Bas-Relief Modeling from Normal Layers.

Bas-relief is characterized by its unique presentation of intrinsic shape properties and/or detailed appearance using materials raised up in different degrees above a background. However, many bas-relief modeling methods could not manipulate scene details well. We propose a simple and effective solution for two kinds of bas-relief modeling (i.e., structure-preserving and detail-preserving) which is different from the prior tone mapping alike methods. Our idea originates from an observation on typical 3D models, which are decomposed into a piecewise smooth base layer and a detail layer in normal field. Proper manipulation of the two layers contributes to both structure-preserving and detail-preserving bas-relief modeling. We solve the modeling problem in a discrete geometry processing setup that uses normal-based mesh processing as a theoretical foundation. Specifically, using the two-step mesh smoothing mechanism as a bridge, we transfer the bas-relief modeling problem into a discrete space, and solve it in a least-squares manner. Experiments and comparisons to other methods show that (i) geometry details are better preserved in the scenario with high compression ratios, and (ii) structures are clearly preserved without shape distortion and interference from details.

Packing Vertex Data into Hardware-Decompressible Textures.

Most graphics hardware features memory to store textures and vertex data for rendering. However, because of the irreversible trend of increasing complexity of scenes, rendering a scene can easily reach the limit of memory resources. Thus, vertex data are preferably compressed, with a requirement that they can be decompressed during rendering. In this paper, we present a novel method to exploit existing hardware texture compression circuits to facilitate the decompression of vertex data in graphics processing unit (GPUs). This built-in hardware allows real-time, random-order decoding of data. However, vertex data must be packed into textures, and careless packing arrangements can easily disrupt data coherence. Hence, we propose an optimization approach for the best vertex data permutation that minimizes compression error. All of these result in fast and high-quality vertex data decompression for real-time rendering. To further improve the visual quality, we introduce vertex clustering to reduce the dynamic range of data during quantization. Our experiments demonstrate the effectiveness of our method for various vertex data of 3D models during rendering with the advantages of a minimized memory footprint and high frame rate.

Improving the discrimination of hand motor imagery via virtual reality based visual guidance.

While research on the brain-computer interface (BCI) has been active in recent years, how to get high-quality electrical brain signals to accurately recognize human intentions for reliable communication and interaction is still a challenging task. The evidence has shown that visually guided motor imagery (MI) can modulate sensorimotor electroencephalographic (EEG) rhythms in humans, but how to design and implement efficient visual guidance during MI in order to produce better event-related desynchronization (ERD) patterns is still unclear. The aim of this paper is to investigate the effect of using object-oriented movements in a virtual environment as visual guidance on the modulation of sensorimotor EEG rhythms generated by hand MI. To improve the classification accuracy on MI, we further propose an algorithm to automatically extract subject-specific optimal frequency and time bands for the discrimination of ERD patterns produced by left and right hand MI. The experimental results show that the average classification accuracy of object-directed scenarios is much better than that of non-object-directed scenarios (76.87% vs. 69.66%). The result of the t-test measuring the difference between them is statistically significant (p = 0.0207). When compared to algorithms based on fixed frequency and time bands, contralateral dominant ERD patterns can be enhanced by using the subject-specific optimal frequency and the time bands obtained by our proposed algorithm. These findings have the potential to improve the efficacy and robustness of MI-based BCI applications.

Discrimination of motor imagery tasks via information flow pattern of brain connectivity.

BACKGROUND: The effective connectivity refers explicitly to the influence that one neural system exerts over another in frequency domain. To investigate the propagation of neuronal activity in certain frequency can help us reveal the mechanisms of information processing by brain. OBJECTIVE: This study investigates the detection of effective connectivity and analyzes the complex brain network connection mode associated with motor imagery (MI) tasks. METHODS: The effective connectivity among the primary motor area is firstly explored using partial directed coherence (PDC) combined with multivariate empirical mode decomposition (MEMD) based on electroencephalography (EEG) data. Then a new approach is proposed to analyze the connection mode of the complex brain network via the information flow pattern. RESULTS: Our results demonstrate that significant effective connectivity exists in the bilateral hemisphere during the tasks, regardless of the left-/right-hand MI tasks. Furthermore, the out-in rate results of the information flow reveal the existence of the contralateral lateralization. The classification performance of left-/right-hand MI tasks can be improved by careful selection of intrinsic mode functions (IMFs). CONCLUSION: The proposed method can provide efficient features for the detection of MI tasks and has great potential to be applied in brain computer interface (BCI).

