Mesh scissoring with minima rule and part salience
This paper presents an intelligent scissoring operator for meshes. Unlike common approaches that segment a mesh using clustering schemes, here we introduce a method that concentrates on the contours for cutting. Our approach is based on the minima rule and part salience theory from the cognitive theory. The minima rule states that human perception usually divides a surface into parts along the concave discontinuity of the tangent plane. The part salience theory provides factors which determine the salience of segments. Our method first extracts features to find candidate contours based on the minima rule. Subsequently, these open contours are prioritized to select the most salient one. Then, the selected open contour is automatically completed to form a loop around a specific part of the mesh. This loop is used as the initial position of a 3D geometric snake. Before applying a snake, we measure the part salience of the segments obtained by the completed contour. If conditions for the salience are not met, the contour is rejected. Otherwise, the snake moves by relaxation until it settles to define the final scissoring position. In this paper, we focus on a fully automatic scissoring scheme; nevertheless, we also report on semi-automatic user interfaces for intelligent scissoring which are easy to use and intuitive.

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: 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 sensitive mesh segmentation with mean shift
abstract: 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: 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: Similarity search for multidimensional data sequences
abstract: Time series data, which are a series of one dimensional real numbers, have been studied in various database applications. We extend the traditional similarity search methods on time series data to support a multidimensional data sequence, such as a video stream. We investigate the problem of retrieving similar multidimensional data sequences from a large database. To prune irrelevant sequences in a database, we introduce correct and efficient similarity functions. Both data sequences and query sequences are partitioned into subsequences, and each of them is represented by a Minimum Bounding Rectangle (MBR). The query processing is based upon these MBRs, instead of scanning data elements of entire sequences. Our method is designed: (1) to select candidate sequences in a database, and (2) to find the subsequences of a selected sequence, each of which falls under the given threshold. The latter is of special importance in the case of retrieving subsequences from large and complex sequences such as video. By using it, we do not need to browse the whole of the selected video stream, but just browse the sub-streams to find a scene we want. We have performed an extensive experiment on synthetic, as well as real data sequences (a collection of TV news, dramas, and documentary videos) to evaluate our proposed method. The experiment demonstrates that 73-94 percent of irrelevant sequences are pruned using the proposed method, resulting in 16-28 times faster response time compared with that of the sequential search.

title: Noise-robust automatic speech recognition using mainlobe-resilient time-frequency quantile-based noise estimation
abstract: In standard speech recognition systems in which training data are clean speech, the presence of background noise in received signal can severely deteriorate the recognition performance. This paper presents a simple noise-robust speech recognition system based on a modified noise spectral estimation method called mainlobe-resilient time-frequency quantile-based noise estimation (M-R T-F QBNE), which focuses on the mainlobes at harmonic frequencies. We estimate the global signal-to-noise ratio (SNR) and select a recognition model, which is best matched to the SNR operating range. Experimental results show that the recognition accuracy of the proposed recognition system is higher than that of the AURORA2 clean training baseline by 23%. Compared to multicondition training, the proposed method achieves comparable recognition accuracy.

title: Requirements, Implementation and Applications of Hand-held Virtual Reality
abstract: While hand-held computing devices are capable of rendering advanced 3D graphics and processing of multimedia data, they are not designed to provide and induce sufficient sense of immersion and presence for virtual reality. In this paper, we propose minimal requirements for realizing VR on a hand-held device. Furthermore, based on the proposed requirements, we have designed and implemented a low cost hand-held VR platform by adding multimodal sensors and display components to a hand-held PC. The platform enables a motion based interface, an essential part of realizing VR on a small hand-held device, and provides outputs in three modalities, visual, aural and tactile/haptic for a reasonable sensory experience. We showcase our platform and demonstrate the possibilities of hand-hand VR through three VR applications: a typical virtual walkthrough, a 3D multimedia contents browser, and a motion based racing game. Index Terms—Virtual Reality, Computer interface human factors

title: EUDA: detecting and avoiding unidirectional links in ad hoc networks
abstract: Although there has been a great amount of research work in ad hoc networks, most of the research assumes the nodes are homogeneous. All the nodes are assumed to have the same or similar radio propagation range, processing capability, battery power, storage, and so forth. In reality however, nodes in ad hoc networks tend to have heterogeneity. In the military scenarios for instance, the troop leader is usually equipped with more powerful networking devices than the soldiers of the troop. Radios installed in the vehicles such as tanks and jeeps have more capabilities than radios the soldiers carry, as vehicles do not have the same size- or power-constraints as the soldiers.

title: Subject Specific Finite Element Modelling of the Levator Ani
abstract: Understanding of the dynamic behaviour of the levator ani is important to the assessment of pelvic floor dysfunction. Whilst shape modelling allows the depiction of 3D morphological variation of the levator ani between different patient groups, it is insufficient to determine the underlying behaviour of how the muscle deforms during contraction and strain. The purpose of this study is to perform a subject specific finite element analysis of the levator ani with open access magnetic resonance imaging. The method is based on a Mooney-Rivlin hyperelastic model and permits dynamic study of subjects under natural physiological loadings. The value of the proposed modelling framework is demonstrated with dynamic 3D data from nulliparous, female subjects.