Hypnotic line art is a modern form in which white narrow curved ribbons, with the width and direction varying along each path over a black background, provide a keen sense of 3D objects regarding surface shapes and topological contours. However, the procedure of manually creating such line art work can be quite tedious and time-consuming. In this article, we present an interactive system that offers a What-You-See-Is-What-You-Get (WYSIWYG) scheme for producing hypnotic line art images by integrating and placing evenly-spaced streamlines in tensor fields. With an input picture segmented, the user just needs to sketch a few illustrative strokes to guide the construction of a tensor field for each part of the objects therein. Specifically, we propose a new method which controls, with great precision, the aesthetic layout and artistic drawing of an array of streamlines in each tensor field to emulate the style of hypnotic line art. Given several parameters for streamlines such as density, thickness, and sharpness, our system is capable of generating professional-level hypnotic line art work. With great ease of use, it allows art designers to explore a wide variety of possibilities to obtain hypnotic line art results of their own preferences.

This paper presents an accurate parallel implementation of unsteady flow line integral convolution (UFLIC) for high-performance visualization of large time-varying flows. Our approach differs from previous implementations by using a novel value scattering+gathering mechanism to parallelize UFLIC and designing a pathline reuse strategy to reduce the computational cost of pathline integration. By exploiting the massive parallelism of modern graphical processing units (GPU), the proposed method allows for real-time dense visualization of unsteady flows with high spatial-temporal coherence.

This paper presents a 2D flow visualization user study that we conducted using new methodologies to increase the objectiveness. We evaluated grid-based variable-size arrows, evenly spaced streamlines, and line integral convolution (LIC) variants (basic, oriented, and enhanced versions) coupled with a colorwheel and/or rainbow color map, which are representative of many geometry-based and texture-based techniques. To reduce data-related bias, template-based explicit flow synthesis was used to create a wide variety of symmetric flows with similar topological complexity. To suppress task-related bias, pattern-based implicit task design was employed, addressing critical point recognition, critical point classification, and symmetric pattern categorization. In addition, variable-duration and fixed-duration measurement schemes were utilized for lightweight precision-critical and heavyweight judgment intensive flow analysis tasks, respectively, to record visualization effectiveness. We eliminated outliers and used the Ryan REGWQ post-hoc homogeneous subset tests in statistical analysis to obtain reliable findings. Our study shows that a texture-based dense representation with accentuated flow streaks, such as enhanced LIC, enables intuitive perception of the flow, while a geometry-based integral representation with uniform density control, such as evenly spaced streamlines, may exploit visual interpolation to facilitate mental reconstruction of the flow. It is also shown that inappropriate color mapping (e.g., colorwheel) may add distractions to a flow representation.

This paper presents a new streamline placement algorithm that produces evenly spaced long streamlines while preserving topological features of a flow field. Singularities and separatrices are extracted to decompose the flow field into topological regions. In each region, a seeding path is selected from a set of streamlines integrated in the orthogonal flow field. The uniform sample points on this path are then used as seeds to generate streamlines in the original flow field. Additional seeds are placed where a large gap between adjacent streamlines occurs. The number of short streamlines is significantly reduced as evenly spaced long streamlines spawned along the seeding paths can fill the topological regions very well. Several metrics for evaluating streamline placement quality are discussed and applied to our method as well as some other approaches. Compared to previous work in uniform streamline placement, our method is more effective in creating evenly spaced long streamlines and preserving topological features. It has the potential to provide both intuitive perception of important flow characteristics and detail reconstruction across visually pleasing streamlines.

Abstract We present the results from a user study looking at the ability of observers to mentally integrate wind direction and magnitude over a vector field. The data set chosen for the study is an MM5 (PSU/NCAR Mesoscale Model) simulation of Hurricane Lili over the Gulf of Mexico as it approaches the southeastern United States. Nine observers participated in the study. This study investigates the effect of layering on the observer's ability to determine the magnitude and direction of a vector field. We found a tendency for observers to underestimate the magnitude of the vectors and a counter‐clockwise bias when determining the average direction of a vector field. We completed an additional study with two observers to try to uncover the source of the counter‐clockwise bias. These results have direct implications to atmospheric scientists, but may also be able to be applied to other fields that use 2D vector fields.

This paper presents an advanced evenly-spaced streamline placement algorithm for fast, high-quality, and robust layout of flow lines. A fourth-order Runge-Kutta integrator with adaptive step size and error control is employed for rapid accurate streamline advection. Cubic Hermite polynomial interpolation with large sample-spacing is adopted to create fewer evenly-spaced samples along each streamline to reduce the amount of distance checking. We propose two methods to enhance placement quality. Double queues are used to prioritize topological seeding and to favor long streamlines to minimize discontinuities. Adaptive distance control based on the local flow variance is explored to reduce cavities. Furthermore, we propose a universal, effective, fast, and robust loop detection strategy to address closed and spiraling streamlines. Our algorithm is an order-of-magnitude faster than Jobard and Lefer's algorithm with better placement quality and over 5 times faster than Mebarki et al.'s algorithm with comparable placement quality, but with a more robust solution to loop detection.

Unsteady flow line integral convolution (UFLIC) is a texture synthesis technique for visualizing unsteady flows with high temporal-spatial coherence. Unfortunately, UFLIC requires considerable time to generate each frame due to the huge amount of pathline integration that is computed for particle value scattering. This paper presents Accelerated UFLIC (AUFLIC) for near interactive (1 frame/second) visualization with 160,000 particles per frame. AUFLIC reuses pathlines in the value scattering process to reduce computationally expensive pathline integration. A flow-driven seeding strategy is employed to distribute seeds such that only a few of them need pathline integration while most seeds are placed along the pathlines advected at earlier times by other seeds upstream and, therefore, the known pathlines can be reused for fast value scattering. To maintain a dense scattering coverage to convey high temporal-spatial coherence while keeping the expense of pathline integration low, a dynamic seeding controller is designed to decide whether to advect, copy, or reuse a pathline. At a negligible memory cost, AUFLIC is 9 times faster than UFLIC with comparable image quality.