Wai-Man Pang

Structure-aware halftoning

This paper presents an optimization-based halftoning technique that preserves the structure and tone similarities between the original and the halftone images. By optimizing an objective function consisting of both the structure and the tone metrics, the generated halftone images preserve visually sensitive texture details as well as the local tone. It possesses the blue-noise property and does not introduce annoying patterns. Unlike the existing edge-enhancement halftoning, the proposed method does not suffer from the deficiencies of edge detector. Our method is tested on various types of images. In multiple experiments and the user study, our method consistently obtains the best scores among all tested methods.

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 systems effect, the researchers conducted experiments on player completion time and off-target contacts to test their learning of psychomotor skills in blood management.

Richness-preserving manga screening

Due to the tediousness and labor intensive cost, some manga artists have already employed computer-assisted methods for converting color photographs to manga backgrounds. However, existing bitonal image generation methods usually produce unsatisfactory uniform screening results that are not consistent with traditional mangas, in which the artist employs a rich set of screens. In this paper, we propose a novel method for generating bitonal manga backgrounds from color photographs. Our goal is to preserve the visual richness in the original photograph by utilizing not only screen density, but also the variety of screen patterns. To achieve the goal, we select screens for different regions in order to preserve the tone similarity, texture similarity, and chromaticity distinguishability. The multi-dimensional scaling technique is employed in such a color-to-pattern matching for maintaining pattern dissimilarity of the screens. Users can control the mapping by a few parameters and interactively fine-tune the result. Several results are presented to demonstrate the effectiveness and convenience of the proposed method.

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.

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

PurposeTo accelerate the simultaneous algebraic reconstruction technique (SART) with motion compensation for speedy and quality computed tomography reconstruction by exploiting CUDA-enabled GPU.MethodsTwo 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.ResultsExtensive 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.ConclusionOur proposed method has potential to realize instantaneous presentation of 3D CT volume to physicians once the projection data are acquired.

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 resynthesizing 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, which 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 a compressed domain. This not only allows the handling of large BTF data during the synthesis, but also facilitates the compact storage of the BTF in a GPU memory during the rendering.

Particle-based simulation of blood flow and vessel wall interactions in virtual surgery

We propose a particle-based solution to simulate the interactions between blood flow and vessel wall for virtual surgery. By coupling two particle-based techniques, the smoothed particle hydrodynamics (SPH) and mass-spring model (MSM), we can simulate the blood flow and deformation of vessel seamlessly. At the vessel wall, particles are considered as both boundary particles for SPH solver and mass points for the MSM solver. We implement an improved repulsive boundary condition to simulate the interactions. The computation of blood flow dynamics and vessel wall deformations are performed in an alternating fashion in every time step. To ensure realism, parameters of both SPH and MSM are carefully configured. Experimental results demonstrate the potential of the proposed method in providing real-time and realistic interactions for virtual vascular surgery systems.

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.

Generating massive high-quality random numbers using GPU

Pseudo-random number generators (PRNG) have been intensively used in many stochastic algorithms in artificial intelligence, computer graphics and other scientific computing. However, the current commodity GPU design does not facilitate the efficient implementation of high-quality PRNGs that require high-precision integer arithmetics and bitwise operations. In this paper, we propose a framework to generate a high-quality PRNG shader for all kinds of GPUs. We adopt the cellular automata (CA) PRNG to facilitate high speed and parallel random number generation. The configuration of the CA PRNG is completed automatically by optimizing an objective function that accounts for quality of generated random sequences. To visually evaluate the result, we apply the best PRNG shader to photon mapping. Timing statistics show that our GPU parallelized PRNG is much faster than a pure CPU implementation.

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 Poissons 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.