Information Visualization Toolkits Workshop 2004 Sheelagh Carpendale Department of Computer Science, University of Calgary, Alberta, Canada Sheelagh Carpendale.

Slides:



Advertisements
Similar presentations
Gestures Recognition. Image acquisition Image acquisition at BBC R&D studios in London using eight different viewpoints. Sequence frame-by-frame segmentation.
Advertisements

Shape grammar implementation with vision Iestyn Jowers University of Leeds Design Computing and Cognition 2010 Workshop on Shape Grammar Implementation.
Topics in learning from high dimensional data and large scale machine learning Ata Kaban School of Computer Science University of Birmingham.
M. Belkin and P. Niyogi, Neural Computation, pp. 1373–1396, 2003.
Chapter 30 Lenses. Lens – a lens is a transparent material that bends light rays depending on its shape Converging lens – a lens (top left) in which light.
Structure-Based Distance Metric for High-Dimensional Space Exploration with Multi-Dimensional Scaling Jenny Hyunjung Lee , Kevin T. McDonnell, Alla Zelenyuk.
Robot Vision SS 2005 Matthias Rüther 1 ROBOT VISION Lesson 3: Projective Geometry Matthias Rüther Slides courtesy of Marc Pollefeys Department of Computer.
Image Segmentation some examples Zhiqiang wang
CPSC 335 Geometric Data Structures in Computer Modeling and GIS Dr. Marina L. Gavrilova Assistant Professor Dept of Comp. Science, University of Calgary,
One-Shot Multi-Set Non-rigid Feature-Spatial Matching
DIMENSIONALITY REDUCTION BY RANDOM PROJECTION AND LATENT SEMANTIC INDEXING Jessica Lin and Dimitrios Gunopulos Ângelo Cardoso IST/UTL December
A Review and Taxonomy of Distortion-Oriented Presentation Techniques Y.K Leung and M.D. Apperley Presentation by Sean Lynch.
Dimensionality Reduction and Embeddings
“Occlusion” Prepared by: Shreya Rawal 1. Extending Distortion Viewing from 2D to 3D S. Carpendale, D. J. Cowperthwaite and F. David Fracchia (1997) 2.
Topology Matching For Fully Automatic Similarity Matching of 3D Shapes Masaki Hilaga Yoshihisa Shinagawa Taku Kohmura Tosiyasu L. Kunii.
Vector Space Information Retrieval Using Concept Projection Presented by Zhiguo Li
OBBTree: A Hierarchical Structure for Rapid Interference Detection Gottschalk, M. C. Lin and D. ManochaM. C. LinD. Manocha Department of Computer Science,
Uncertainty Quantification and Visualization: Geo-Spatially Registered Terrains and Mobile Targets Suresh Lodha Computer Science, University of California,
An Exploratory Study of Visual Information Analysis Petra Isenberg Dept. of Computer Science University of Calgary Calgary, AB, Canada
Multiple View Geometry Marc Pollefeys University of North Carolina at Chapel Hill Modified by Philippos Mordohai.
Lecture 2: Geometry vs Linear Algebra Points-Vectors and Distance-Norm Shang-Hua Teng.
CSE554SimplificationSlide 1 CSE 554 Lecture 7: Simplification Fall 2014.
Department of Biomedical Informatics Biomedical Data Visualization Kun Huang Department of Biomedical Informatics OSUCCC Biomedical Informatics Shared.
Vectors Tools for Graphics.  To review vector arithmetic, and to relate vectors to objects of interest in graphics.  To relate geometric concepts to.
Statistical analysis of pore space geometry Stefano Favretto Supervisor : Prof. Martin Blunt Petroleum Engineering and Rock Mechanics Research Group Department.
Spatial Concepts Mathematical Types of Space –Euclidean –Network –Metric –Topologic.
University of Texas at Austin CS384G - Computer Graphics Fall 2008 Don Fussell Orthogonal Functions and Fourier Series.
Careers in Computer Science What is computer science? Who should major in computer science? What do computer scientists really do? Research Paper Alice.
Introduction to Computational Geometry Hackson
AN ORTHOGONAL PROJECTION
A Generalization of PCA to the Exponential Family Collins, Dasgupta and Schapire Presented by Guy Lebanon.
Digital Image Processing CCS331 Relationships of Pixel 1.
Spatial & Terrain Analysis Nigel Trodd Coventry University in 3D.
CSE554SimplificationSlide 1 CSE 554 Lecture 7: Simplification Fall 2013.
Evaluation of Agent Building Tools and Implementation of a Prototype for Information Gathering Leif M. Koch University of Waterloo August 2001.
Knowledge Systems Lab JN 12/2/2015 Pliable Display Technology: Contextual Zoom as a Learning System Interface Joshua R. New Special Topics Jacksonville.
Non-Linear Dimensionality Reduction
Algorithms 2005 Ramesh Hariharan. Algebraic Methods.
The InfoVis Toolkit Jean-Daniel Fekete INRIA Futurs/LRI, France
Unit 5 Shap Description (Orthographic Projection) نظرية الأسقاط.
1 Angel: Interactive Computer Graphics 4E © Addison-Wesley 2005 Classical Viewing Ed Angel Professor of Computer Science, Electrical and Computer Engineering,
A Statistical Approach to Texture Classification Nicholas Chan Heather Dunlop Project Dec. 14, 2005.
Data Visualization Fall The Data as a Quantity Quantities can be classified in two categories: Intrinsically continuous (scientific visualization,
InfoVis Cyberinfrastructure Shashikant Penumarthy, Bruce Herr & Katy Börner School of Library and Information Science sprao | bherr
Ghislain Fouodji Tasse Supervisor: Dr. Karen Bradshaw Computer Science Department Rhodes University 04 August 2009.
Tallahassee, Florida, 2016 CIS4930 Introduction to Data Mining Midterm Review Peixiang Zhao.
Focus and Context DIVA Research Group Master Seminar 2006/07 Lorenzo Clementi, 14/2/ of 13.
What is a working drawing and what is orthographic projection?
Types of Models Marti Blad PhD PE
Building Adaptive Basis Function with Continuous Self-Organizing Map
Decimation Of Triangle Meshes
D I s , a ·.... l8l8.
Guest lecturer: Isabel K. Darcy
Central Tendency Central Tendency – measures of location for a distribution Mode – the commonly occurring number in a data set Median – the middle score.
Physics-based simulation for visual computing applications
3.4 – Linear Programming.
HOW TO NAVIGATE A LINEAR GRAPH IN SCIENCE OR MATH!
Topological Ordering Algorithm: Example
Computing Vertex Normals from Arbitrary Meshes
Introduction to linear Lie groups
Depth Of Field (DOF).
I ll I
Luísa Ferreira Bastos, João Manuel R. S. Tavares
Routing Algorithms Problems
Lecture 2: Geometry vs Linear Algebra Points-Vectors and Distance-Norm
Nur Hasan Mahmud Shahen Lecturer of Mathematics. Department of Computer Science & Engineering (CSE). University- Institute of Science Trade & Technology,
' 1 A ./.\.l+./.\.l
Topological Ordering Algorithm: Example
Topological Ordering Algorithm: Example
Topological Ordering Algorithm: Example
Presentation transcript:

Information Visualization Toolkits Workshop 2004 Sheelagh Carpendale Department of Computer Science, University of Calgary, Alberta, Canada Sheelagh Carpendale Department of Computer Science, University of Calgary, Alberta, Canada

Discussion points Why –Support further research –Allow creation of an InfoVis of my data Integration of algorithms Distinction of InfoVis aspects –Representation –Presentation –Integration Geometry only Concept of Meta-Vis Why –Support further research –Allow creation of an InfoVis of my data Integration of algorithms Distinction of InfoVis aspects –Representation –Presentation –Integration Geometry only Concept of Meta-Vis

Distance Metrics distinction between orthogonal and radial layout preserve topology, orthogonality, proximity distinction between orthogonal and radial layout preserve topology, orthogonality, proximity

Distance metrics - L 2 L 2 = (x 1 - x 2 ) 2 + (y 1 - y 2 ) 2 2 Eucildean distance Eucildean distance generalizing distance generalizing distance L p = (x 1 - x 2 ) p + (y 1 - y 2 ) p p

Distance metrics - L L = (x 1 - x 2 ) + (y 1 - y 2 ) L L Simplifies to L = max ( | x 1 - x 2 |, | y 1 - y 2 |)

Distance metrics - L 1 L 1 = | x 1 - x 2 | 1 + | y 1 - y 2 | 1 1 L 1 Manhattan metric L 1 Manhattan metric Simplifies to L 1 = | x 1 - x 2 | + | y 1 - y 2 |

L p -metrics L p = (x 1 - x 2 ) p + (y 1 - y 2 ) p p L1L1 L1L1 L2L2 L2L2 L L L3L3 L3L3

EPF Library- environment class magnifypoint3D/2D baseplane pointers lenses viewpoint normal additive none blending mode magnify3D/2D pointers data environment

EPF Library- lens class focal shape dimensionality lens coordinates drop-off regions lens limits focal coordinates d-metric regions lens

EPF Library- lens class focal shape

EPF Library- lens class L1L1 L1L1 L2L2 L2L2 L L L3L3 L3L3 distance metrics d-metric regions

EPF Library- lens class, drop-off function linear cosine Gaussian hemisphere auxiliary function drop-off function drop-off regions

EPF Library- data class get data set data data

Questions for this workshop Is common infrastructure possible? Algorithm integration Distinction of InfoVis aspects –Representation –Presentation –Integration Supporting easy application development Supporting meta-vis Supporting creativity Is common infrastructure possible? Algorithm integration Distinction of InfoVis aspects –Representation –Presentation –Integration Supporting easy application development Supporting meta-vis Supporting creativity

Folding