Visualization Stages, Sensory vs. Arbitrary symbols, Data Characteristics, Visualization Goals. Trajectory Reminder
|
|
- Robert O’Neal’
- 6 years ago
- Views:
Transcription
1 Visualization Stages, Sensory vs. Arbitrary symbols, Data Characteristics, Visualization Goals Russell M. Taylor II Slide 1 Trajectory Reminder Where we ve been recently Seen nm system that displays 2D-in-3D surfaces and provides haptic control using novel input devices Seen science experiments that relied on this Where we re headed next Understand why data type, human visual system, and question asked affect vis choices Learn about available VR input devices Final Goal for part 1 of course: How to build one? Learn the available 2D display types & when to use Learn about haptic display and control Slide 2 Foundation for a Science of Data Visualization What does Ware say are the advantages of visualization? Slide 3
2 Visualization Stages Collect the data (lab work or simulation) Transform the data into a format readable by the visualization software into the form most likely to reveal information (Rspace) Visualization algorithms run on graphics hardware or software renderers Human views and interacts with the visualization (changing parameters, techniques, view direction) Preferably: User studies to evaluate effectiveness Slide 4 Slide 5 Sensory vs. Arbitrary Symbols Sensory: You can see and understand without training. Match the way our brains are wired Object shape, color, texture Arbitrary: Must be learned Having no perceptual basis The word dog Slide 6
3 Properties of Sensory Reps. Can be understood without training Resistant to instructional bias Is processed very quickly, and in parallel Is valid across cultures Danger: Poor mappings can be misunderstood, even in the presence of instruction, quickly and without effort. Slide 7 Properties of Arbitrary Reps. Formally powerful Capable of rapid change May already be learned (summation notation) Dangers: Can be hard to learn (alphabet) Can be easy to forget Can vary with culture and application (different disciplines use different symbols for the same concept and the same symbol for different concepts): i = sqrt(-1), i = current Slide 8 Two-Stage Model of Perceptual Processing Preattentive Attentive Slide 9
4 Slide 10 What is a Good Visualization? Understanding means making a model that captures the essence of a system A model is an abstraction with the important things in and the unimportant out Different visualizations provide different levels of detail, show and hide different things; so support different abstractions Good visualizations are those that are useful to aid understanding, not just realistic representations (what color is a carbon atom?) Good visualizations map the important parts of the task onto techniques that show the relevant characteristics best Slide 11 Data Characteristics and Visualization Goals Why classify data and visualization goals? No known silver bullet technique Helps select which technique(s) to try Helps predict other uses for good techniques Some tools only work with some formats (This section draws heavily on sources outside the Ware book) Slide 12
5 Data Characteristics Dimensionality Type of each value/field Structure of the sampling Other characteristics Slide 13 Dimensionality Of the space the fields are embedded in (2D or 3D) Of each data field (0=point, 1=line, 2=surface, 3=volume, ) Of the data type in each field (scalar, vector, tensor) Of the space used to visualize the data Two 2D scalar fields in 2D (drawn in 3D) 3D vector field in 3D 2D isosurfaces of 3D scalar field in 3D 2D vec/tensor fields Embedded in 3D Drawn in 2D Slide 14 Type of each Value Nominal: names without ordering Continents: Africa, Antarctica, Asia, Australia, Europe North America, South America. Ordinal: Less than relationship holds Rental cars: Economy, Compact, Mid-sized, Full-sized. Interval: Relative measurements, no absolute zero Height of AFM scan Ratio: Absolute zero (can say twice as much as ) Height above sea level (not height ), Account balance Slide 15
6 Structure of the Sampling Grid Structured Square/Cube Rectilinear Curvilinear Unstructured Tetrahedral Cloud of points Slide 16 Other Data Characteristics Spatial/temporal frequencies in the data Continuous vs. Discrete Sampling of the field (aperture) Values within each sample (truncation) Missing values? Interpolate? Show explicitly? Special values? Of particular interest to visualize Zero for some ratio scales (height above sea level) Slide 17 Data Characteristics: Example Slide 18
7 Data Characteristics: Example Slide 19 Visualization Problems vs. Data Types Medical Scientific Information 2D Scalar Square 3D Scalar Rectilinear 2D Structured 3D Unstructured n D Scalar Vector Slide 20 Slide 21
8 Goal-Based Visualization Design High-level goals / middle-level tasks / atomic actions Determine task(s) before determining representations!!! tasks often determined informally or implicitly Each representation may serve one high-level goal Slide 22 Visualization Goals Exploration Gaining new (unexpected, profound) insights Increasing scientific productivity Making invisible visible Presentation Enhancing understanding of concepts and processes Visual medium of communication Debugging Quality control of simulations, measurements Others? Slide 23 Exploration Tasks Identify and distinguish objects Specialized Categorize objects Compare values Discover extrema (qualitative) Look up metric information (quantitative) Recognize pattern/structure Identify clusters Correlations between data sets What s going on here? General Slide 24
