Intelligent vector field visualization based on line integral convolution

Line integral convolution (LIC) is an effective and powerful technique for generating images from vector fields. In the 3D-LIC, it is very important to select an adequate region of interest (ROI) in the vector field. One way for specifying ROI is to use a surface defined in the vector field. The other way is to use a significance map that defines a ROI related values at each point in the vector field. To represent an anisotropic vector field around a vortex center in an understandable way, we introduced a time-oriented significance map. Our technique for specifying ROI is to use a passage-time for a mass-less particle to travel from near a vortex center to a pixel location. In our technique, what we call “restricted LIC technique (RLIC), ” we refer to the passage-time buffer before we start the convolution p.rocess at a pixel location. To confirm the effectiveness of our technique, we use an anisotropic swirl vector field and construct two types of significance maps, a distance-oriented map and a time-oriented map. We will show the difference in the resulting images are generated from these significance maps.

Line integral convolution (LIC) is an effective technique for visualizing vector fields. The application of LIC to 3D flow fields has yet been limited by difficulties to efficiently display and animate the resulting 3D-images. Texture-based volume rendering allows interactive visualization and manipulation of 3D-LIC textures. In order to ensure the comprehensive and convenient exploration of flow fields, we suggest interactive functionality including transfer functions and different clipping mechanisms. Thereby, we efficiently substitute the calculation of LIC based on sparse noise textures and show the convenient visual access of interior structures. Further on, we introduce two approaches for animating static 3D-flow fields without the computational expense and the immense memory requirements for pre-computed 3D-textures and without loss of interactivity. This is achieved by using a single 3D-LIC texture and a set of time surfaces as clipping geometries. In our first approach we use the clipping geometry to pre-compute a special 3D-LIC texture that can be animated by time-dependent color tables. Our second approach uses time volumes to actually clip the 3D-LIC volume interactively during rasterization. Additionally, several examples demonstrate the value of our strategy in practice.

ObjectiveThere are little data regarding the efficacy of texture and color enhancement imaging (TXI) for early gastric cancer (EGC) diagnosis. This study aimed to compare the color difference and visibility of EGC between white light imaging (WLI) and TXI.MethodsThis study included 20 EGCs of 18 patients undergoing endoscopic submucosal dissection. Still images of EGC in WLI, TXI mode 1 (with color enhancement), and TXI mode 2 (without color enhancement), which were consistent in distance, angle, and air insufflation, were constructed by computer simulation. The center of the lesion, eight equal peripheral points 5 mm outside the lesion, and eight inner points two‐thirds of the distance from peripheral points to the EGC lesion center were annotated. Mean color differences (ΔE) of the area between peripheral and inner points per lesion in WLI, TXI mode 1, and TXI mode 2 were analyzed. In addition, four endoscopists independently scored the visibility of EGC images of TXI mode 1 and 2 compared with WLI.ResultsClinicopathological characteristics were as follows: 0‐IIa/0‐IIb/0‐IIc/0‐IIa+IIc = 6/1/11/2, reddish/pale = 10/10, differentiated/undifferentiated = 18/2, median tumor size = 13.5 mm. Mean ΔE ± SD = WLI/TXI mode1/TXI mode2 = 10.3 ± 4.7, 15.5 ± 7.8, and 12.7 ± 6.1, respectively. Mean ΔE was significantly higher in TXI mode 1 than in WLI. Visibility (improved/no change/decreased) was 7/13/0 and 4/16/0 in TXI mode 1 and 2, respectively. The visibility was significantly more commonly improved in the macroscopic type 0‐IIc or 0‐IIb than in 0‐IIa or IIa+IIc in TXI mode 1.ConclusionsTXI could improve the visibility of EGC compared with WLI.

The analysis of data that consists of multiple vector fields can be greatly facilitated by the simultaneous visualization of the vector fields. An effective visualization must accurately reflect the key physical structures of the fields in a way that does not allow for an unintended bias toward one distribution. While there are several effective techniques to visualize a single vector field through equally spaced streamlines, applying these techniques to individual vector fields and combining them in a single image yields undesirable artifacts. In this paper, we present strategies for the effective visualization of two vector fields through the use of streamlines.

