Generating Color Palettes using Intuitive Parameters

Color is widely used in data visualization to show data values. The proper selection of colors is critical to convey information correctly. In this paper, we present a technique for generating univariate lightness ordered palettes. These are specified via intuitive input parameters that are used define the appearance of the palette: number of colors, hue, lightness, saturation, contrast and hue range. The settings of the parameters are used to generate curves through CIELUV color space. This color space is used in order to correctly translate the requirements in terms of perceptual properties to a set of colors. The presented palette generation method enables users to specify palettes that have these perceptual properties, such as perceived order, equal perceived distance and equal importance. The technique has been integrated in Magna View, a system for multivariate data visualization.     

Invariant crease lines for topological and structural analysis of tensor fields

We introduce a versatile framework for characterizing and extracting salient structures in three-dimensional symmetric second-order tensor fields. The key insight is that degenerate lines in tensor fields, as defined by the standard topological approach, are exactly crease (ridge and valley) lines of a particular tensor invariant called mode. This reformulation allows us to apply well-studied approaches from scientific visualization or computer vision to the extraction of topological lines in tensor fields. More generally, this main result suggests that other tensor invariants, such as anisotropy measures like fractional anisotropy (FA), can be used in the same framework in lieu of mode to identify important structural properties in tensor fields. Our implementation addresses the specific challenge posed by the non-linearity of the considered scalar measures and by the smoothness requirement of the crease manifold computation. We use a combination of smooth reconstruction kernels and adaptive refinement strategy that automatically adjust the resolution of the analysis to the spatial variation of the considered quantities. Together, these improvements allow for the robust application of existing ridge line extraction algorithms in the tensor context of our problem. Results are proposed for a diffusion tensor MRI dataset, and for a benchmark stress tensor field used in engineering research.     

Complete Tensor Field Topology on 2D Triangulated Manifolds embedded in 3D

This paper is concerned with the extraction of the surface topology of tensor fields on 2D triangulated manifolds embedded in 3D. In scientific visualization topology is a meaningful instrument to get a hold on the structure of a given dataset. Due to the discontinuity of tensor fields on a piecewise planar domain, standard topology extraction methods result in an incomplete topological skeleton. In particular with regard to the high computational costs of the extraction this is not satisfactory. This paper provides a method for topology extraction of tensor fields that leads to complete results. The core idea is to include the locations of discontinuity into the topological analysis. For this purpose the model of continuous transition bridges is introduced, which allows to capture the entire topology on the discontinuous field. The proposed method is applied to piecewise linear three‐dimensional tensor fields defined on the vertices of the triangulation and for piecewise constant two or three‐dimensional tensor fields given per triangle, e.g. rate of strain tensors of piecewise linear flow fields.     

适用于识别指定颜色的色调过滤视图

为便于快速识别RGB图像中的红、绿、蓝或黄颜色,提出一种用像素的色调和色调可信度定量地评估像素对于指定颜色的隶属度的方法和彩色图像的色调过滤视图的定义,推导出直接由图像的R、G、B色彩分量计算指定颜色的色调过滤视图的公式。实验结果表明,用色调过滤视图来识别图像中指定的颜色具有较好的效果,并且受拍摄光照条件的影响较小。     

Asymmetric tensor field visualization for surfaces

Asymmetric tensor field visualization can provide important insight into fluid flows and solid deformations. Existing techniques for asymmetric tensor fields focus on the analysis, and simply use evenly-spaced hyperstreamlines on surfaces following eigenvectors and dual-eigenvectors in the tensor field. In this paper, we describe a hybrid visualization technique in which hyperstreamlines and elliptical glyphs are used in real and complex domains, respectively. This enables a more faithful representation of flow behaviors inside complex domains. In addition, we encode tensor magnitude, an important quantity in tensor field analysis, using the density of hyperstreamlines and sizes of glyphs. This allows colors to be used to encode other important tensor quantities. To facilitate quick visual exploration of the data from different viewpoints and at different resolutions, we employ an efficient image-space approach in which hyperstreamlines and glyphs are generated quickly in the image plane. The combination of these techniques leads to an efficient tensor field visualization system for domain scientists. We demonstrate the effectiveness of our visualization technique through applications to complex simulated engine fluid flow and earthquake deformation data. Feedback from domain expert scientists, who are also co-authors, is provided.     