9 Presentation Tasks Effective presentation of significant features Attempt to convince Attract interest Slide 25 Example: to Convince Tufte, The Visual Display of Quantitative Information, p. 41. Slide 26 Slide 27
10 Consider Whole Visualization Interplay between techniques 3D color-mapped objects? Don t vary lightness in color scale Multiple variables displayed? Map to different perceptual channels Integrated vs. separate May separate in space (parallel presentation) Maybe in time (animation, user switches) Combine if you can effectively (shows correlation) Slide 28 Consider whole vis example 1 Slide 29 Consider whole vis example 2 Slide 30
11 Summary Data Characteristics For each technique, consider what dimensions and types of data it can support For each visualization, consider the best space to display it in Consider spatial frequency and missing values Visualization Goals Consider what tasks need to be done to achieve the visualization goals Consider what tasks are to be achieved, and which techniques are well suited for each Final consideration: Does this work? Slide 31 But How do we know which techniques are Well Suited? Learn a bit about how perception works Learn what techniques: Support different data types Support different tasks That s what we ll hear about in this course! Slide 32 Slide 33
12 The Dream System, part 1 Catalog of Visualizations: Classification of simple and complex visualization techniques [WEH90] Categorize each visualization technique by: what kind of data can be displayed ( attributes ): [scalar field, nominal, direction field, shape, position, spatially extend region or object, structure] what operations act on these attributes ( operations/judgments ). operations: [identify, locate, distinguish, categorize, cluster, distribution, rank, compare within and between relations, associate, correlate] Large 2-d matrix to identify meaningful visualization techniques for a pair of (attribute/operation). Slide 34 The Dream System, part 2 Assisted Visualization Toolkit looks up the best visualization from the new version of the above table Questions about the tasks drive selection from the table AI gives you the best visualization Chris Healey at NC State and others are working on this! They hope to have a system that makes a reasonable first pass Several others are working on this as well (see notes from Domik lecture in ACM course) Slide 35 The Current System We re not there yet with the dream system This course will present what is known I try to organize like the ideal table You are the I in place of AI Slide 36
13 Slide 37 Interviewing a Client First Goal: Determine what scientific question they are trying to answer What do they hope to learn from the visualization? What are they trying to do scientifically? Specific questions they want answers to! This guides the visualization design Second goal: Get a description and copy of data How it is collected, number of sets, type of each Lets you start trying to load into visualization code Slide 38 What makes a scientific question good? It describes a goal that the scientist has in understanding the data better Either in the scientist s domain language or in generic task language Not focused on possible techniques It is specific enough to guide selection of which technique is appropriate from a given set of techniques Slide 39
14 Interviewing: Example Scientific Questions Better questions Compare the surface predicted by our tumor detection algorithm to five MRI volume scalar fields, where does the algorithm overestimate and where does it underestimate? Understand the relationship between five hand-selected tumor surfaces drawn by different radiologists: where are they the same, and how different are they where they differ? Poorer questions Using volume rendering techniques to visualize tumor tissues (vague and focuses on the technique) Evaluating tumor location algorithms in 2D MRI images (vague) Use multiple-variable display techniques and Marching Squares algorithm to visualize areas with abnormal gray scale values in 2D MR slices (focuses on the techniques, not the questions) Slide 40 Interviewing: Potential Problems Learning the language Science they are doing (need to understand at least an overview) Keep asking questions until you understand Lots of strange nouns and acronyms (may only need to remember) Data, geometry, and tasks may be a common language Fear of non-shared goals They will likely worry that your goal is to provide pretty pictures, not aid their science Help allay these fears by your questions Make these fears unfounded by your actions this semester Slide 41 Interviewing: Potential Problems 2 They may have unreasonable expectations Too low Too high Different than visualization They may have ideas about techniques: listen, but don t treat as the end of the story They are smart people and know what they seek Out of their field, they will likely think incrementally This course will train you to explore the best-fit visualization Slide 42
15 Interview Example Interview between visualization expert and scientist studying multi-channel MRI Russ plays both parts Please forgive inaccuracies (I m not an MRI expert) Slide 43 Slide 44 References Foundation, Stages, Sensory vs. Arbitrary, 2-Stage Model: Ware. Goals, Data, Categorizations, Analysis: Gitta Domik. Problems vs. data types, data structure: David Ebert Exploration tasks, Consider Task, Consider Whole Visualization (and examples), Final Consideration: Penny Rheingans Slide 45
ACGV 2008, Lecture 1 Tuesday January 22, 2008
