Multivariate data visualization using shadow



Similar documents
A Short Introduction to Computer Graphics

Choosing Colors for Data Visualization Maureen Stone January 17, 2006

Expert Color Choices for Presenting Data

INTRODUCTION TO RENDERING TECHNIQUES

Visibility optimization for data visualization: A Survey of Issues and Techniques

Template-based Eye and Mouth Detection for 3D Video Conferencing

Using Photorealistic RenderMan for High-Quality Direct Volume Rendering

Visualization and Feature Extraction, FLOW Spring School 2016 Prof. Dr. Tino Weinkauf. Flow Visualization. Image-Based Methods (integration-based)

DATA VISUALIZATION GABRIEL PARODI STUDY MATERIAL: PRINCIPLES OF GEOGRAPHIC INFORMATION SYSTEMS AN INTRODUCTORY TEXTBOOK CHAPTER 7

Computer-Generated Photorealistic Hair

D animation. Advantages of 2-D2. Advantages of 3-D3. Related work. Key idea. Applications of Computer Graphics in Cel Animation.

Green = 0,255,0 (Target Color for E.L. Gray Construction) CIELAB RGB Simulation Result for E.L. Gray Match (43,215,35) Equal Luminance Gray for Green

Robert Collins CSE598G. More on Mean-shift. R.Collins, CSE, PSU CSE598G Spring 2006

Building an Advanced Invariant Real-Time Human Tracking System

Introduction to Computer Graphics

Computer Applications in Textile Engineering. Computer Applications in Textile Engineering

Perception of Light and Color

Visualization methods for patent data

VISUALIZATION TOOLS FOR AGENT-BASED MODELING IN NETLOGO

Tutorial 3: Graphics and Exploratory Data Analysis in R Jason Pienaar and Tom Miller

Elements of Art Name Design Project!

VISUAL ARTS VOCABULARY

Enhanced LIC Pencil Filter

Principles of Data Visualization for Exploratory Data Analysis. Renee M. P. Teate. SYS 6023 Cognitive Systems Engineering April 28, 2015

Intuitive Navigation in an Enormous Virtual Environment

Image Processing and Computer Graphics. Rendering Pipeline. Matthias Teschner. Computer Science Department University of Freiburg

CS171 Visualization. The Visualization Alphabet: Marks and Channels. Alexander Lex [xkcd]

3 hours One paper 70 Marks. Areas of Learning Theory

How many PIXELS do you need? by ron gibbs

A Scientific Visualization Schema Incorporating Perceptual Concepts

HSI BASED COLOUR IMAGE EQUALIZATION USING ITERATIVE n th ROOT AND n th POWER

Bernice E. Rogowitz and Holly E. Rushmeier IBM TJ Watson Research Center, P.O. Box 704, Yorktown Heights, NY USA

Robot Perception Continued

Adding Animation With Cinema 4D XL

Diagrams and Graphs of Statistical Data

Graphic Design. Background: The part of an artwork that appears to be farthest from the viewer, or in the distance of the scene.

Digital Image Basics. Introduction. Pixels and Bitmaps. Written by Jonathan Sachs Copyright Digital Light & Color

Lecture 1: The Visual System

Colorado School of Mines Computer Vision Professor William Hoff

Vision based Vehicle Tracking using a high angle camera

Big Data: Rethinking Text Visualization

Digitisation Disposal Policy Toolkit

Part 2: Data Visualization How to communicate complex ideas with simple, efficient and accurate data graphics

Cartoon-Looking Rendering of 3D-Scenes

3D Data Visualization / Casey Reas

Principles of Data Visualization

Monash University Clayton s School of Information Technology CSE3313 Computer Graphics Sample Exam Questions 2007

Section 11.4: Equations of Lines and Planes

Digital Image Fundamentals. Selim Aksoy Department of Computer Engineering Bilkent University

A Proposal for OpenEXR Color Management

Time Series Data Visualization

Projection Center Calibration for a Co-located Projector Camera System

Visualizing of Berkeley Earth, NASA GISS, and Hadley CRU averaging techniques

A Color Placement Support System for Visualization Designs Based on Subjective Color Balance

Computer Animation: Art, Science and Criticism

Lines & Planes. Packages: linalg, plots. Commands: evalm, spacecurve, plot3d, display, solve, implicitplot, dotprod, seq, implicitplot3d.

