17077403
Rate-Distortion Optimized Progressive Geometry Compression
During progressive transmission of 3D geometry models, the transmission order of details at different region has great effects on the quality of reconstructed models at low bit-rate. This work presents a ratedistortion (R-D) optimized progressive geometry compression scheme to improve the quality of reconstructed models by adjusting the transmission order of details. In this scheme, the input mesh is partitioned into parts, then each part is encoded into bit-stream independently, and the encoded bit-streams are truncated into segments while getting the R-D characteristics of every segment, at last all segments are assembled into a codestream based on R-D optimization, which ensure the region with rich detail will be transmitted early and make the reconstructed mesh achieve better quality as soon as possible. Experimental results show that, as compared with the well-known PGC method, the proposed one provides better R-D performance. Moreover, it provides a novel way to realize the region of interest (ROI) coding of 3D meshes. Keywords--Rate-distortion optimization; Progressive compression; Mesh partition
17309491
Multi-resolution Meshes Deformation Based on Pyramid Coordinates
In this paper, we present a technique for multi-resolution meshes deformation based on pyramid coordinates. Pyramid coordinates are rotation invariant, and can achieve low distortion editing for large deformations. Their drawback is that the reconstruction process is nonlinear and time consuming especially for large meshes. In order to decrease the computational cost, the proposed method reduces the number of vertices involved in the deformation. We first construct a simplified mesh using a modified CPM algorithm; then edit the simplified meshes by pyramid coordinates based mesh detail presentation; finally, series of details are added to the deformed simplified mesh. Our algorithm can generate natural looking deformations more efficiently than the original pyramid coordinates method.
29104494
Mesh Editing in ROI with Dual Laplacian
39145518
Boundary Constrained Manifold Unfolding
15959526
Cut-and-Paste Editing over 3D Meshes
A novel direct cut-and-paste editing method over 3D triangular meshes is proposed in this paper. The source feature mesh is transferred to the position where it is to be pasted through the corresponding planes. The planes are created using the least squares approximation. To decrease the distortion of pasting region, the local mesh representation is used, and the pasted region is reconstructed in the least squares sense. Then, the boundary vertices of the source feature mesh are connected with the target mesh. This method is applicable to source geometry features with non-zero genus. It can be performed at interactive rates and produces less distortion and natural results
32625268
An Energy-Minimizing Mesh Parameterization
In this paper, we propose a new energy-minimizing mesh parameterization method, which linearly combines two new energies EQ and EM. It not only avoids triangles overlap in the parameter domain, but also is invariant under rotation, translation and scale transformations. We first parameterize the original 3D mesh to the parameter plane by using the energy-minimizing parameterization, and get the optimal effect by optimizing the weights wij gradually. Experimental results indicate that this optimized energy-minimizing method has low distortion and good stability.
13166441
WISE-2005 Tutorial: Web Content Mining
Web mining aims to develop a new generation of techniques to effectively mine useful information or knowledge from the Web. It consists of Web usage mining, Web structure mining, and Web content mining. Web usage mining refers to the discovery of user access patterns from Web usage logs. Web structure mining tries to discover useful knowledge from the structure of Web hyperlinks. Web content mining aims to extract and mine useful information or knowledge from Web page contents. This tutorial focuses on Web Content Mining. In the past few years, there was a rapid expansion of activities in this area. In this tutorial, I will introduce the main web content mining tasks and problems and state-of-the-art techniques for dealing with them. All parts of the tutorial have a mix of research and industry flavor, addressing seminal research concepts and looking at the technology from an industry angle.
16428264
SQUARE: A New TCP Variant for Future High Speed and Long Delay Environments
The increasing diversity of Internet application requirements has spurred recent interest in transport protocol for high speed delay product connectivity. Addressing the deficiency of previous protocols, this paper presents a spectrum of time based odd function congestion control protocols for deployment in high speed and long distance networks. Through extensive analysis we focus on SQUARE, one of the protocols in odd function congestion control. Without the need to revise the current end-to-end architecture, SQUARE is shown to be efficient when there is bandwidth available, to be fair when many flows compete with each other for the same bottleneck, to be friendly when deployed with the conventional TCP, to be robust when there are oscillations in the network.
159216
A weight based compact genetic algorithm
In order to improve the performance of the compact Genetic Algorithm (cGA) to solve difficult optimization problems, an improved cGA which named as the weight based compact Genetic Algorithm (wcGA) is proposed. In the wcGA, S individuals are generated from the probability vector in each generation, when the winner competing with the other S-1 individuals to update the probability vector, different weights are multiplied to each solution according to the sequence of the solution ranked in the S-1 individuals. Experimental results on three kinds of Benchmark functions show that the proposed algorithm has higher optimal precision than that of the standard cGA and the cGA simulating higher selection pressures.
11065479
Selector: A Language Construct for Developing Dynamic Applications
Fitting algorithms to meet input data characteristics and/or a changing computing environment is a tedious and error prone task. Programmers need to deal with code instrumentation details and implement the selection of which algorithm best suits a given data set. In this paper we describe a set of simple programming constructs for C that allows programmers to specify and generate applications that can select at run-time the best of several possible implementations based on measured run-time performance and/or algorithmic input values. We describe the application of this approach to a realistic linear solver for an engineering crash analysis code. The preliminary experimental results reveal that this approach provides an effective mechanism for creating sophisticated dynamic application behavior with minimal effort.
42555464
Nesting Algorithm for Multi-Classification Problems
Support vector machines (SVMs) are originally designed for binary classifications. As for multi-classifications, they are usually converted into binary ones. In the conventional multi-classifiable algorithms, One-against-One algorithm is a very power method. However, there exists a middle unclassifiable region. In order to overcome this drawback, a novel method called Nesting Algorithm is presented in this paper. Our ideas are as follows: firstly, construct the optimal hyperplanes based on One-against-One approach. Secondly, if there exist data points in the middle unclassifiable region, select them to construct the optimal hyperplanes with the same hyperparameters. Thirdly, repeat the second step until there are no data points in the unclassifiable region or the region is disappeared. In this paper, we also prove the validity of the proposed algorithm for unclassifiable region and give the computational complexity analysis of the method. In order to examine the training accuracy and the generalization performance of the proposed algorithm, One-against-One algorithm, fuzzy least square support vector machine (FLS-SVM) and the proposed algorithm are applied to five UCI datasets. The results show that the training accuracy of the proposed algorithm is higher than the others, and its generalization performance is also comparable with them.