Advanced Computer Graphics and Visualization Spring 2008 Ch 1: Introduction Ch 4: The Visualization Pipeline Ch 5: Basic Data Representation Organization, Spring 2008 Stefan Seipel Filip Malmberg Mats
More informationWeek 7 Picturing Network. Vahe and Bethany
Week 7 Picturing Network Vahe and Bethany Freeman (2005) - Graphic Techniques for Exploring Social Network Data The two main goals of analyzing social network data are identification of cohesive groups
More informationVisualisation : Lecture 1. So what is visualisation? Visualisation
So what is visualisation? UG4 / M.Sc. Course 2006 toby.breckon@ed.ac.uk Computer Vision Lab. Institute for Perception, Action & Behaviour Introducing 1 Application of interactive 3D computer graphics to
More informationData Visualization (CIS/DSC 468)
Data Visualization (CIS/DSC 468) Data & Tasks Dr. David Koop Programmatic SVG Example Draw a horizontal bar chart - var a = [6, 2, 6, 10, 7, 18, 0, 17, 20, 6]; Steps: - Programmatically create SVG - Create
More informationWhich is better? Sentential. Diagrammatic Indexed by location in a plane
Jeanette Bautista Perceptual enhancement: text or diagrams? Why a Diagram is (Sometimes) Worth Ten Thousand Words Larkin, J. and Simon, H.A Structural object perception: 2D or 3D? Diagrams based on structural
More informationExample Videos. Administrative 1/26/2012. UNC-CH Comp/Phys/Mtsc 715. Vis 2006: ritter.avi. Vis2006: krueger.avi. Vis2011: Palke: ttg s.
UNC-CH Comp/Phys/Mtsc 715 2D Scalar: Color, Contour, Height Fields, (Glyphs), Textures, and Transparency 2D Visualization Comp/Phys/Mtsc 715 Taylor 1 Example Videos Vis 2006: ritter.avi Displaying vascular
More informationLecture overview. Visualisatie BMT. Goal. Summary (1) Summary (3) Summary (2) Goal Summary Study material
Visualisatie BMT Introduction, visualization, visualization pipeline Arjan Kok a.j.f.kok@tue.nl Lecture overview Goal Summary Study material What is visualization Examples Visualization pipeline 1 2 Goal
More informationData Representation in Visualisation
Data Representation in Visualisation Visualisation Lecture 4 Taku Komura Institute for Perception, Action & Behaviour School of Informatics Taku Komura Data Representation 1 Data Representation We have
More informationData+Dataset Types/Semantics Tasks
Data+Dataset Types/Semantics Tasks Visualization Michael Sedlmair Reading Munzner, Visualization Analysis and Design : Chapter 2+3 (Why+What+How) Shneiderman, The Eyes Have It: A Task by Data Type Taxonomy
More informationTo make sense of data, you can start by answering the following questions:
Taken from the Introductory Biology 1, 181 lab manual, Biological Sciences, Copyright NCSU (with appreciation to Dr. Miriam Ferzli--author of this appendix of the lab manual). Appendix : Understanding
More informationScientific Visualization Example exam questions with commented answers
Scientific Visualization Example exam questions with commented answers The theoretical part of this course is evaluated by means of a multiple- choice exam. The questions cover the material mentioned during
More information4. Basic Mapping Techniques
4. Basic Mapping Techniques Mapping from (filtered) data to renderable representation Most important part of visualization Possible visual representations: Position Size Orientation Shape Brightness Color
More informationData and Data Presentation
Chapter 1 Data and Data Presentation 1.1. Introduction A Statistician collects data (in an appropriate manner) analyses it using statistical techniques, interprets the results and makes conclusions and
More informationVisual Computing. Lecture 2 Visualization, Data, and Process
Visual Computing Lecture 2 Visualization, Data, and Process Pipeline 1 High Level Visualization Process 1. 2. 3. 4. 5. Data Modeling Data Selection Data to Visual Mappings Scene Parameter Settings (View
More informationWhat is Visualization? Introduction to Visualization. Why is Visualization Useful? Visualization Terminology. Visualization Terminology
What is Visualization? Introduction to Visualization Transformation of data or information into pictures Note this does not imply the use of computers Classical visualization used hand-drawn figures and
More informationLecture overview. Visualisatie BMT. Fundamental algorithms. Visualization pipeline. Structural classification - 1. Structural classification - 2
Visualisatie BMT Fundamental algorithms Arjan Kok a.j.f.kok@tue.nl Lecture overview Classification of algorithms Scalar algorithms Vector algorithms Tensor algorithms Modeling algorithms 1 2 Visualization
More informationExample Videos. Administrative 1/28/2014. UNC-CH Comp/Phys/Apsc 715. Vis 2006: ritter.avi. Vis2006: krueger.avi
UNC-CH Comp/Phys/Apsc 715 2D Scalar: Color, Contour, Height Fields, (Glyphs), Textures, and Transparency 2D Visualization Comp/Phys/Apsc 715 Taylor 1 Example Videos Vis 2006: ritter.avi Displaying vascular
More informationAdvanced Visualization
320581 Advanced Visualization Prof. Lars Linsen Fall 2011 0 Introduction 0.1 Syllabus and Organization Course Website Link in CampusNet: http://www.faculty.jacobsuniversity.de/llinsen/teaching/320581.htm
More informationScalar Visualization
Scalar Visualization Visualizing scalar data Popular scalar visualization techniques Color mapping Contouring Height plots outline Recap of Chap 4: Visualization Pipeline 1. Data Importing 2. Data Filtering
More informationScalar Algorithms: Contouring
Scalar Algorithms: Contouring Computer Animation and Visualisation Lecture tkomura@inf.ed.ac.uk Institute for Perception, Action & Behaviour School of Informatics Contouring Scaler Data Last Lecture...