Visualization Techniques in Data Mining

TEXTURE AND BUMP MAPPING

Visualization. For Novices. ( Ted Hall ) University of Michigan 3D Lab Digital Media Commons, Library

Course Overview. CSCI 480 Computer Graphics Lecture 1. Administrative Issues Modeling Animation Rendering OpenGL Programming [Angel Ch.

AUDIO. 1. An audio signal is an representation of a sound. a. Acoustical b. Environmental c. Aesthetic d. Electrical

COMP175: Computer Graphics. Lecture 1 Introduction and Display Technologies

A NEW SUPER RESOLUTION TECHNIQUE FOR RANGE DATA. Valeria Garro, Pietro Zanuttigh, Guido M. Cortelazzo. University of Padova, Italy

Multi-Attribute Glyphs on Venn and Euler Diagrams to Represent Data and Aid Visual Decoding

Using Microsoft Picture Manager

Outline. Quantizing Intensities. Achromatic Light. Optical Illusion. Quantizing Intensities. CS 430/585 Computer Graphics I

Modelling 3D Avatar for Virtual Try on

Automatic 3D Reconstruction via Object Detection and 3D Transformable Model Matching CS 269 Class Project Report

Creating a History Day Exhibit Adapted from materials at the National History Day website

ELEMENTS AND PRINCIPLES OF DESIGN

McAFEE IDENTITY. October 2011

PART 1 Basic Setup. Section 1.1 Direct The Strokes 1.1.1

Investigation of Color Aliasing of High Spatial Frequencies and Edges for Bayer-Pattern Sensors and Foveon X3 Direct Image Sensors

Color holographic 3D display unit with aperture field division

Op Art: Working With Optical Illusions Review Questions

An Energy-Based Vehicle Tracking System using Principal Component Analysis and Unsupervised ART Network

The 3D rendering pipeline (our version for this class)

MassArt Studio Foundation: Visual Language Digital Media Cookbook, Fall 2013

Logo Standards Guideline

Optical Design Tools for Backlight Displays

ELEMENTS OF ART & PRINCIPLES OF DESIGN

Visualization Techniques for Geospatial Data IDV 2015/2016

USING ADOBE PhotoShop TO MEASURE EarthKAM IMAGES

TEXT-FILLED STACKED AREA GRAPHS Martin Kraus

Visualization Software

Assessment. Presenter: Yupu Zhang, Guoliang Jin, Tuo Wang Computer Vision 2008 Fall

Deferred Shading & Screen Space Effects

Circle Object Recognition Based on Monocular Vision for Home Security Robot

EUSIPCO

Computer Graphics Global Illumination (2): Monte-Carlo Ray Tracing and Photon Mapping. Lecture 15 Taku Komura

Computer Graphics. Introduction. Computer graphics. What is computer graphics? Yung-Yu Chuang

Transcription:

Proceedings of the IIEEJ Ima and Visual Computing Wor Kuching, Malaysia, Novembe Multivariate data visualization using shadow Zhongxiang ZHENG Suguru SAITO Tokyo Institute of Technology ABSTRACT When visualizing data, one expected effect is to overview the data intuitively. In case of multivariate data visualization, combinations of visual cues which are color, texture, shape, size and orientation have been used. In this study, we focus on shadow which is distinguished from texture or color gradation unconsciously by human perception as a new visual cue for visualization, and introduce three methods which use shadows for multivariate data visualization. These are shadow by cylinder, texture-like shadow, and the combination of them. As the experimental result, we show visualization images that these methods make from weather data. 1 INTRODUCTION When visualizing data, one expected effect is to overview the data intuitively. Especially in case of visualizing multivariate data, it is very useful to display them at the same time. In that case, it is necessary to display the values so that viewer can read each element separately. Elements that have been used to visualize multivariate data are color, texture, shape, size and orientation. Visualization has been achieved by using these elements together[1]. In this study, we focus on the shadow which has not been used as an element for visualization. When considering the effect of the shadow, the presence of the shadow will become an important clue for the relationship of object positions, the shape of the shadow receiver s surface and the shape of the occluder[2]. In addition, we can cancel the change of color caused by the shadow as 1 we realize through some optical illusions[3]. That is, change of color by the base color change and by shadow can unconsciously be recognized separately. Therefore, We propose visualization methods that uses these characteristics of the shadow. 2 PREVIOUS WORK When using colors for multivariate data visualization, a certain color property tends to reduce readability of other properties. Rheingans showed that the difficulty of reading can be reduced by using multivariate data with the pair of hue and brightness is relatively lower than other pairs of color properties[4]. Several methods which use texture for visualization were proposed. Natural textures which are more intricate and rich in detail than standard primitives archives more vast and comprehensive data representation than standard primitives are used for multivariate data visualization[5]. To visualize multivariate data, data attributes correspond to the scale, orientation and brightness of textures respectively[6]. However, these correspondence is not intuitive to understand. In this paper, we propose the technique which is easy and intuitive to read the value like a bar graph. Colin[7] proposed the method to visualize two variables with high readability by using textures and colors together. However textures are overlapped on background colors, important information may be covered completely by these textures. We propose the visualization technique using the shadow which does not hide background colors completely.