A Two-Phase Space Resection Model for Accurate Topographic Reconstruction from Lunar Imagery with PushbroomScanners.

Exterior orientation parameters' (EOP) estimation using space resection plays an important role in topographic reconstruction for push broom scanners. However, existing models of space resection are highly sensitive to errors in data. Unfortunately, for lunar imagery, the altitude data at the ground control points (GCPs) for space resection are error-prone. Thus, existing models fail to produce reliable EOPs. Motivated by a finding that for push broom scanners, angular rotations of EOPs can be estimated independent of the altitude data and only involving the geographic data at the GCPs, which are already provided, hence, we divide the modeling of space resection into two phases. Firstly, we estimate the angular rotations based on the reliable geographic data using our proposed mathematical model. Then, with the accurate angular rotations, the collinear equations for space resection are simplified into a linear problem, and the global optimal solution for the spatial position of EOPs can always be achieved. Moreover, a certainty term is integrated to penalize the unreliable altitude data for increasing the error tolerance. Experimental results evidence that our model can obtain more accurate EOPs and topographic maps not only for the simulated data, but also for the real data from Chang'E-1, compared to the existing space resection model.

Enhancing training performance for brain-computer interface with object-directed 3D visual guidance.

PURPOSE: The accuracy of the classification of user intentions is essential for motor imagery (MI)-based brain-computer interface (BCI). Effective and appropriate training for users could help us produce the high reliability of mind decision making related with MI tasks. In this study, we aimed to investigate the effects of visual guidance on the classification performance of MI-based BCI. METHODS: In this study, leveraging both the single-subject and the multi-subject BCI paradigms, we train and classify MI tasks with three different scenarios in a 3D virtual environment, including non-object-directed scenario, static-object-directed scenario, and dynamic object-directed scenario. Subjects are required to imagine left-hand or right-hand movement with the visual guidance. RESULTS: We demonstrate that the classification performances of left-hand and right-hand MI task have differences on these three scenarios, and confirm that both static-object-directed and dynamic object-directed scenarios could provide better classification accuracy than the non-object-directed case. We further indicate that both static-object-directed and dynamic object-directed scenarios could shorten the response time as well as be suitable applied in the case of small training data. In addition, experiment results demonstrate that the multi-subject BCI paradigm could improve the classification performance comparing with the single-subject paradigm. These results suggest that it is possible to improve the classification performance with the appropriate visual guidance and better BCI paradigm. CONCLUSION: We believe that our findings would have the potential for improving classification performance of MI-based BCI and being applied in the practical applications.

Bi-Normal Filtering for Mesh Denoising.

Most mesh denoising techniques utilize only either the facet normal field or the vertex normal field of a mesh surface. The two normal fields, though contain some redundant geometry information of the same model, can provide additional information that the other field lacks. Thus, considering only one normal field is likely to overlook some geometric features. In this paper, we take advantage of the piecewise consistent property of the two normal fields and propose an effective framework in which they are filtered and integrated using a novel method to guide the denoising process. Our key observation is that, decomposing the inconsistent field at challenging regions into multiple piecewise consistent fields makes the two fields complementary to each other and produces better results. Our approach consists of three steps: vertex classification, bi-normal filtering, and vertex position update. The classification step allows us to filter the two fields on a piecewise smooth surface rather than a surface that is smooth everywhere. Based on the piecewise consistence of the two normal fields, we filtered them using a piecewise smooth region clustering strategy. To benefit from the bi-normal filtering, we design a quadratic optimization algorithm for vertex position update. Experimental results on synthetic and real data show that our algorithm achieves higher quality results than current approaches on surfaces with multifarious geometric features and irregular surface sampling.

Turbulence simulation by adaptive multi-relaxation lattice boltzmann modeling.