More informationIntroduction to Scientific Visualization
Introduction to Scientific Visualization Aaron Birkland Cornell Center for Advanced Computing Data Analysis on Ranger January 2012 A lab-intensive workshop Start off with basic concepts Data, transformations,
More informationVisualization Computer Graphics I Lecture 20
15-462 Computer Graphics I Lecture 20 Visualization Height Fields and Contours Scalar Fields Volume Rendering Vector Fields [Angel Ch. 12] November 20, 2003 Doug James Carnegie Mellon University http://www.cs.cmu.edu/~djames/15-462/fall03
More informationDesign & Use of the Perceptual Rendering Intent for v4 Profiles
Design & Use of the Perceptual Rendering Intent for v4 Profiles Jack Holm Principal Color Scientist Hewlett Packard Company 19 March 2007 Chiba University Outline What is ICC v4 perceptual rendering? What
More informationVisualization Computer Graphics I Lecture 20
15-462 Computer Graphics I Lecture 20 Visualization Height Fields and Contours Scalar Fields Volume Rendering Vector Fields [Angel Ch. 12] April 15, 2003 Frank Pfenning Carnegie Mellon University http://www.cs.cmu.edu/~fp/courses/graphics/
More informationTNM093 Tillämpad visualisering och virtuell verklighet. Jimmy Johansson C-Research, Linköping University
TNM093 Tillämpad visualisering och virtuell verklighet Jimmy Johansson C-Research, Linköping University Introduction to Visualization New Oxford Dictionary of English, 1999 visualize - verb [with obj.]
More informationPreprocessing Short Lecture Notes cse352. Professor Anita Wasilewska
Preprocessing Short Lecture Notes cse352 Professor Anita Wasilewska Data Preprocessing Why preprocess the data? Data cleaning Data integration and transformation Data reduction Discretization and concept
More informationData Visualization (DSC 530/CIS )
Data Visualization (DSC 530/CIS 60-0) Isosurfaces & Volume Rendering Dr. David Koop Fields & Grids Fields: - Values come from a continuous domain, infinitely many values - Sampled at certain positions
More informationScientific Visualization
Scientific Visualization Dr. Ronald Peikert Summer 2007 Ronald Peikert SciVis 2007 - Introduction 1-1 Introduction to Scientific Visualization Ronald Peikert SciVis 2007 - Introduction 1-2 What is Scientific
More informationVisualization. Images are used to aid in understanding of data. Height Fields and Contours Scalar Fields Volume Rendering Vector Fields [chapter 26]
Visualization Images are used to aid in understanding of data Height Fields and Contours Scalar Fields Volume Rendering Vector Fields [chapter 26] Tumor SCI, Utah Scientific Visualization Visualize large
More informationInsight VisREU Site. Agenda. Introduction to Scientific Visualization Using 6/16/2015. The purpose of visualization is insight, not pictures.
2015 VisREU Site Introduction to Scientific Visualization Using Vetria L. Byrd, Director Advanced Visualization VisREU Site Coordinator REU Site Sponsored by NSF ACI Award 1359223 Introduction to SciVis(High
More informationMAT 155. Chapter 1 Introduction to Statistics. sample. population. parameter. statistic
MAT 155 Dr. Claude Moore Cape Fear Community College Chapter 1 Introduction to Statistics 1 1Review and Preview 1 2Statistical Thinking 1 3Types of Data 1 4Critical Thinking 1 5Collecting Sample Data Key
More informationHomework # 4. Example: Age in years. Answer: Discrete, quantitative, ratio. a) Year that an event happened, e.g., 1917, 1950, 2000.
Homework # 4 1. Attribute Types Classify the following attributes as binary, discrete, or continuous. Further classify the attributes as qualitative (nominal or ordinal) or quantitative (interval or ratio).
