Neural mesh ensembles
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: Mesh parameterization with a virtual boundary
abstract: Parameterization of a 3D triangular mesh is a fundamental problem in various applications of meshes. The convex combination approach is widely used for parameterization because of its good properties, such as fast computation and one-to-one embedding. However, the approach has a drawback: most boundary triangles have high distortion in the embedding compared with interior ones. In this paper, we present an extension of the convex combination approach that resolves the drawback by using a virtual boundary. Since the virtual boundary is fixed onto a given convex polygon instead of the real boundary, the real boundary triangles can better reflect the shape of the corresponding 3D triangles. The proposedapproach obtains a parameterization of a 3D mesh with less d istortion than with the original convex combination approach.

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: Motion retargeting and evaluation for VR-based training of free motions
abstract: Virtual reality (VR) has emerged as one of the important and effective tools for education and training. Most VR-based training systems are situation based, where the trainees are trained for discrete decision making in special situations presented by the VR environments. In contrast, this paper discusses the application of VR to a different class of training, for learning free motion, often required in sports and the arts. We propose a VR-based motion-training framework that contains an intuitive motion-guiding interface, posture-oriented motion retargeting, and an evaluation and advice scheme for corrective feedback. Applications of the proposed framework to simple fencing training and a dance imitation game are demonstrated .

title: Out-of-Core Remeshing of Large Polygonal Meshes
abstract: We propose an out-of-core method for creating semi-regular surface representations from large input surface meshes. Our approach is based on a streaming implementation of the MAPS remesher of Lee et al. Our remeshing procedure consists of two stages. First, a simplification process is used to obtain the base domain. During simplification, we maintain the mapping information between the input and the simplified meshes. The second stage of remeshing uses the mapping information to produce samples of the output semi-regular mesh. The out-of-core operation of our method is enabled by the synchronous streaming of a simplified mesh and the mapping information stored at the original vertices. The synchronicity of two streaming buffers is maintained using a specially designed write strategy for each buffer. Experimental results demonstrate the remeshing performance of the proposed method, as well as other applications that use the created mapping between the simplified and the original surface representations

title: Motif analysis of noisy regular textures
abstract: A regular texture contains a periodic arrangement of a determinable pattern. Motifs of a regular texture are the minimal parallelogrammic sub-regions that can reproduce the texture by tiling. A motif extracted from a regular texture can be used for texture reproduction, texture recognition, and texture compression. However, it is not simple to accurately capture a motif of a regular texture, as the motif may be non-rectangular or arranged in an arbitrary direction. Moreover, when the texture contains additive noises, such as are created by the scanning process, motif analysis becomes more difficult. In this paper, we present an algorithm to obtain a motif of a noisy regular texture in O( mn log mn ) time, where m × n is the size of the texture. The analysis examples demonstrate that the proposed algorithm can robustly derive motifs of noisy regular textures.

title: Contention free embedding of complete binary trees into 3D meshes in half and full-duplexed network models
abstract: We consider the problem of dimension-ordered embedding of complete binary trees into 3-dimensional meshes without link contention. In this paper, the embedding problems are studied under two different network models: half-duplexed networks and full-duplexed networks. Gibbons and Paterson showed that a complete binary tree T/sub p/ could be disjointedly embedded into a full-duplexed 2-dimensional mesh of optimum size without dimension-ordered routing. Using the dimension-ordered routing, the authors showed that T/sub p/ could be embedded into a 3-dimensional mesh of optimum size with link congestion two. This paper presents the dimension-ordered embedding algorithms of the complete binary trees into the wormhole routed 3-dimensional meshes without link contention achieving expansion of no larger than 1.125 of optimum in half-duplexed network model and optimum in full-duplexed network model.

title: Video Data Indexing by 2D C-Trees
abstract: Video data contains a large amount of spatial and temporal information. The changes of video frames are quite useful for motion analysis and cannot be provided by other media easily. Chang et al. had proposed an effective 2D string approach for spatial indexing of image data. In this paper, we extend this iconic approach and apply it to video data indexing. We propose 2D C-trees to represent the spatial content within individual frames. A video sequence can then be represented and indexed by a temporal set or an ordering set of 2D C-trees. The similarity retrieval of video matching problem becomes the problem of video sequence matching by computing the similarity, or the minimum cost of matched frames. We present three schemes, full-sequence matching, segment matching and subsequence matching, for video information retrieval. The matching schemes can be modified and extended to approximate sequence matching by computing the partial distance for providing a comprehensive retrieval of video data. A prototype video information system is also developed to validate the effectiveness of video data indexing by 2D C-trees.

title: Congestion controlled adaptive lightweight multicast in wireless mobile ad hoc networks
abstract: The use of contention-based MAC protocols combined with hidden terminal problems make multi-hop wireless ad hoc networks much more sensitive to load and congestion than wired networks or even wireless cellular networks. In such an environment, we argue that multicast reliability cannot be achieved solely by retransmission of lost packets as is typically done in wired networks with protocols such as SRM. We contend that in order to achieve reliable multicast delivery in such networks, besides reliability mechanisms, we must also consider jointly two components: reliability and congestion control. In this paper, we propose CALM, a congestion controlled, adaptive, lightweight multicast transport protocol and show that congestion control alone can significantly improve reliable packet delivery in ad hoc networks when compared to traditional "wired" reliable multicast protocols.

title: Allocation of Signature File on Parallel Device for WWW Index Servers
abstract: Signature files are widely used in information retrieval and database. They act as search filters for content-based retrieval. In a large database server, a parallel device is utilized to achieve concurrency access. Efficient allocation of signature files on parallel devices minimizes the query response time and is important in the design of access methods for large scale index servers. We have developed an algorithm to organize the storage of signatures in parallel secondary storage to reduce the query response time. First, signature file is clustered into signature pages. Then, the clustered signature pages are distributed among the disks using the parity check matrix of error correcting code in coding theory. Through the construction of error correcting code, the least frequently simultaneously accessed pages are allocated on the same disk. Performance analysis shows that this algorithm improves the efficiency of access.

title: Performance-guided neural network for rapidly self-organising active network management
abstract: We present a neural network for real-time learning and mapping of patterns using an external performance indicator. In a non-stationary environment where new patterns are introduced over time, the learning process utilises a novel snap-drift algorithm that performs fast, convergent, minimalist learning (snap) when the overall network performance is poor and slower, more cautious learning (drift) when the performance is good. Snap is based on a modified form of Adaptive Resonance Theory (CGIP 37(1987)54); and drift is based on Learning Vector Quantization (LVQ) (. The two are combined within a semi-supervised learning system that shifts its learning style whenever it receives a significant change in performance feedback. The learning is capable of rapid re-learning and re-stabilisation, according to changes in external feedback or input patterns. We have incorporated this algorithm into the design of a modular neural network system, Performance-guided Adaptive Resonance Theory (P-ART) ( Soft computing systems: Design, Management and application, IOS Press, Netherland, 2002; . Simulation results show that the system discovers alternative solutions in response to significant changes in the input patterns and/or in the environment, which may require similar patterns to be treated differently over time. The simulations involve attempting to optimise the selection of network services in a non-stationary, real-time active computer network environment, in which the factors influencing the required selections are subject to change.