This paper presents a novel approach to simulating turbulent flows by developing an adaptive multirelaxation scheme in the framework of lattice Boltzmann equation (LBE). Existing LBE methods in graphics simulations are usually insufficient for turbulent flows since the collision term disturbs the underlying stability and accuracy. We adopt LBE with the multiple relaxation time (MRT) collision model (MRT-LBE), and address this issue by enhancing the collision-term modeling. First, we employ renormalization group analysis and formulate a new turbulence model with an adaptive correction method to compute more appropriate eddy viscosities on a uniform lattice structure. Efficient algebraic calculations are retained with small-scale turbulence details while maintaining the system stability. Second, we note that for MRT-LBE, predicting single eddy viscosity per lattice node may still result in instability. Hence, we simultaneously predict multiple eddy viscosities for stress-tensor-related elements, thereby asynchronously computing multiple relaxation parameters to further enhance the MRT-LBE stability. With these two new strategies, turbulent flows can be simulated with finer visual details even on coarse grid configurations. We demonstrate our results by simulating and visualizing various turbulent flows, particularly with smoke animations, where stable turbulent flows with high Reynolds numbers can be faithfully produced.

Virtual suturing simulation based on commodity physics engine for medical learning.

Development of virtual-reality medical applications is usually a complicated and labour intensive task. This paper explores the feasibility of using commodity physics engine to develop a suturing simulator prototype for manual skills training in the fields of nursing and medicine, so as to enjoy the benefits of rapid development and hardware-accelerated computation. In the prototype, spring-connected boxes of finite dimension are used to simulate soft tissues, whereas needle and thread are modelled with chained segments. Spherical joints are used to simulate suture's flexibility and to facilitate thread cutting. An algorithm is developed to simulate needle insertion and thread advancement through the tissue. Two-handed manipulations and force feedback are enabled with two haptic devices. Experiments on the closure of a wound show that the prototype is able to simulate suturing procedures at interactive rates. The simulator is also used to study a curvature-adaptive suture modelling technique. Issues and limitations of the proposed approach and future development are discussed.

Fast rendering of diffusion curves with triangles.

Diffusion curves are a new kind of primitive in vector graphics, capable of representing smooth color transitions among boundaries. Their rendering requires solving Poisson's equation; much previous research relied on traditional solvers, which commonly require GPU acceleration to achieve real-time rasterization. This obviously restricts deployment on the Internet—for example, as rich Internet applications, in which various computing environments are involved. Diffusion effects are similar to locally defined interpolation with a particular orientation and magnitude. Inspired by that observation, a mesh-based framework combined with mean value coordinates (MVC) interpolants efficiently renders diffusion curve images on a CPU. This method employs a visibility algorithm to efficiently find and sort neighboring curve nodes for each vertex. It then assigns the vertex colors according to MVC interpolation with the neighboring curve nodes. Experiments produced rendering results comparable to traditional solvers, but this method is computationally more efficient and runs much faster on a CPU.

Accelerating simultaneous algebraic reconstruction technique with motion compensation using CUDA-enabled GPU.

PURPOSE: To accelerate the simultaneous algebraic reconstruction technique (SART) with motion compensation for speedy and quality computed tomography reconstruction by exploiting CUDA-enabled GPU. METHODS: Two core techniques are proposed to fit SART into the CUDA architecture: (1) a ray-driven projection along with hardware trilinear interpolation, and (2) a voxel-driven back-projection that can avoid redundant computation by combining CUDA shared memory. We utilize the independence of each ray and voxel on both techniques to design CUDA kernel to represent a ray in the projection and a voxel in the back-projection respectively. Thus, significant parallelization and performance boost can be achieved. For motion compensation, we rectify each ray's direction during the projection and back-projection stages based on a known motion vector field. RESULTS: Extensive experiments demonstrate the proposed techniques can provide faster reconstruction without compromising image quality. The process rate is nearly 100 projections s (-1), and it is about 150 times faster than a CPU-based SART. The reconstructed image is compared against ground truth visually and quantitatively by peak signal-to-noise ratio (PSNR) and line profiles. We further evaluate the reconstruction quality using quantitative metrics such as signal-to-noise ratio (SNR) and mean-square-error (MSE). All these reveal that satisfactory results are achieved. The effects of major parameters such as ray sampling interval and relaxation parameter are also investigated by a series of experiments. A simulated dataset is used for testing the effectiveness of our motion compensation technique. The results demonstrate our reconstructed volume can eliminate undesirable artifacts like blurring. CONCLUSION: Our proposed method has potential to realize instantaneous presentation of 3D CT volume to physicians once the projection data are acquired.

A simulation framework for estimating wall stress distribution of abdominal aortic aneurysm.