More information(Information) Visualization
(Information) Visualization CSC 511 Instructor: Melanie Tory First, a bit about me Human-computer interaction Psychology Computer Graphics Domain knowledge Data Visualization is Use of computer supported,
More informationGrundlagen methodischen Arbeitens Informationsvisualisierung [WS ] Monika Lanzenberger
Grundlagen methodischen Arbeitens Informationsvisualisierung [WS0708 01 ] Monika Lanzenberger lanzenberger@ifs.tuwien.ac.at 17. 10. 2007 Current InfoVis Research Activities: AlViz 2 [Lanzenberger et al.,
More informationData Visualization (DSC 530/CIS )
Data Visualization (DSC 530/CIS 60-01) Scalar Visualization Dr. David Koop Online JavaScript Resources http://learnjsdata.com/ Good coverage of data wrangling using JavaScript Fields in Visualization Scalar
More informationCP SC 8810 Data Visualization. Joshua Levine
CP SC 8810 Data Visualization Joshua Levine levinej@clemson.edu Lecture 15 Text and Sets Oct. 14, 2014 Agenda Lab 02 Grades! Lab 03 due in 1 week Lab 2 Summary Preferences on x-axis label separation 10
More informationCartographic symbolization
Symbology Cartographic symbolization Cartographic symbolization is based on a systematic approach for selecting the graphic symbols to use on a map Symbolization is the process of creating graphic symbols
More informationUlrik Söderström 17 Jan Image Processing. Introduction
Ulrik Söderström ulrik.soderstrom@tfe.umu.se 17 Jan 2017 Image Processing Introduction Image Processsing Typical goals: Improve images for human interpretation Image processing Processing of images for
More informationDSC 201: Data Analysis & Visualization
DSC 201: Data Analysis & Visualization Visualization Design Dr. David Koop Definition Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks
More informationIso-surface cell search. Iso-surface Cells. Efficient Searching. Efficient search methods. Efficient iso-surface cell search. Problem statement:
Iso-Contouring Advanced Issues Iso-surface cell search 1. Efficiently determining which cells to examine. 2. Using iso-contouring as a slicing mechanism 3. Iso-contouring in higher dimensions 4. Texturing
More informationStep 10 Visualisation Carlos Moura
Step 10 Visualisation Carlos Moura COIN 2017-15th JRC Annual Training on Composite Indicators & Scoreboards 06-08/11/2017, Ispra (IT) Effective communication through visualization Why investing on visual
More informationECLT 5810 Clustering
ECLT 5810 Clustering What is Cluster Analysis? Cluster: a collection of data objects Similar to one another within the same cluster Dissimilar to the objects in other clusters Cluster analysis Grouping
More informationComputer Graphics and Visualization. What is computer graphics?
CSCI 120 Computer Graphics and Visualization Shiaofen Fang Department of Computer and Information Science Indiana University Purdue University Indianapolis What is computer graphics? Computer graphics
More informationScientific Visualization
Scientific Visualization Topics Motivation Color InfoVis vs. SciVis VisTrails Core Techniques Advanced Techniques 1 Check Assumptions: Why Visualize? Problem: How do you apprehend 100k tuples? when your
More informationLecture 13 Theory of Registration. ch. 10 of Insight into Images edited by Terry Yoo, et al. Spring (CMU RI) : BioE 2630 (Pitt)
Lecture 13 Theory of Registration ch. 10 of Insight into Images edited by Terry Yoo, et al. Spring 2018 16-725 (CMU RI) : BioE 2630 (Pitt) Dr. John Galeotti The content of these slides by John Galeotti,
More informationOrganisation and Presentation of Data in Medical Research Dr K Saji.MD(Hom)
Organisation and Presentation of Data in Medical Research Dr K Saji.MD(Hom) Any data collected by a research or reference also known as raw data are always in an unorganized form and need to be organized
More informationVisualisierung W, VU, 2.0h, 3.0EC
Visualisierung 1 2014W, VU, 2.0h, 3.0EC 186.827 Eduard Gröller Johanna Schmidt Oana Moraru Institute of Computer Graphics and Algorithms (ICGA), VUT Austria Visualization Examples VolVis InfoVis FlowVis
More informationHeight Fields and Contours Scalar Fields Volume Rendering Vector Fields [Angel Ch. 12] April 23, 2002 Frank Pfenning Carnegie Mellon University
15-462 Computer Graphics I Lecture 21 Visualization Height Fields and Contours Scalar Fields Volume Rendering Vector Fields [Angel Ch. 12] April 23, 2002 Frank Pfenning Carnegie Mellon University http://www.cs.cmu.edu/~fp/courses/graphics/
More informationClipping. CSC 7443: Scientific Information Visualization
Clipping Clipping to See Inside Obscuring critical information contained in a volume data Contour displays show only exterior visible surfaces Isosurfaces can hide other isosurfaces Other displays can
More information11/1/13. Visualization. Scientific Visualization. Types of Data. Height Field. Contour Curves. Meshes
CSCI 420 Computer Graphics Lecture 26 Visualization Height Fields and Contours Scalar Fields Volume Rendering Vector Fields [Angel Ch. 2.11] Jernej Barbic University of Southern California Scientific Visualization
More informationVisualization. CSCI 420 Computer Graphics Lecture 26
CSCI 420 Computer Graphics Lecture 26 Visualization Height Fields and Contours Scalar Fields Volume Rendering Vector Fields [Angel Ch. 11] Jernej Barbic University of Southern California 1 Scientific Visualization
More informationLecture 3: Linear Classification
Lecture 3: Linear Classification Roger Grosse 1 Introduction Last week, we saw an example of a learning task called regression. There, the goal was to predict a scalar-valued target from a set of features.
