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 320581: Advanced Visualization 4
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. 320581: Advanced Visualization 5
Prerequisites None Corequisites 320621 Advanced Visualization Lab 320581: Advanced Visualization 6
Lectures Times: Monday, 3:45pm 5:00pm Tuesday, 3:45pm 5:00pm. Location: West Hall 3 320581: Advanced Visualization 7
Instructor Lars Linsen Office: Res I, 128. Phone: 3196 E-Mail: l.linsen [@jacobs-university.de] Office hours: by appointment 320581: Advanced Visualization 8
Assignments There will be no assignments. 320581: Advanced Visualization 9
Exams There will be a midterm and a final examination. It is planned to have a written midterm and an oral final examination. 320581: Advanced Visualization 10
Grading The midterm exam will contribute 33% and the final exam 67% to the overall grade. 320581: Advanced Visualization 11
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 320581: Advanced Visualization 12
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, 2011 320581: Advanced Visualization 13
Dates - Exams Midterm: October 25, 2009 Final: tbd (finals week) 320581: Advanced Visualization 14
Lab Link in CampusNet: http://www.faculty.jacobsuniversity.de/llinsen/teaching/320621.htm Times: tbd Location: tbd 320581: Advanced Visualization 15
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. 320581: Advanced Visualization 16
Lab Exams: There will be no exams Grading: The grade is to 100% based on the projects. 320581: Advanced Visualization 17
Lab Dates: Project 1: Project 2: Project 3: Project 4: Project 5: Handed out: 09.09.2011 Handed out: 23.09.2011 Handed out: 14.10.2011 Handed out: 04.11.2011 Handed out: 18.11.2011 Due: 23.09.2011 Due: 14.10.2011 Due: 04.11.2011 Due: 18.11.2011 Due: 02.12.2011 320581: Advanced Visualization 18
Literature Alexandru Telea: Data Visualization: Principles and Practice, Wellesley, Mass.: AK Peters, 1st edition, 2008. Dave Shreiner, Mason Woo, and Jackie Neider: OpenGL Programming Guide: The Official Guide to Learning OpenGL, Addison-Wesley Longman, 3rd edition, 2006. (Old version available at http://www.glprogramming.com/red.) 320581: Advanced Visualization 19
Announcements Master thesis topics will be available soon: http://www.faculty.jacobsuniversity.de/llinsen/teaching/320371+320501.htm 320581: Advanced Visualization 20
0.2 Goals
Goals of this course Advanced concepts and techniques for data visualization and interactive visual data exploration. 320581: Advanced Visualization 22
Definition of Visualization Creating images that convey salient information about underlying data and processes. 1987 NSF report: a method for seeing the unseen new scientific insight through visual methods 320581: Advanced Visualization 23
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 320581: Advanced Visualization 24
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? 320581: Advanced Visualization 25
Visualization Process 320581: Advanced Visualization 26
0.3 Application Examples
Medicine Imaging techniques using scanners: CT, MRI, 320581: Advanced Visualization 28
Sensors Sensors measuring environment: 320581: Advanced Visualization 29
Natural Sciences Numerically computed data in Physics, Chemistry, and Life Sciences: 320581: Advanced Visualization 30
Economics Stock data (including statistics): 320581: Advanced Visualization 31
Hierarchical data: file system company structure Every-day Life 320581: Advanced Visualization 32
0.4 Data Representation
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. 320581: Advanced Visualization 34
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 320581: Advanced Visualization 35
Density field in CT scan: Scalar Field 320581: Advanced Visualization 36
Scalar Field Temperature field in physical simulation: 320581: Advanced Visualization 37
Flow Field Wind field of hurricane data set: 320581: Advanced Visualization 38
Tensor Field Diffusion tensor field in human brain: 320581: Advanced Visualization 39
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. 320581: Advanced Visualization 40
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). 320581: Advanced Visualization 41
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. 320581: Advanced Visualization 42
Face Types 320581: Advanced Visualization 43
Cell Types 320581: Advanced Visualization 44
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. 320581: Advanced Visualization 45
Rectilinear Grid In a rectilinear grid, all vertices have valence 6 (excluding boundary vertices) and the angles between connected edges are right. 320581: Advanced Visualization 46
In a structured grid, Structured Grid all vertices have valence 6 (excluding boundary vertices). Such a grid is also referred to as curvilinear. 320581: Advanced Visualization 47
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. 320581: Advanced Visualization 48
Properties of Structures For an unstructured grid, we also need to store connectivity information for each vertex (cf. triangular mesh). 320581: Advanced Visualization 49
Scattered Data If there is no grid that connects data samples, the data is unstructured or scattered. 320581: Advanced Visualization 50
Sample Structure (Abstract Data) In abstract data, samples may have relations. Relations are often pairwise. Sometimes the relations build a hierarchical structure. 320581: Advanced Visualization 51
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. 320581: Advanced Visualization 52
0.5 Outline
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 320581: Advanced Visualization 54
Virtual Reality Virtual environments use stereoscopic viewing to support 3D visualization, e.g., in a CAVE: 320581: Advanced Visualization 55
Virtual Reality 320581: Advanced Visualization 56
Virtual Reality Virtual environments are often immersive. Virtual Reality devices will not be part of this course. 320581: Advanced Visualization 57
Human Computer Interaction Input Devices: 320581: Advanced Visualization 58
Human Computer Interaction Graphical User Interface: 320581: Advanced Visualization 59