Interactive tensor field design and visualization on surfaces

Designing tensor fields in the plane and on surfaces is a necessary task in many graphics applications, such as painterly rendering, pen-and-ink sketching of smooth surfaces, and anisotropic remeshing. In this article, we present an interactive design system that allows a user to create a wide variety of symmetric tensor fields over 3D surfaces either from scratch or by modifying a meaningful input tensor field such as the curvature tensor. Our system converts each user specification into a basis tensor field and combines them with the input field to make an initial tensor field. However, such a field often contains unwanted degenerate points which cannot always be eliminated due to topological constraints of the underlying surface. To reduce the artifacts caused by these degenerate points, our system allows the user to move a degenerate point or to cancel a pair of degenerate points that have opposite tensor indices. These operations provide control over the number and location of the degenerate points in the field. We observe that a tensor field can be locally converted into a vector field so that there is a one-to-one correspondence between the set of degenerate points in the tensor field and the set of singularities in the vector field. This conversion allows us to effectively perform degenerate point pair cancellation and movement by using similar operations for vector fields. In addition, we adapt the image-based flow visualization technique to tensor fields, therefore allowing interactive display of tensor fields on surfaces. We demonstrate the capabilities of our tensor field design system with painterly rendering, pen-and-ink sketching of surfaces, and anisotropic remeshing     

一种多通道融合的交通标志检测方法

针对单纯利用RGB或HIS空间不能很好地检测出交通标志这一问题,提出一种多通道融合的交通标志检测方法。将图像转换至HIS空间,使用迭代的Otsu算法对饱和度进行阈值分割,以色相和饱和度信息为基础,结合RGB通道的颜色特征进行颜色聚类。利用水平和垂直投影获得交通标志的外接矩形,利用曲线拟合的最小二乘法定位交通标志并对其进行分类。研究结果表明,该方法可以准确地从背景图像中提取出交通标志,有效地解决存在亮度变化、噪声干扰、方位倾斜等情况的交通标志检测问题。     

一种基于HSI的距离联合RGB 3维场边缘探测方法

在彩色图像边缘检测中，欧氏距离方法虽对亮度变化敏感，但对色度和饱和度的差异不太敏感，而矢量角方法又仅能探测色彩差异。为了在RGB3维彩色场中获取精确的边缘信息，提出了一种基于HIS(色度、饱和度和亮度)的距离联合边缘检测方法。该方法在高亮和色差大的区域主要利用矢量角对色彩差异来进行检测，而在低亮和色差小的区域则主要利用欧氏距离的亮度差异来进行边缘检测。实验表明，该方法可有效地解决矢量角存在的问题，其与LUV欧氏距离检测方法相比，则可以避免复杂的色彩空间变换计算，其检测效果也更佳。     

一种新的彩色有噪图像边缘检测方法

针对在RGB空间中很难有效区分颜色相似性问题,提出一种在HSI空间基于广义形态滤波的彩色有噪图像边缘检测方法。首先将图像转换到HSI色彩空间,然后对H、S、I三个分量分别计算熵,并利用判决机制选择熵值最大的分量进行广义形态滤波,最后对滤波结果采用形态学梯度算子检测边缘。将该方法与基于传统形态滤波的边缘检测方法进行了比较,实验结果表明,本文提出的方法在抑制噪声的同时能较好地保持边缘和细节,并且大大提高处理速度。     

Detection of diffuse and specular interface reflections and inter-reflections by color image segmentation

We present a computational model and algorithm for detecting diffuse and specular interface reflections and some inter-reflections. Our color reflection model is based on the dichromatic model for dielectric materials and on a color space, called S space, formed with three orthogonal basis functions. We transform color pixels measured in RGB into the S space and analyze color variations on objects in terms of brightness, hue and saturation which are defined in the S space. When transforming the original RGB data into the S space, we discount the scene illumination color that is estimated using a white reference plate as an active probe. As a result, the color image appears as if the scene illumination is white. Under the whitened illumination, the interface reflection clusters in the S space are all aligned with the brightness direction. The brightness, hue and saturation values exhibit a more direct correspondence to body colors and to diffuse and specular interface reflections, shading, shadows and inter-reflections than the RGB coordinates. We exploit these relationships to segment the color image, and to separate specular and diffuse interface reflections and some inter-reflections from body reflections. The proposed algorithm is effications for uniformly colored dielectric surfaces under singly colored scene illumination. Experimental results conform to our model and algorithm within the liminations discussed.     

Topology of a computed incompressible three-dimensional separated flow field around a high-angle-of-attack cone-cylinder

The concept of “topology” of a continuous vector field is introduced to numerical studies on the structure of a three-dimensional separated flow field. In this paper, the flow structure is regarded as the topological properties of the streamlines and described as a composition of finite types of basic elements correlated with some typical flow features. The present topological studies are made in four vector fields defined in the flow around a body with regard to some physical aspects. All possible basic elements are formulated as a critical point or line in each vector field by use of the eigenvalues and eigenvectors for a local velocity gradient tensor. The former represents the quantitative topological properties and the latter determines the direction of a critical line. The method used to identify these basic elements from numerical flow data is also presented. Computational studies on a separated flow field around a high-angle-of-attack cone-cylinder demonstrate that the present topological approaches have great potential for revealing the essential properties of flow phenomena with three-dimensional separation.     