More informationWhat is visualization? Why is it important?
What is visualization? Why is it important? What does visualization do? What is the difference between scientific data and information data Cycle of Visualization Storage De noising/filtering Down sampling
More informationChapter 1 Introduction to Statistics
Corresponds to ELEMENTARY STATISTICS USING THE TI 83/84 PLUS CALCULATOR 3rd ed. Lecture Slides Elementary Statistics Eleventh Edition and the Triola Statistics Series by Mario F. Triola Chapter 1 Introduction
More informationShadows in the graphics pipeline
Shadows in the graphics pipeline Steve Marschner Cornell University CS 569 Spring 2008, 19 February There are a number of visual cues that help let the viewer know about the 3D relationships between objects
More informationChapter 3. Uncertainty and Vagueness. (c) 2008 Prof. Dr. Michael M. Richter, Universität Kaiserslautern
Chapter 3 Uncertainty and Vagueness Motivation In most images the objects are not precisely defined, e.g. Landscapes, Medical images etc. There are different aspects of uncertainty involved that need to
More informationCIS 467/602-01: Data Visualization
CIS 467/60-01: Data Visualization Isosurfacing and Volume Rendering Dr. David Koop Fields and Grids Fields: values come from a continuous domain, infinitely many values - Sampled at certain positions to
More informationCS513-Data Mining. Lecture 2: Understanding the Data. Waheed Noor
CS513-Data Mining Lecture 2: Understanding the Data Waheed Noor Computer Science and Information Technology, University of Balochistan, Quetta, Pakistan Waheed Noor (CS&IT, UoB, Quetta) CS513-Data Mining
More informationDEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF TORONTO CSC318S THE DESIGN OF INTERACTIVE COMPUTATIONAL MEDIA. Lecture March 1998
DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF TORONTO CSC318S THE DESIGN OF INTERACTIVE COMPUTATIONAL MEDIA Lecture 19 30 March 1998 PRINCIPLES OF DATA DISPLAY AND VISUALIZATION 19.1 Nature, purpose of
More informationDATA ABSTRACTION & INTRO TO TABLEAU
cs6630 September 4 2014 DATA ABSTRACTION & INTRO TO TABLEAU Miriah Meyer University of Utah 1 administrivia... 2 - design critiques due tonight - first assignment out today - there *might* be 3 seats available
More informationCSE528 Computer Graphics: Theory, Algorithms, and Applications
CSE528 Computer Graphics: Theory, Algorithms, and Applications Hong Qin State University of New York at Stony Brook (Stony Brook University) Stony Brook, New York 11794--4400 Tel: (631)632-8450; Fax: (631)632-8334
More informationNorbert Schuff VA Medical Center and UCSF
Norbert Schuff Medical Center and UCSF Norbert.schuff@ucsf.edu Medical Imaging Informatics N.Schuff Course # 170.03 Slide 1/67 Objective Learn the principle segmentation techniques Understand the role
More informationINTRODUCTORY SPSS. Dr Feroz Mahomed Swalaha x2689
INTRODUCTORY SPSS Dr Feroz Mahomed Swalaha fswalaha@dut.ac.za x2689 1 Statistics (the systematic collection and display of numerical data) is the most abused area of numeracy. 97% of statistics are made
More informationVolume Illumination, Contouring
Volume Illumination, Contouring Computer Animation and Visualisation Lecture 0 tkomura@inf.ed.ac.uk Institute for Perception, Action & Behaviour School of Informatics Contouring Scaler Data Overview -
More informationWho has worked on a voxel engine before? Who wants to? My goal is to give the talk I wish I would have had before I started on our procedural engine.