Vector field visualization techniques have evolved very rapidly over the last two decades, however, visualizing vector fields on complex boundary surfaces from computational flow dynamics (CFD) still remains a challenging task. In part, this is due to the large, unstructured, adaptive resolution characteristics of the meshes used in the modeling and simulation process. Out of the wide variety of existing flow field visualization techniques, vector field clustering algorithms offer the advantage of capturing a detailed picture of important areas of the domain while presenting a simplified view of areas of less importance. This paper presents a novel, robust, automatic vector field clustering algorithm that produces intuitive and insightful images of vector fields on large, unstructured, adaptive resolution boundary meshes from CFD. Our bottom-up, hierarchical approach is the first to combine the properties of the underlying vector field and mesh into a unified error-driven representation. The motivation behind the approach is the fact that CFD engineers may increase the resolution of model meshes according to importance. The algorithm has several advantages. Clusters are generated automatically, no surface parameterization is required, and large meshes are processed efficiently. The most suggestive and important information contained in the meshes and vector fields is preserved while less important areas are simplified in the visualization. Users can interactively control the level of detail by adjusting a range of clustering distance measure parameters. We describe two data structures to accelerate the clustering process. We also introduce novel visualizations of clusters inspired by statistical methods. We apply our method to a series of synthetic and complex, real-world CFD meshes to demonstrate the clustering algorithm results.

Line Integral Convolution (LIC) is a common approach for the visualisation of 2D vector fields. It is well suited for visualizing the direction of a flow field, but it gives no information about the orientation of the underlying vectors. We introduce Oriented Line Integral Convolution (OLIC), where direction as well as orientation are encoded within the resulting image. This is achieved by using a sparse input texture and a ramp like (anisotropic) convolution kernel. This method can be used for animations, whereby the computation of so called pixel traces fastens the calculation process. Various OLICs illustrating simple and real world vector fields are shown.

The need for efficient seismic interference (SI) attenuation solutions is essential to reduce time-sharing that is very costly. We present two SI attenuation methods, both based on the automatic vector field estimation of the SI local moveout. The first method proposes an automatic determination of tau-p mute parameters, while the second one uses line integral convolution and the estimated vector field to separate SI from reflection hyperbolas. Because of the automatic detection of SI attributes, they require little user interaction and the processing time is short. Both methods are tested on real shot gathers and successfully attenuate the SI while preserving the reflections.

This paper describes an approach for interactive visualization of mixed scalar and vector fields, in which vector icons are generated from pre-voxelized icon templates and volume-rendered together with the volumetric scalar data. This approach displays simultaneously the global structure of the scalar field and the detailed features of the vector field. Interactive visualization is achieved with incremental image update, by re-rendering only a small portion of the image wherever and whenever a change occurs. This technique supports a set of interactive visualization tools, including change of vector field visualization parameters, real-time animation of vector icons advected within the scalar field, a zooming lens, and a local probe. Subject Terms: data visualisation; interactive systems; graphical user interfaces; rendering (computer graphics); interactive visualization; scalar fields; vector fields; vector icons; pre-voxelized icon templates; volume-rendered; volumetric scalar data; incremental image update; real-time animation; zooming lens; local probe

A new method for the simplification and the visualization of vector fields is presented based on the notion of centroidal Voronoi tessellations (CVT's). A CVT is a special Voronoi tessellation for which the generators of the Voronoi regions in the tessellation are also the centers of mass (or means) with respect to a prescribed density. A distance function in both the spatial and vector spaces is introduced to measure the similarity of the spatially distributed vector fields. Based on such a distance, vector fields are naturally clustered and their simplified representations are obtained. Our method combines simple geometric intuitions with the rigorously established optimality properties of the CVTs. It is simple to describe, easy to understand and implement. Numerical examples are also provided to illustrate the effectiveness and competitiveness of the CVT-based vector simplification and visualization methodology.