Abdominal aortic aneurysm (AAA) rupture is believed to occur when the mechanical stress acting on the wall exceeds the strength of the wall tissue. In endovascular aneurysm repair, a stent-graft in a catheter is released at the aneurysm site to form a new blood vessel and protect the weakened AAA wall from the pulsatile pressure and, hence, possible rupture. In this paper, we propose a framework to estimate the wall stress distribution of non-stented/stented AAA based on fluid-structure interaction, which is utilized in a surgical simulation system (IRAS). The 3D geometric model of AAA is reconstructed from computed tomography angiographic (CTA) images. Based on our experiments, a combined logarithm and polynomial strain energy equation is applied to model the elastic properties of arterial wall. The blood flow is modeled as laminar, incompressible, and non-Newtonian flow by applying Navier-Stokes equation. The obtained pressure of blood flow is applied as load on the AAA meshes with and without stent-graft and the wall stress distribution is calculated by fluid-structure interaction (FSI) solver equipped in ANSYS. Experiments demonstrate that our analytical results are consistent with clinical observations.

Learning blood management in orthopedic surgery through gameplay.

Orthopedic surgery treats the musculoskeletal system, in which bleeding is common and can be fatal. To help train future surgeons in this complex practice, researchers designed and implemented a serious game for learning orthopedic surgery. The game focuses on teaching trainees blood management skills, which are critical for safe operations. Using state-of-the-art graphics technologies, the game provides an interactive and realistic virtual environment. It also integrates game elements, including task-oriented and time-attack scenarios, bonuses, game levels, and performance evaluation tools. To study the system's effect, the researchers conducted experiments on player completion time and off-target contacts to test their learning of psychomotor skills in blood management.

A novel modeling framework for multilayered soft tissue deformation in virtual orthopedic surgery.

Realistic modeling of soft tissue deformation is crucial to virtual orthopedic surgery, especially orthopedic trauma surgery which involves layered heterogeneous soft tissues. In this paper, a novel modeling framework for multilayered soft tissue deformation is proposed in order to facilitate the development of orthopedic surgery simulators. We construct our deformable model according to the layered structure of real human organs, and this results in a multilayered model. The division of layers is based on the segmented Chinese Visible Human (CVH) dataset. This enhances the realism and accuracy in the simulation. For the sake of efficiency, we employ 3D mass-spring system to our multilayered model. The nonlinear passive biomechanical properties of skin and skeletal muscle are achieved by introducing a bilinear elasticity scheme to the springs in the mass-spring system. To efficiently and accurately reproduce the biomechanical properties of certain human tissues, an optimization approach is employed in configuring the parameters of the springs. Experimental data from biomechanics literatures are used as benchmarking references. With the employment of Physics Processing Unit (PPU) and high quality volume visualization, our framework is developed into an interactive and intuitive platform for virtual surgery training systems. Several experiments demonstrate the feasibility of the proposed framework in providing interactive and realistic deformation for orthopedic surgery simulation.

Orthopedics surgery trainer with PPU-accelerated blood and tissue simulation.

This paper presents a novel orthopedics surgery training system with both the components for modeling as well as simulating the deformation and visualization in an efficient way. By employing techniques such as optimization, segmentation and center line extraction, the modeling of deformable model can be completed with minimal manual involvement. The novel trainer can simulate rigid body, soft tissue and blood with state-of-the-art techniques, so that convincing deformation and realistic bleeding can be achieved. More important, newly released Physics Processing Unit (PPU) is adopted to tackle the high requirement for physics related computations. Experiment shows that the acceleration gain from PPU is significant for maintaining interactive frame rate under a complex surgical environments of orthopedics surgery.

Tileable BTF.

This paper presents a modular framework to efficiently apply the bidirectional texture functions (BTF) onto object surfaces. The basic building blocks are the BTF tiles. By constructing one set of BTF tiles, a wide variety of objects can be textured seamlessly without re-synthesizing the BTF. The proposed framework nicely decouples the surface appearance from the geometry. With this appearance-geometry decoupling, one can build a library of BTF tile sets to instantaneously dress and render various objects under variable lighting and viewing conditions. The core of our framework is a novel method for synthesizing seamless high-dimensional BTF tiles, that are difficult for existing synthesis techniques. Its key is to shorten the cutting paths and broaden the choices of samples so as to increase the chance of synthesizing seamless BTF tiles. To tackle the enormous data, the tile synthesis process is performed in compressed domain. This not just allows the handling of large BTF data during the synthesis, but also facilitates compact storage of the BTF in GPU memory during the rendering.