Advanced Visualization

Transcription

1 Advanced Visualization Prof. Lars Linsen Fall 2011

2 0 Introduction

3 0.1 Syllabus and Organization

4 Course Website Link in CampusNet: : Advanced Visualization 4

5 Content Scientific visualization deals with the visualization of data with a natural spatial interpretation such as computer-generated data from numerical simulations (physics, chemistry) or measured data using scanning or sensor techniques (medicine, life sciences, geosciences). Volume visualization methods such as segmentation, surface extraction, and direct volume rendering for structured and unstructured gridded as well as scattered data are being taught. These include techniques for scalar field, vector field, and tensor field visualization. Information visualization deals with the visualization of abstract data with no spatial interpretation such as graph- or network-based data (life sciences, social sciences, computer networks) or multidimensional data (economics, databases). Methods that tackle these visualization problems are being taught. The course deepens, broadens, and enhances the knowledge in visualization obtained from the undergraduate course on "Graphics and Visualization" in terms of visualization methods : Advanced Visualization 5

6 Prerequisites None Corequisites Advanced Visualization Lab : Advanced Visualization 6

7 Lectures Times: Monday, 3:45pm 5:00pm Tuesday, 3:45pm 5:00pm. Location: West Hall : Advanced Visualization 7

8 Instructor Lars Linsen Office: Res I, 128. Phone: l.linsen Office hours: by appointment : Advanced Visualization 8

9 Assignments There will be no assignments : Advanced Visualization 9

10 Exams There will be a midterm and a final examination. It is planned to have a written midterm and an oral final examination : Advanced Visualization 10

11 Grading The midterm exam will contribute 33% and the final exam 67% to the overall grade : Advanced Visualization 11

12 Dates Lectures (1) Lecture 1 - September 05, 2011 Lecture 2 - September 06, 2011 Lecture 3 - September 12, 2011 Lecture 4 - September 13, 2011 Lecture 5 - September 19, 2011 Lecture 6 - September 20, 2011 Lecture 7 - September 26, 2011 Lecture 8 - September 27, 2011 No Lecture - October 03, 2011 Holiday No Lecture - October 04, 2011 Off campus Lecture 9 - October 10, 2011 Lecture 10 - October 11, 2011 No Lecture - October 17, 2011 Reading Days No Lecture - October 18, 2011 Reading Days : Advanced Visualization 12

13 Dates Lectures (2) No Lecture - October 24, 2011 Off campus Midterm - October 25, 2011 Midterm Lecture 12 - October 31, 2011 Lecture 13 - November 01, 2011 Lecture 14 - November 07, 2011 Lecture 15 - November 08, 2011 Lecture 16 - November 14, 2011 Lecture 17 - November 15, 2011 Lecture 18 - November 21, 2011 Lecture 19 - November 22, 2011 Lecture 20 - November 28, 2011 Lecture 21 - November 29, 2011 Lecture 22 - December 05, 2011 Lecture 23 - December 06, : Advanced Visualization 13

14 Dates - Exams Midterm: October 25, 2009 Final: tbd (finals week) : Advanced Visualization 14

15 Lab Link in CampusNet: Times: tbd Location: tbd : Advanced Visualization 15

16 Lab Assignments: There will be 6 project assignments. The six assignments are handed out on a biweekly basis. Solutions that are handed in late lead to reduced credit (-15% per day). Exceptions are only made with an official excuse : Advanced Visualization 16

17 Lab Exams: There will be no exams Grading: The grade is to 100% based on the projects : Advanced Visualization 17

18 Lab Dates: Project 1: Project 2: Project 3: Project 4: Project 5: Handed out: Handed out: Handed out: Handed out: Handed out: Due: Due: Due: Due: Due: : Advanced Visualization 18

19 Literature Alexandru Telea: Data Visualization: Principles and Practice, Wellesley, Mass.: AK Peters, 1st edition, Dave Shreiner, Mason Woo, and Jackie Neider: OpenGL Programming Guide: The Official Guide to Learning OpenGL, Addison-Wesley Longman, 3rd edition, (Old version available at : Advanced Visualization 19

20 Announcements Master thesis topics will be available soon: : Advanced Visualization 20

21 0.2 Goals

22 Goals of this course Advanced concepts and techniques for data visualization and interactive visual data exploration : Advanced Visualization 22

23 Definition of Visualization Creating images that convey salient information about underlying data and processes NSF report: a method for seeing the unseen new scientific insight through visual methods : Advanced Visualization 23

24 Design Goals 1. Extract salient information (features) 2. Intuitive presentation (display) of extracted information 3. Interactive modification of presentation viewing parameters displaying parameters exploration/extraction parameters : Advanced Visualization 24

25 Goals of Visualization Obtain insight into the given data: 1. Answering specific questions: quantitative: What are the data values and their distribution? qualitative: Is this feature occurring in the data? 2. Discovering the unknown: What is in the data set? : Advanced Visualization 25

