A Blind Watermarking of 3D Triangular Meshes Using Geometry Image

title: Boundary Constrained Manifold Unfolding

title: Mesh Editing in ROI with Dual Laplacian

title: Rate-Distortion Optimized Progressive Geometry Compression
abstract: 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. Rate-distortion optimization; Progressive compression; Mesh partition

title: Cut-and-Paste Editing over 3D Meshes
abstract: 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

title: An Energy-Minimizing Mesh Parameterization
abstract: 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.

title: Semi-supervised Text Classification Using Partitioned EM
abstract: Text classification using a small labeled set and a large unlabeled data is seen as a promising technique to reduce the labor-intensive and time consuming effort of labeling training data in order to build accurate classifiers since unlabeled data is easy to get from the Web. In  it has been demonstrated that an unlabeled set improves classification accuracy significantly with only a small labeled training set. However, the Bayesian method used in [16] assumes that text documents are generated from a mixture model and there is a one-to-one correspondence between the mixture components and the classes. This may not be the case in many applications. In many real-life applications, a class may cover documents from many different topics, which violates the one-to-one correspondence assumption. In such cases, the resulting classifiers can be quite poor. In this paper, we propose a clustering based partitioning technique to solve the problem. This method first partitions the training documents in a hierarchical fashion using hard clustering. After running the expectation maximization (EM) algorithm in each partition, it prunes the tree using the labeled data. The remaining tree nodes or partitions are likely to satisfy the one-to-one correspondence condition. Extensive experiments demonstrate that this method is able to achieve a dramatic gain in classification performance.

title: Positive Unlabeled Learning for Data Stream Classification
abstract: Learning from positive and unlabeled examples (PU learning) has been investigated in recent years as an alternative learning model for dealing with situations where negative training examples are not available. It has many real world applications, but it has yet to be applied in the data stream environment where it is highly possible that only a small set of positive data and no negative data is available. An important challenge is to address the issue of concept drift in the data stream environment, which is not easily handled by the traditional PU learning techniques. This paper studies how to devise PU learning techniques for the data stream environment. Unlike existing data stream classification methods that assume both positive and negative training data are available for learning, we propose a novel PU learning technique LELC (PU Learning by Extracting Likely positive and negative micro-Clusters) for document classification. LELC only requires a small set of positive examples and a set of unlabeled examples which is easily obtainable in the data stream environment to build accurate classifiers. Experimental results show that LELC is a PU learning method that can effectively address the issues in the data stream environment with significantly better speed and accuracy on capturing concept drift than the existing state-of-the-art PU learning techniques.

title: Positive solutions of a nonlinear three-point boundary value problem
abstract: In this paper, by using Krasnoselskii's fixed point theorem in a cone, we study the existence of single and multiple positive solutions to the three-point boundary value problem (BVP)y''(t)+a(t)f(y(t))=0,0

title: Mining topic-specific concepts and definitions on the web
abstract: Traditionally, when one wants to learn about a particular topic, one reads a book or a survey paper. With the rapid expansion of the Web, learning in-depth knowledge about a topic from the Web is becoming increasingly important and popular. This is also due to the Web's convenience and its richness of information. In many cases, learning from the Web may even be essential because in our fast changing world, emerging topics appear constantly and rapidly. There is often not enough time for someone to write a book on such topics. To learn such emerging topics, one can resort to research papers. However, research papers are often hard to understand by non-researchers, and few research papers cover every aspect of the topic. In contrast, many Web pages often contain intuitive descriptions of the topic. To find such Web pages, one typically uses a search engine. However, current search techniques are not designed for in-depth learning. Top ranking pages from a search engine may not contain any description of the topic. Even if they do, the description is usually incomplete since it is unlikely that the owner of the page has good knowledge of every aspect of the topic. In this paper, we attempt a novel and challenging task, mining topic-specific knowledge on the Web. Our goal is to help people learn in-depth knowledge of a topic systematically on the Web. The proposed techniques first identify those sub-topics or salient concepts of the topic, and then find and organize those informative pages, containing definitions and descriptions of the topic and sub-topics, just like those in a book. Experimental results using 28 topics show that the proposed techniques are highly effective.

title: An New Global Dynamic Scheduling Algorithm with Multi-Hop Path Splitting and Multi-Pathing Using GridFTP