3 OUR METHOD We introduce three methods which use shadows for multivariate data visualization. There are shadow by cylinder, texture-like shadow, and the combination of them. 3.1 Shadow by cylinder with bump shade The data to be displayed is 2 variate data which can be mapped on a plane. var 1 and var 2 take the values from 0 to 1. var 1 decides a color and var 2 corresponds to height. These two values generates a colored bump surface. Hereafter, the color determined by var 1 is called ground color. After performing the spline interpolation to var 2, a smooth bump surface whose height is decided by var 2 is generated. We display the local difference of the surface by shade. In order to distinguish the color changes by shade and by shadow, we use the technique based on the idea of the cold-to-warm tone[8] for shading. Shading is displayed using the tone of dark blue to bright yellow. Red to green tone is used for displaying var 1 as ground color. In order to display the absolute height at points on the surface, we put cylinders whose height and thickness are proportional to the height at the points. The heights of cylinders are displayed by casting shadows to surface. The form of a shadow is dependent on both the form of a surface which the shadow is cast on and the shape of an occluder. To get information from the length of shadows, the length of shadows should only be proportional to the height of cylinders. If the generated surface is the real bump surface, the length of shadows is not proportional to the height of cylinders under the influence of surface. To avoid this, the bump surface is expressed only by shading like bump mapping in our method. That is, the shadows which appear with cylinders are displayed as if on the flat plane. Cylinders are put randomly to avoid overlap of shadows. The actual calculation of RGB color C of a pixel is performed by the following equations with constants L, k, s, t, u, 2 C = LabtoRGB(l, a, b), (1) l = L + s shade t(1 shadow), (2) a = k(var 1 0.5), (3) b = u shade and (4) shade = l n, (5) where LabtoRGB(l, a, b) is the function which calculates RGB color from CIELAB color, shadow is the ratio of shadow, whose range is 0 to 1, and l and n are the normalized light vector and the normalized normal vector at the pixel calculated from fake bump surface. When pixel is in a shadow completely and shadow becomes 0 which is calculated by PCF[9]. 3.2 Texture-like shadow Considering with the possibility of the simultaneous use with shadow by cylinder we design one more type of shadow, texture-like shadow. To display shadow by cylinder with shading and texture-like shadow at the same time, we need to use marks which is easy to distinct and read values. We use a series of marks as shown in Fig. 1. A texture-like shadow is equal to an orthogonal projection of a occluder. Occluders whose shape varied by var 2 are set above the camera and a parallel light is put above the occluders. Size of occluders is varied so that the size of a shadow becomes the largest when var 2 = 1 and becomes smallest when var 2 = 0. Since it is guaranteed that these shadows do not overlap, these shadows can be expressed in higher density than shadows by cylinder. When displaying var 1 using color and var 2 with texture-like shadow simultaneously, equation (1), (2), and (3) are calculated with b = 0. 3.3 Combined method We consider the combination of color, shade, shadow by cylinder, and a texture-like shadow to visualize 3 variate data. When combining shadow by cylinder and a texture-like shadow, two light sources are required