1 Who has worked on a voxel engine before? Who wants to? My goal is to give the talk I wish I would have had before I started on our procedural engine. Three parts to this talk. A lot of content, so I
More informationSTAT STATISTICAL METHODS. Statistics: The science of using data to make decisions and draw conclusions
STAT 515 --- STATISTICAL METHODS Statistics: The science of using data to make decisions and draw conclusions Two branches: Descriptive Statistics: The collection and presentation (through graphical and
More informationECLT 5810 Clustering
ECLT 5810 Clustering What is Cluster Analysis? Cluster: a collection of data objects Similar to one another within the same cluster Dissimilar to the objects in other clusters Cluster analysis Grouping
More informationVisualization? Information Visualization. Information Visualization? Ceci n est pas une visualization! So why two disciplines? So why two disciplines?
Visualization? New Oxford Dictionary of English, 1999 Information Visualization Matt Cooper visualize - verb [with obj.] 1. form a mental image of; imagine: it is not easy to visualize the future. 2. make
More informationVisualization with ParaView
Visualization with Before we begin Make sure you have 3.10.1 installed so you can follow along in the lab section http://paraview.org/paraview/resources/software.html http://www.paraview.org/ Background
More informationScalar Visualization
Scalar Visualization 5-1 Motivation Visualizing scalar data is frequently encountered in science, engineering, and medicine, but also in daily life. Recalling from earlier, scalar datasets, or scalar fields,
More informationScalable and Distributed Visualization using ParaView
Scalable and Distributed Visualization using ParaView Eric A. Wernert, Ph.D. Senior Manager & Scientist, Advanced Visualization Lab Pervasive Technology Institute, Indiana University Big Data for Science
More informationUnderstanding Geospatial Data Models
Understanding Geospatial Data Models 1 A geospatial data model is a formal means of representing spatially referenced information. It is a simplified view of physical entities and a conceptualization of
More informationChapter 9 Object Tracking an Overview
Chapter 9 Object Tracking an Overview The output of the background subtraction algorithm, described in the previous chapter, is a classification (segmentation) of pixels into foreground pixels (those belonging
More informationScope and Sequence for the New Jersey Core Curriculum Content Standards
Scope and Sequence for the New Jersey Core Curriculum Content Standards The following chart provides an overview of where within Prentice Hall Course 3 Mathematics each of the Cumulative Progress Indicators
More informationTest Metrics CzechTest 2014 Presentation
Test Metrics CzechTest 2014 Presentation Bogdan Bereza, Victo Test Metrics CzechTest 2014 Presentation 1 (45) Pozdrawiam Best regards Med vänliga hälsningar Bogdan Bereza bogdan.bereza@victo.eu +48 519
More informationVisual Perception. Basics
Visual Perception Basics Please refer to Colin Ware s s Book Some materials are from Profs. Colin Ware, University of New Hampshire Klaus Mueller, SUNY Stony Brook Jürgen Döllner, University of Potsdam
More informationPredict Outcomes and Reveal Relationships in Categorical Data
PASW Categories 18 Specifications Predict Outcomes and Reveal Relationships in Categorical Data Unleash the full potential of your data through predictive analysis, statistical learning, perceptual mapping,
More informationVolume Illumination & Vector Field Visualisation
Volume Illumination & Vector Field Visualisation Visualisation Lecture 11 Institute for Perception, Action & Behaviour School of Informatics Volume Illumination & Vector Vis. 1 Previously : Volume Rendering
More informationCS 584 Data Mining. Classification 1
CS 584 Data Mining Classification 1 Classification: Definition Given a collection of records (training set ) Each record contains a set of attributes, one of the attributes is the class. Find a model for
More informationMachine Learning - Lecture 2: Nearest-neighbour methods
Machine Learning - Lecture 2: Nearest-neighbour methods Chris Thornton January 8, 22 Brighton pier Switchback Data Data Vertical axis is age; horizontal axis is alchohol consumption per week. Data Vertical
More informationA Nested Model for Visualization. Tamara Munzner University of British Columbia Department of Computer Science. Design and Validation
A Nested Model for Visualization Tamara Munzner University of British Columbia Department of Computer Science Design and Validation How do you show your system is good? so many possible ways! algorithm
More informationInformation Retrieval. CS630 Representing and Accessing Digital Information. What is a Retrieval Model? Basic IR Processes
CS630 Representing and Accessing Digital Information Information Retrieval: Retrieval Models Information Retrieval Basics Data Structures and Access Indexing and Preprocessing Retrieval Models Thorsten
More informationNote 8: Visual Interface Design