Line Integral Convolution (LIC) has been widely adopted in pencil hatching generation, where an image degraded by random binary white noise (RBWN) is filtered by LIC along a priori vector field. Nonetheless, an RBWN degraded image through LIC produces hatching graduation only in terms of stroke intensity, and can neither produce hatching graduation explicitly in stroke density nor create visually clear hatching strokes while input pixel values are fairly low. In this paper we address these issues from a noise point of view by assessing several noise models and subsequently constructing a new noise model, called hybrid noise. The new noise model has been experimentally demonstrated in simulating hatching graduation in terms of both stroke intensity and stroke density with quantified graduality measurements. To oversee the effectiveness of hybrid noise we implement the whole pipeline of pencil drawing simulation and compare our results with those state of the art algorithms.

Recognition of lesions with subtle morphological and/or color changes during white light imaging (WLI) endoscopy remains a challenge. Often the endoscopic image suffers from nonuniform illumination across the image due to curvature in the lumen and the direction of the illumination light of the endoscope. We propose an image enhancement technology to resolve the drawbacks above called texture and color enhancement imaging (TXI). TXI is designed to enhance three image factors in WLI (texture, brightness, and color) in order to clearly define subtle tissue differences. In our proposed method, retinex-based enhancement is employed in the chain of endoscopic image processing. Retinex-based enhancement is combined with color enhancement to greatly accentuate color tone differences of mucosal surfaces. We apply TXI to animal endoscopic images and evaluate the performance of TXI compared with conventional endoscopic enhancement technologies, conventionally used techniques for real-world image processing, and newly proposed techniques for surgical endoscopic image augmentation. Our experimental results show that TXI can enhance brightness selectively in dark areas of an endoscopic image and can enhance subtle tissue differences such as slight morphological or color changes while simultaneously preventing over-enhancement. These experimental results demonstrate the potential of the proposed TXI algorithm as a future clinical tool for detecting gastrointestinal lesions having difficult-to-recognize tissue differences.

Streamline1 is one of the most widely used method in vector field visualization. However, with the expansion of data size, the existing methods cannot ensure the efficiency while maintaining high accuracy. In order to analyse the vector field efficiently, this paper proposes an algorithm for streamline visualization in unstructured vector field while keeping both efficiency and accuracy based on Kipfer's algorithm[1]. First, all grids are classified according to their Jordan canonical form. Then the streamline expressions in each grid cell are deduced for each kind of the grids, and we found that the expressions can be represented in a uniform way. Finally, the exit point of the streamline in the grid cell is calculated by solving the equation composed of the streamline expression and the equation of each face of the tetrahedron. The exit point can be obtained quickly and accurately by using Newton iterative method. The experimental results show that our algorithm is more efficient for streamline visualization with the same accuracy than Kipfer's algorithm.

One major discipline of Scientific Visualization is the display of vector field data. We address the problem of visualizing vector fields containing critical points of higher order Poincare-Index. We use polynomial pieces so that the degree is high enough to get the right Poincare-Index at the location of the critical points. This is an essential improvement because nearly all visualization of vector fields up to now used piecewise linear interpolation and therefore can not show this kind of critical points. A description of vector fields in Clifford algebra opens the way to a better understanding of some plane polynomial vector fields. This is used to choose the polynomial approximations needed for the visualization.

Interactive exploration of tensor fields in geosciences using volume rendering

The increasing interest in health-enhancing diets and functional foods has driven attention toward traditional crops like millets and pulses. These underutilized food sources offer a promising alternative to conventional cereals owing to their rich nutritional profile, including dietary fiber, resistant starch, quality proteins, essential micronutrients, and phytochemicals. The opportunities for value addition extend to the development of gluten-free baked food products, snacks, and breakfast cereals; improvement of texture and color in gluten-free breads and batters; enhancement of texture and color in fresh and dry gluten-free pasta; development of gluten-free soups, sauces, and gravies; protein enrichment in fruit smoothies and vegan protein drinks; and the creation of egg-replacement products, among others. This review critically examines the nutritional composition, functional properties, processing techniques, product development, and consumer acceptability of functional foods derived from millets and pulses. Furthermore, it discusses the role of these crops in addressing malnutrition, promoting food diversity, and supporting sustainable agriculture in the face of lifestyle-related diseases.