基于RGB灰度值缩放的彩色图像增强

为保持彩色图像增强时色调不变,提出一种基于RGB灰度值缩放的彩色图像增强算法。采用二次函数增强像素RGB三基色的最大值,获得缩放因子k,利用k值增强相应像素的RGB灰度值。实验结果表明,该算法能增强不同压缩比的彩色图像,综合性能优于现有算法。     

Topological Features in 2D Symmetric Higher‐Order Tensor Fields

The topological structure of scalar, vector, and second‐order tensor fields provides an important mathematical basis for data analysis and visualization. In this paper, we extend this framework towards higher‐order tensors. First, we establish formal uniqueness properties for a geometrically constrained tensor decomposition. This allows us to define and visualize topological structures in symmetric tensor fields of orders three and four. We clarify that in 2D, degeneracies occur at isolated points, regardless of tensor order. However, for orders higher than two, they are no longer equivalent to isotropic tensors, and their fractional Poincaré index prevents us from deriving continuous vector fields from the tensor decomposition. Instead, sorting the terms by magnitude leads to a new type of feature, lines along which the resulting vector fields are discontinuous. We propose algorithms to extract these features and present results on higher‐order derivatives and higher‐order structure tensors.     

光照色调颜色恒常性算法研究

提出了一种新的适合于实时处理视频图像颜色识别的光照色调颜色恒常性算法。该算法主要解决在视频颜色识别中，由于近点光源的发散作用和在复杂背景影响下所造成的目标对象在图像局部各点上的颜色识别问题。在彩色感知的光学原理和数学模型基础上推导出了光照校正算法，并利用色调校正实现颜色恒常性。通过试验分析评估了各种识别算法，如光照校正、色调校正、光照色调校正、以及线性回归算法对颜色的校正效果，最后将测得的颜色差值和色调差值进行对比，证明了本算法对点光源散射作用引起的光照变化具有良好的校正作用，并证明了其算法理论推导的合理性。该算法的运算复杂度为O(n)，因而适合于视频的实时处理。     

恶意软件的操作码可视化方法研究

提出了一种基于操作码频率的恶意代码可视化分析方法。该方法在静态反汇编的基础上,获取机器指令中的操作码序列,使用设计的色谱来区分常见的和罕见的操作码指令,并依据对应颜色向量在RGB空间中的次序来重排操作码的位置,以此实现关于操作码频率的映射,解决了现有可视化方法视觉区分度不强、分类精准度不高的问题。将该方法应用于微软提供的恶意样本集（BIG 2015|Kaggle）,可视化结果经深度融合网络学习后,取得了98.50%的分类正确率。     

Human mimic color perception for segmentation of color images using a three-layered self-organizing map previously trained to classify color chromaticity

Most of the works addressing segmentation of color images use clustering-based methods; the drawback with such methods is that they require a priori knowledge of the amount of clusters, so the number of clusters is set depending on the nature of the scene so as not to lose color features of the scene. Other works that employ different unsupervised learning-based methods use the colors of the given image, but the classifying method employed is retrained again when a new image is given. Humans have the nature capability to: (1) recognize colors by using their previous knowledge, that is, they do not need to learn to identify colors every time they observe a new image and, (2) within a scene, humans can recognize regions or objects by their chromaticity features. Hence, in this paper we propose to emulate the human color perception for color image segmentation. We train a three-layered self-organizing map with chromaticity samples so that the neural network is able to segment color images by their chromaticity features. When training is finished, we use the same neural network to process several images, without training it again and without specifying, to some extent, the number of colors the image have. The hue component of colors is extracted by mapping the input image from the RGB space to the HSV space. We test our proposal using the Berkeley segmentation database and compare quantitatively our results with related works; according to the results comparison, we claim that our approach is competitive.

     

扩散张量加权梯度域图像彩色化方法

基于扩散张量的加权拉普拉斯核推广了图像彩色化的泊松解法,该彩色化过程是通过颜色在亮度值扩散张量加权的梯度场引导下自动传播完成的.首先在灰度图像上由用户手工地给定少量的颜色条带;然后计算每个像素的扩散张量,并利用这些扩散张量构造加权梯度场,从而导出基于散度的图像彩色化方程;最后求解方程,获得灰度图像着色结果.实验结果表明:该方法效果良好,比原泊松解法有显著改善.