Computer Science and Software Engineering University of Wisconsin - Platteville Note 8: Visual Interface Design Yan Shi Lecture Notes for SE 3330 UW-Platteville Based on About Face 3: Chapter 14 Visual
More informationData Visualization. Fall 2016
Data Visualization Fall 2016 Information Visualization Upon now, we dealt with scientific visualization (scivis) Scivisincludes visualization of physical simulations, engineering, medical imaging, Earth
More informationCP SC 8810 Data Visualization. Joshua Levine
CP SC 8810 Data Visualization Joshua Levine levinej@clemson.edu Lecture 05 Visual Encoding Sept. 9, 2014 Agenda Programming Lab 01 Questions? Continuing from Lec04 Attribute Types no implicit ordering
More informationGlyphs. Presentation Overview. What is a Glyph!? Cont. What is a Glyph!? Glyph Fundamentals. Goal of Paper. Presented by Bertrand Low
Presentation Overview Glyphs Presented by Bertrand Low A Taxonomy of Glyph Placement Strategies for Multidimensional Data Visualization Matthew O. Ward, Information Visualization Journal, Palmgrave,, Volume
More informationLast Time: Data and Image Models
CS448B :: 2 Oct 2012 Visualization Design Last Time: Data and Image Models Jeffrey Heer Stanford University The Big Picture Nominal, Ordinal and Quantitative task questions & hypotheses intended audience
More informationStatistical graphics in analysis Multivariable data in PCP & scatter plot matrix. Paula Ahonen-Rainio Maa Visual Analysis in GIS
Statistical graphics in analysis Multivariable data in PCP & scatter plot matrix Paula Ahonen-Rainio Maa-123.3530 Visual Analysis in GIS 11.11.2015 Topics today YOUR REPORTS OF A-2 Thematic maps with charts
More informationData Visualization. What is the goal? A generalized environment for manipulation and visualization of multidimensional data
Data Visualization NIH-NSF NSF BBSI: Simulation and Computer Visualization of Biological Systems at Multiple Scales June 2-4, 2 2004 Joel R. Stiles, MD, PhD What is the goal? A generalized environment
More informationApplying Supervised Learning
Applying Supervised Learning When to Consider Supervised Learning A supervised learning algorithm takes a known set of input data (the training set) and known responses to the data (output), and trains
More informationApproaches to Visual Mappings
Approaches to Visual Mappings CMPT 467/767 Visualization Torsten Möller Weiskopf/Machiraju/Möller Overview Effectiveness of mappings Mapping to positional quantities Mapping to shape Mapping to color Mapping
More informationPlanar Graphs and Surfaces. Graphs 2 1/58
Planar Graphs and Surfaces Graphs 2 1/58 Last time we discussed the Four Color Theorem, which says that any map can be colored with at most 4 colors and not have two regions that share a border having
More informationThe basic arrangement of numeric data is called an ARRAY. Array is the derived data from fundamental data Example :- To store marks of 50 student
Organizing data Learning Outcome 1. make an array 2. divide the array into class intervals 3. describe the characteristics of a table 4. construct a frequency distribution table 5. constructing a composite
More informationThe capability of traditional presentation techniques is not sufficient for the increasing amount of data to be interpreted
2. Basics Data sources Visualization pipeline Data representation Domain Data structures Data values Data classification 1 2.1. Data Sources The capability of traditional presentation techniques is not
More informationScalar Data. Visualization Torsten Möller. Weiskopf/Machiraju/Möller
Scalar Data Visualization Torsten Möller Weiskopf/Machiraju/Möller Overview Basic strategies Function plots and height fields Isolines Color coding Volume visualization (overview) Classification Segmentation
More information3. Visual Analytics (Part 1: Visual Encoding) Jacobs University Visualization and Computer Graphics Lab
3. Visual Analytics (Part 1: Visual Encoding) 3.1 Introduction Motivation Big Data cannot be analyzed anymore without the help of computers. Computers are good in quickly processing large amounts of data.
More informationData Visualization (CIS 468)
Data Visualization (CIS 468) D3 + Marks & Channels Dr. David Koop Tasks Actions Targets Analyze All Data Consume Trends Outliers Features Discover Present Enjoy Produce Annotate Record Derive tag Attributes
More informationMachine Learning Practice and Theory
Machine Learning Practice and Theory Day 9 - Feature Extraction Govind Gopakumar IIT Kanpur 1 Prelude 2 Announcements Programming Tutorial on Ensemble methods, PCA up Lecture slides for usage of Neural
More informationComputer Graphics. - Volume Rendering - Philipp Slusallek
Computer Graphics - Volume Rendering - Philipp Slusallek Overview Motivation Volume Representation Indirect Volume Rendering Volume Classification Direct Volume Rendering Applications: Bioinformatics Image
More informationScalar Data. Alark Joshi
Scalar Data Alark Joshi Announcements Pick two papers to present Email me your top 3/4 choices. FIFO allotment Contact your clients Blog summaries: http://cs.boisestate.edu/~alark/cs564/participants.html
More information