Feature sensitive mesh segmentation with mean shift
Feature sensitive mesh segmentation is important for many computer graphics and geometric modeling applications. In this paper, we develop a mesh segmentation method, which is capable of producing high-quality shape partitioning. It respects fine shape features and works well on various types of shapes, including natural shapes and mechanical parts. The method combines a procedure for clustering mesh normals with a modification of the mesh clarification technique. For clustering of mesh normals, we adopt Mean Shift, a powerful general purpose technique for clustering scattered data. We demonstrate advantages of our method by comparing it with two state-of-the-art mesh segmentation techniques.

title: Neural mesh ensembles
abstract: This work proposes the use of neural network ensembles to boost the performance of a neural network based surface reconstruction algorithm. Ensemble is a very popular and powerful statistical technique based on the idea of averaging several outputs of a probabilistic algorithm. In the context of surface reconstruction, two main problems arise. The first is finding an efficient way to average meshes with different connectivity, and the second is tuning the parameters for surface reconstruction to maximize the performance of the ensemble. We solve the first problem by voxelizing all the meshes on the same regular grid and taking majority vote on each voxel. We tune the parameters experimentally, borrowing ideas from weak learning methods.

title: Feature-based surface light field morphing
abstract: A surface light field is a function that gives the colors of each object point viewed from different directions. Object representation with a surface light field provides a nice structure for 3D photography. This paper presents a feature-based morphing technique for two objects equipped with surface light fields. The technique consists of geometry morphing and in-between light field mapping. Geometry morphing is accomplished by 3D mesh morphing, where we introduce a vertex merging technique to generate a simpler metamesh. In in-between light field mapping, an in-between object is rendered by extracting necessary fragments from input surface light fields. We also propose an acceleration technique for rendering an in-between object. Experimental results with real and synthetic data show natural and plausible morphing between objects with surface light fields. The proposed morphing technique can be used as an editing tool for 3D photography.

title: Scattered Data Interpolation with Multilevel B-Splines
abstract: The paper describes a fast algorithm for scattered data interpolation and approximation. Multilevel B-splines are introduced to compute a C/sup 2/ continuous surface through a set of irregularly spaced points. The algorithm makes use of a coarse to fine hierarchy of control lattices to generate a sequence of bicubic B-spline functions whose sum approaches the desired interpolation function. Large performance gains are realized by using B-spline refinement to reduce the sum of these functions into one equivalent B-spline function. Experimental results demonstrate that high fidelity reconstruction is possible from a selected set of sparse and irregular samples.

title: Local injectivity conditions of 2D and 3D uniform cubic B-spline functions
abstract: Uniform cubic B-spline functions have been used for mapping functions in various areas such as image warping and morphing, 3D deformation, and volume morphing. The injectivity (one-to-one property) of a mapping function is important to obtain good results in these areas. We consider the local injectivity conditions of 2D and 3D uniform cubic B-spline functions. We propose a geometric interpretation of the local injectivity of a uniform cubic B-spline function, with which 2D and 3D cases can be handled in a similar way. Based on the geometric interpretation, we present sufficient conditions for the local injectivity that are represented in terms of control point displacements. These sufficient conditions are simple and easy to check and will be useful to guarantee the injectivity of mapping functions in application areas.

title: Optical Flow Rendering
abstract: This paper proposes a new approach to image-based rendering that generates an image viewed from an arbitrary camera position and orientation by rendering optical flows extracted from reference images. To derive valid optical flows, we develop an analysis technique that improves the quality of stereo matching. Without using any special equipments such as range cameras, this technique constructs reliable optical flows from a sequence of matching results between reference images. We also derive validity conditions of optical flows and show that the obtained flows satisfy those conditions. Since environment geometry is inferred from the optical flows, we are able to generate more accurate images with this additional geometric information. Our approach makes it possible to combine an image rendered from optical flows with an image generated by a conventional rendering technique through a simple Z-buffer algorithm.

