17229395
Region-of-interest coding of 3D mesh based on wavelet transform
A scheme for the region of interest (ROI) coding of 3D meshes is proposed for the first time. The ROI is encoded with higher fidelity than the rest region, and the "priority" of ROI relative to the rest region (background, BG) can be specified by encoder or decoder (user). Wavelet transform is used on 3D mesh and zerotrees are adopted to organize the coefficients. The wavelet coefficients of ROI are scaled up and encoded with a modified set partitioning in hierarchical trees (SPIHT) algorithm. In additional, a fast algorithm is proposed for creating the ROI mask. Once the quality of reconstructed ROI becomes high enough, the transmission can be intermitted and much transmission bandwidth and storage space will be saved consequently.
39145518
Boundary Constrained Manifold Unfolding
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.
13551397
A Blind Watermarking of 3D Triangular Meshes Using Geometry Image
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
29104494
Mesh Editing in ROI with Dual Laplacian
18169252
Fully automatic and segmentation-robust classification of breast tumors based on local texture analysis of ultrasound images
Region of interest (ROI) is a region used to extract features. In breast ultrasound (BUS) image, the ROI is a breast tumor region. Because of poor image quality (low SNR (signal/noise ratio), low contrast, blurry boundaries, etc.), it is difficult to segment the BUS image accurately and produce a ROI which precisely covers the tumor region. Due to the requirement of accurate ROI for feature extraction, fully automatic classification of BUS images becomes a difficult task. In this paper, a novel fully automatic classification method for BUS images is proposed which can be divided into two steps: ''ROI generation step'' and ''ROI classification step''. The ROI generation step focuses on finding a credible ROI instead of finding the precise tumor location. The ROI classification step employs a novel feature extraction and classification strategy. First, some points in the ROI are selected as the ''classification checkpoints'' which are evenly distributed in the ROI, and the local texture features around each classification checkpoint are extracted. For each ROI, all the classification checkpoints are classified. Finally, the class of the BUS image is determined by analyzing every classification checkpoint in the corresponding ROI. Both steps were implemented by utilizing a supervised texture classification approach. The experiments demonstrate that the proposed method is very robust to the segmentation of BUS images, and very effective and useful for classifying breast tumors.
15938540
A small listener for heterogeneous mobile devices: a service enabler with a uniform Web object view
We recently developed "system on mobile devices" (SyD) middleware for rapidly developing and deploying collaborative distributed applications over a collection of autonomous Web objects and data-stores, independent of the underlying device, data, or network. SyDListener is a key component of SyD middleware. SyDListener provides a set of interfaces and classes that allows distributed SyD-based application components to communicate seamlessly in mobile environments. SyDListener provides a uniform object view of the underlying server application and enables client applications to remotely invoke those methods using XML messages. SyDListener is implemented as a multi-threaded wrapper with simple persistence management and asynchronous invocation functionality for J2ME mobile information device profile (MIDP) on connected limited device configuration (CLDL) devices. We discuss the functionality, architecture, implementation, and performance of SyDListener. We believe it is the first comprehensive working prototype of its kind for Java-enabled handhelds with a small footprint of 10 KB.
8786674
Entity discovery and assignment for opinion mining applications
Opinion mining became an important topic of study in recent years due to its wide range of applications. There are also many companies offering opinion mining services. One problem that has not been studied so far is the assignment of entities that have been talked about in each sentence. Let us use forum discussions about products as an example to make the problem concrete. In a typical discussion post, the author may give opinions on multiple products and also compare them. The issue is how to detect what products have been talked about in each sentence. If the sentence contains the product names, they need to be identified. We call this problem entity discovery. If the product names are not explicitly mentioned in the sentence but are implied due to the use of pronouns and language conventions, we need to infer the products. We call this problem entity assignment. These problems are important because without knowing what products each sentence talks about the opinion mined from the sentence is of little use. In this paper, we study these problems and propose two effective methods to solve the problems. Entity discovery is based on pattern discovery and entity assignment is based on mining of comparative sentences. Experimental results using a large number of forum posts demonstrate the effectiveness of the technique. Our system has also been successfully tested in a commercial setting.
14013953
Opinion Extraction and Summarization on the Web
The Web has become an excellent source for gathering consumer opinions. There are now numerous Web sources containing such opinions, e.g., product reviews, forums, discussion groups. and blogs. Techniques are now being developed to exploit these sources to help organizations and individuals to gain such important information easily and quickly. In this paper, we first discuss several aspects of the problem in the AI context, and then present some results of our existing work published in KDD-04 and WWW-05.
215471
Nesting One-Against-One Algorithm Based on SVMs for Pattern Classification
Support vector machines (SVMs), which were originally designed for binary classifications, are an excellent tool for machine learning. For the multiclass classifications, they are usually converted into binary ones before they can be used to classify the examples. In the one-against-one algorithm with SVMs, there exists an unclassifiable region where the data samples cannot be classified by its decision function. This paper extends the one-against-one algorithm to handle this problem. We also give the convergence and computational complexity analysis of the proposed method. Finally, one-against-one, fuzzy decision function (FDF), and decision-directed acyclic graph (DDAG) algorithms and our proposed method are compared using five University of California at Irvine (UCI) data sets. The results report that the proposed method can handle the unclassifiable region better than others.