of losses or alteration of data while communicating through unguided medium like wireless network, sensor network and cellular network. This paper will briefly discuss the steganographic methods and their experimental results explained in the survey paper of Niels Provos and Peter Honeyman to hide and seek message. Finally the proposed results and the directions for future works are addressed.

scholarly journals Smart Nearest Neighbors

2011 ◽  
Author(s):  
David Doria ◽  
Wanlin Zhu
Keyword(s):  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
Spatial Arrangement ◽  
K Nearest Neighbors ◽  
Binary Space Partition ◽  
Space Partition ◽  
3D Geometry ◽  
Local Duplication ◽  
Point Set ◽  
Better Than

This document presents an implementation of two algorithms, Voronoi Neighbors and Binary Space Partition (BSP) Neighbors. These algorithms find neighbors of a point in a point set that are somehow better'' than aK nearest neighbors’’ or a ``all neighbors within a radius’’ query. This type of nearest neighbor query is more computationally expensive, but results in set of neighbors with more desirable properties. The BSP Neighbors search ensures that there is less local duplication, while the Voronoi Neighbors search ensures that the spatial arrangement of the neighbors is as uniform as possible.These algorithms are explained in ``Point Primitives for Interactive Modeling and Processing of 3D Geometry’’.The code is available here: https://github.com/daviddoria/SmartNearestNeighbors

scholarly journals SEPARABILITY OF POINT SETS BY k-LEVEL LINEAR CLASSIFICATION TREES

2012 ◽  
Vol 22 (02) ◽  
pp. 143-165
Author(s):  
ESTHER M. ARKIN ◽  
DELIA GARIJO ◽  
ALBERTO MÁRQUEZ ◽  
JOSEPH S. B. MITCHELL ◽  
CARLOS SEARA
Keyword(s):  
General Position ◽  
Internal Node ◽  
Linear Classification ◽  
Minimum Level ◽  
Binary Space Partition ◽  
Space Partition ◽  
Axis Parallel ◽  
Level 3 ◽  
Special Case ◽  
Convex Cell

Let R and B be sets of red and blue points in the plane in general position. We study the problem of computing a k-level binary space partition (BSP) tree to classify/separate R and B, such that the tree defines a linear decision at each internal node and each leaf of the tree corresponds to a (convex) cell of the partition that contains only red or only blue points. Specifically, we show that a 2-level tree can be computed, if one exists, in time O(n2). We show that a minimum-level (3 ≤ k ≤ log n) tree can be computed in time nO( log n). In the special case of axis-parallel partitions, we show that 2-level and 3-level trees can be computed in time O(n), while a minimum-level tree can be computed in time O(n5).

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