s and t are related with the change of brightness. When s and u are large, the bumpy surface is easy to see. When t is large, shadows become dark and become easy to see. However the readability of ground color is reduced when s,u and t are too large. Figure 1: Example of texture-like shadow. Display values which change from 0 to 1 continuously. to generate two kinds of shadows. Because the densities of a shadows are different, the influence to the readability of ground colors are also difference. The distance of shadows in texture-like shadow is adjusted to be shorter than the average distance of cylinders. The darkness of texture-like shadow are adjusted to be brighter than shadow by cylinder by adjusting intensity of lights. When calculating a final color, the same calculation as Sec. 3.1 is performed under each light source condition, and the values of shade and shadow are decided by the weighted average with equations, shade = w 1shade 1 + w 2 shade 2 w 1 + w 2 and (6) shadow = w 1shadow 1 + w 2 shadow 2 w 1 + w 2 (7) where, w 1 and w 2 are the intensities of light sources and shade 1, shade 2, shadow 1 and shadow 2 are results of shade and shadow under each light source. 3.4 setting of parameters and a light sources Five coefficients L, k, s, t, u are used in these proposal techniques. L is the coefficient of the brightness of ground color, s is the coefficient of change on color axis of green to red, s is the coefficient of brightness change by shading, t is the coefficient of the decrease of brightness by a shadow and u is the coefficient of change on color axis of blue to yellow which caused by shading. Readability depends on these five coefficients. Since ground colors become dark by a shadow, L should take a large value. When k is larger, the ground color becomes more vivid. However the color will be out of the RGB color space if k is too large. Both 4 RESULTS We show results of our method applied for weather data. We set parameters as L = 70, k = 70, s = 20, t = 15, u = 20 in Fig.2(a)(b) and L = 70, k = 70, s = 20, t = 35, u = 20 in Fig.2(c). In these results, color, shade and shadow by cylinder and texture-like shadow correspond to temperature, wind speed and humidity respectively. Fig.2(a) shows temperature and wind speed. Fig.2(b) shows temperature and humidity. Fig.2(c) shows temperature, wind speed and humidity. In each case, the color element under shadows is easy to see. By combining shadow by cylinder and shade for one variate, we can see both local differences with high resolution and absolute values. Shadow by cylinder and texture-like shadow are easy to distinguish, even they are overlapped. The shade of a bump surface and the length of shadows by cylinder, visualize local change and absolute value of data respectively. Showing change of colors by shadow and shade with different color axis, we can read two types of color changes separately. Fig.3 are results of every 6 hours of weather data. We can recognize that the big wind region moves from south of Japan to middle of Japan easily. 5 CONCLUSION We proposed three visualization methods to visualize multivariate data using shadows, shade and ground color. In our methods, shadows do not hide ground colors completely and we can read both shadows and ground colors easily. To distinguish shade and shadows, these change of color is rendered with different color axes. As future work, we will perform user tests to evaluate out methods. 3

(a)result using color, shade and shadow by cylinder (b)result using color and texture-like shadow (c)result using color, shade, shadow by cylinder and texture-like shadow Figure 2: Example results by three methods. 4

Figure 3: Results which visualize weather data of every 6 hours. 5

References [1] H. Senay and E. Ignatius. A knowledge-based system for visualization design. Computer Graphics and Applications, IEEE, Vol. 14, No. 6, pp. 36 47, nov 1994. [2] Pascal Mamassian, David C. Knill, and Daniel Kersten. The perception of cast shadows. Trends in Cognitive Sciences, Vol. 2, No. 8, pp. 288 295, August 1998. [3] Edward H. Adelson. Checkershadow illusion. http://web.mit.edu/persci/people/adelson/ checkershadow_illusion.html. [4] Penny Rheingans. Task-based color scale design. In Proceedings Applied Image and Pattern Recognition. SPIE, pp. 35 43, 1999. [5] Victoria Interrante. Harnessing natural textures for multivariate visualization. IEEE Comput. Graph. Appl., Vol. 20, No. 6, pp. 6 11, November 2000. [6] Ying Tang, Huamin Qu, Yingcai Wu, and Hong Zhou. Natural textures for weather data visualization. In Information Visualization, 2006. IV 2006. Tenth International Conference on, pp. 741 750, july 2006. [7] Colin Ware. Quantitative texton sequences for legible bivariate maps. IEEE Transactions on Visualization and Computer Graphics, Vol. 15, pp. 1523 1530, 2009. [8] Amy Gooch, Bruce Gooch, Peter Shirley, and Elaine Cohen. A non-photorealistic lighting model for automatic technical illustration. In Proceedings of the 25th annual conference on Computer graphics and interactive techniques, SIGGRAPH 98, pp. 447 452, New York, NY, USA, 1998. ACM. [9] William T. Reeves, David H. Salesin, and Robert L. Cook. Rendering antialiased shadows with depth maps. SIGGRAPH Comput. Graph., Vol. 21, pp. 283 291, August 1987. 6