Texture-based Flow Visualization Research Articles

Optimal Viewpoint Selection for Texture-based Flow Visualization

Optimal Viewpoint Selection for Texture-based Flow Visualization

Texture-Based Multiresolution Flow Visualization

Texture-Based Multiresolution Flow Visualization

Image-Space Texture-Based Output-Coherent Surface Flow Visualization

Image-space line integral convolution (LIC) is a popular scheme for visualizing surface vector fields due to its simplicity and high efficiency. To avoid inconsistencies or color blur during the user interactions, existing approaches employ surface parameterization or 3D volume texture schemes. However, they often require expensive computation or memory cost, and cannot achieve consistent results in terms of both the granularity and color distribution on different scales. This paper introduces a novel image-space surface flow visualization approach that preserves the coherence during user interactions. To make the noise texture under different viewpoints coherent, we propose to precompute a sequence of mipmap noise textures in a coarse-to-fine manner for consistent transition, and map the textures onto each triangle with randomly assigned and constant texture coordinates. Further, a standard image-space LIC is performed to generate the flow texture. The proposed approach is simple and GPU-friendly, and can be easily combined with various texture-based flow visualization techniques. By leveraging viewpoint-dependent backward tracing and mipmap noise phase, our method can be incorporated with the image-based flow visualization (IBFV) technique for coherent visualization of unsteady flows. We demonstrate consistent and highly efficient flow visualization on a variety of data sets.

An optimized texture-by-numbers synthesis method and its visual applications

The framework of texture-by-numbers (TBN) synthesizes images of global-varying patterns with intuitive user control. Previous TBN synthesis methods have difficulties in achieving high-quality synthesis results and efficiency simultaneously. This paper proposes a fast TBN synthesis method based on texture optimization, which uses global optimization to solve the controllable non-homogeneous texture synthesis problem. Our algorithm produces high quality synthesis results by combining texture optimization into TBN framework with two improvements. The initialization process is adopted to generate the initial output of the global optimization algorithm, which speeds up the algorithm’s convergence rate and enhances synthesis quality. Besides distance metrics to measure image similarities are specifically designed for different images to better match human visual perception for structural patterns and a user study is conducted to verify the effectiveness of the metrics. To further improve the synthesis speed, the algorithm is entirely implemented on GPU based on CUDA architecture. The optimized TBN method is applied to various visual applications including not only traditional TBN applications, but also image in-painting and texture-based flow visualization. The experimental results show that our method synthesizes images of higher or comparable qualities with higher efficiency than other state-of-art synthesis methods.

Applications of Texture-Based Flow Visualization

Flow visualization is a classic sub-field of scientific visualization. The task of flow visualization algorithms is to depict vector data, i.e., data with magnitude and direction. An important category of flow visualization techniques makes use of textures in order to convey the properties of a vector field. Recently, several research advances have been made in this special category, of dense, texture-based techniques. We present the application of the most recent texture-based techniques to real world data from oceanography and meteorology, computational fluid dynamics (CFD) in the automotive industry, and medicine. We describe the motivations for using texture-based algorithms as well as the important recent advancements required for their successful incorporation into industry grade software. Our goal is to appeal to practitioners in the field interested in learning how these recent techniques can help them gain insight into problems that engineers and other professionals may be faced with on a daily basis. Many of these applications have only recently become possible.

Texture optimization for example-based synthesis

We present a novel technique for texture synthesis using optimization. We define a Markov Random Field (MRF)-based similarity metric for measuring the quality of synthesized texture with respect to a given input sample. This allows us to formulate the synthesis problem as minimization of an energy function, which is optimized using an Expectation Maximization (EM)-like algorithm. In contrast to most example-based techniques that do region-growing, ours is a joint optimization approach that progressively refines the entire texture. Additionally, our approach is ideally suited to allow for controllable synthesis of textures. Specifically, we demonstrate controllability by animating image textures using flow fields. We allow for general two-dimensional flow fields that may dynamically change over time. Applications of this technique include dynamic texturing of fluid animations and texture-based flow visualization.