title: LB_Keogh supports exact indexing of shapes under rotation invariance with arbitrary representations and distance measures
abstract: The matching of two-dimensional shapes is an important problem with applications in domains as diverse as biometrics, industry, medicine and anthropology. The distance measure used must be invariant to many distortions, including scale, offset, noise, partial occlusion, etc. Most of these distortions are relatively easy to handle, either in the representation of the data or in the similarity measure used. However rotation invariance seems to be uniquely difficult. Current approaches typically try to achieve rotation invariance in the representation of the data, at the expense of discrimination ability, or in the distance measure, at the expense of efficiency. In this work we show that we can take the slow but accurate approaches and dramatically speed them up. On real world problems our technique can take current approaches and make them four orders of magnitude faster, without false dismissals. Moreover, our technique can be used with any of the dozens of existing shape representations and with all the most popular distance measures including Euclidean distance, Dynamic Time Warping and Longest Common Subsequence.

title: Scale-Space Derived From B-Splines
abstract: This paper proposes a scale-space theory based on B-spline kernels. Our aim is twofold: 1) present a general framework, and show how B-splines provide a flexible tool to design various scale-space representations. In particular, we focus on the design of continuous scale-space and dyadic scale-space frame representations. A general algorithm is presented for fast implementation of continuous scale-space at rational scales. In the dyadic case, efficient frame algorithms are derived using B-spline techniques to analyze the geometry of an image. The relationship between several scale-space approaches is explored. The behavior of edge models, the properties of completeness, causality, and other properties in such a scale-space representation are examined in the framework of B-splines. It is shown that, besides the good properties inherited from the Gaussian kernel, the B-spline derived scale-space exhibits many advantages for modeling visual mechanism including the efficiency, compactness, orientation feature and parallel structure.

title: ICA-based Clustering of Genes from Microarray Expression Data
abstract: We propose an unsupervised methodology using independent component analysis (ICA) to cluster genes from DNA microarray data. Based on an ICA mixture model of genomic expression patterns, linear and nonlinear ICA finds components that are specific to certain biological processes. Genes that exhibit significant up-regulation or down-regulation within each component are grouped into clusters. We test the statistical significance of enrichment of gene annotations within each cluster. ICA-based clustering outperformed other leading methods in constructing functionally coherent clusters on various datasets. This result supports our model of genomic expression data as composite effect of independent biological processes. Comparison of clustering performance among various ICA algorithms including a kernel-based nonlinear ICA algorithm shows that nonlinear ICA performed the best for small datasets and natural-gradient maximization-likelihood worked well for all the datasets.

title: An arbitration look-ahead scheme for reducing end-to-end latency in networks on chip
abstract: With the increasing complexity of system-on-chips, networks on chip (NoC) using multi-hop switching require low end-to-end latency for QoS guarantees. An arbitration look-ahead scheme is proposed to reduce the end-to-end packet latency in the NoC. Its packet arbitration at each switch is completed a few cycles in advance of the packet arrival. As a result, a packet bypasses the switch without the latency of input queuing and arbitration. This scheme is analyzed on a 4/spl times/4 mesh topology. A maximum 65% and average 26% latency reduction are obtained under random traffic. Latency reduction up to 36% is achieved under multimedia traffic.

title: ARMS: An Authenticated Routing Message in Sensor Networks
abstract: In wireless sensor networks, a sensor node broadcasts its data (such as routing information, beacon messages or meta-data) to all its neighbors, which is called local broadcast. A general case for a sensor node to use a local broadcast is to advertise its routing information. Considering that sensor networks are vulnerable to a variety of attacks and current routing protocols are insecure, a sensor node's broadcast message should be authenticated by all its neighbors. Unfortunately, the previous work on broadcast authentication in sensor networks mainly concentrates on broadcast messages from a base station which has greater capabilities, not from a sensor node. Those schemes' properties are not appropriate for broadcast authentication of sensor node's routing messages. In this paper, we present ARMS, a protocol for broadcast authentication of sensor node's routing messages. It requires only a small memory space, authenticates routing messages without delay (thus, no buffering is needed), needs no time synchronization among sensor nodes, and mitigates the effect of packet loss. These ARMS' properties are suitable for a sensor node to broadcast an authenticated routing message.