26 Visualization Process : Advanced Visualization 26

27 0.3 Application Examples

28 Medicine Imaging techniques using scanners: CT, MRI, : Advanced Visualization 28

29 Sensors Sensors measuring environment: : Advanced Visualization 29

30 Natural Sciences Numerically computed data in Physics, Chemistry, and Life Sciences: : Advanced Visualization 30

31 Economics Stock data (including statistics): : Advanced Visualization 31

32 Hierarchical data: file system company structure Every-day Life : Advanced Visualization 32

33 0.4 Data Representation

34 Observation The data describe a function f: D -> R with domain D and range R = f(d). They are sampled at discrete positions x є D, i.e., only a finite number of values f(x) of function f is known. The sample positions may exhibit a structure : Advanced Visualization 34

35 Range R Scalar field: R c R Vector field: R c R m, m > 1 (special case: flow field, where m = D ) Tensor field: R c R mxn, m,n > 1 f is described by a matrix Multifield: R c R x R x R 3 x f is a combination of multiple fields : Advanced Visualization 35

36 Density field in CT scan: Scalar Field : Advanced Visualization 36

37 Scalar Field Temperature field in physical simulation: : Advanced Visualization 37

38 Flow Field Wind field of hurricane data set: : Advanced Visualization 38

39 Tensor Field Diffusion tensor field in human brain: : Advanced Visualization 39

40 Domain D D c R: simple; visualization reduces to a 2D plot. D c R 2 : simple when dealing with scalar data (3D plot), but gets more complex when considering highdimensional ranges. D c R 3 : most excessively researched area in visualization; also referred to as volume data. Human s perception is restricted to 3D visual spaces. Occlusion is a major challenge. D c R 4 : 4th component is typically time; i.e., time-varying volume data : Advanced Visualization 40

41 Domain D D c R m, m>3: Multi-dimensional data. This is not common for scanned or simulated data, as usually spatial phenomena with a 3D physical space are of interest. However, it is common when considering data that is not embedded in a physical space. Abstract (or non-spatial) data: Samples have no assigned spatial attributes but consist of records (x 1,,x m ), i.e., samples are points in an m-dimensional space. Remark: The values x m do not have to be real values and can even be categorical (like male/female or blond/brown/black/red) : Advanced Visualization 41

42 Sample Structure (Spatial Data) Sample locations x є D may be connected to form grids. In a grid, the sample locations are represented by vertices, their connection are represented by edges, a minimum loop of edges forms a face, and a minimum volume enclosed by faces form a cell : Advanced Visualization 42

43 Face Types : Advanced Visualization 43

44 Cell Types : Advanced Visualization 44

45 Uniform Grid In a uniform grid, all vertices have same valence (excluding boundary vertices), all edges have same length, and the angles between connected edges have same value : Advanced Visualization 45

46 Rectilinear Grid In a rectilinear grid, all vertices have valence 6 (excluding boundary vertices) and the angles between connected edges are right : Advanced Visualization 46

47 In a structured grid, Structured Grid all vertices have valence 6 (excluding boundary vertices). Such a grid is also referred to as curvilinear : Advanced Visualization 47

48 Properties of Structures For a uniform grid, location and connectivity of all vertices are implicitly known (when knowing the bounding box and number of cells per dimension). Data over a uniform grid can be stored as a 3D array with entries out of range R. For a structured (non-uniform) grid, only connectivity of vertices is implicitly known. In addition to the data values, we need to store the sample locations x є D : Advanced Visualization 48

49 Properties of Structures For an unstructured grid, we also need to store connectivity information for each vertex (cf. triangular mesh) : Advanced Visualization 49

50 Scattered Data If there is no grid that connects data samples, the data is unstructured or scattered : Advanced Visualization 50

51 Sample Structure (Abstract Data) In abstract data, samples may have relations. Relations are often pairwise. Sometimes the relations build a hierarchical structure : Advanced Visualization 51

52 Scientific and Information Visualization Visualization methods that cope with spatial data are often referred to as Scientific Visualization. Visualization methods that cope with abstract data are often referred to as Information Visualization : Advanced Visualization 52

53 0.5 Outline

54 Outline of this Course I. Scalar Field Visualization 1. Cutting Planes 2. Surface Extraction 3. Direct Volume Rendering II. Flow & Tensor Field Visualization 4. Geometric Flow Visualization 5. Texture-based Flow Visualization 6. Vector Field Topology 7. Diffusion Tensor Visualization III. Information Visualization 8. Visualization of Relations 9. Multidimensional Data Interaction Mechanisms : Advanced Visualization 54

55 Virtual Reality Virtual environments use stereoscopic viewing to support 3D visualization, e.g., in a CAVE: : Advanced Visualization 55

56 Virtual Reality : Advanced Visualization 56

57 Virtual Reality Virtual environments are often immersive. Virtual Reality devices will not be part of this course : Advanced Visualization 57

58 Human Computer Interaction Input Devices: : Advanced Visualization 58

59 Human Computer Interaction Graphical User Interface: : Advanced Visualization 59