Natural Visualization Steve Haroz & Kwan-Liu Ma University of California at Davis.

Slides:

Advertisements

Similar presentations

Dynamic View Selection for Time-Varying Volumes Guangfeng Ji* and Han-Wei Shen The Ohio State University *Now at Vital Images.

Advertisements

Outline 3. Data Analysis 4. Follow Up Study 1. Previous Work 2. Experiment.

Advances in Mathematical Modeling: Dynamical Equations on Time Scales Ian A. Gravagne School of Engineering and Computer Science Baylor University, Waco,

Chapter 5: Space and Form Form & Pattern Perception: Humans are second to none in processing visual form and pattern information. Our ability to see patterns.

Image Registration  Mapping of Evolution. Registration Goals Assume the correspondences are known Find such f() and g() such that the images are best.

Review of 1-D Fourier Theory:

Filtration based on Color distance

Evaluating Color Descriptors for Object and Scene Recognition Koen E.A. van de Sande, Student Member, IEEE, Theo Gevers, Member, IEEE, and Cees G.M. Snoek,

Signal and System I Magnitude-phase representation of Fourier Transform Magnitude-phase representation of frequency response of LTI systems.

Light Fields PROPERTIES AND APPLICATIONS. Outline  What are light fields  Acquisition of light fields  from a 3D scene  from a real world scene 

1 Computational Vision CSCI 363, Fall 2012 Lecture 35 Perceptual Organization II.

Vision Based Control Motion Matt Baker Kevin VanDyke.

776 Computer Vision Jan-Michael Frahm, Enrique Dunn Fall 2014.

Relevance Feedback Content-Based Image Retrieval Using Query Distribution Estimation Based on Maximum Entropy Principle Irwin King and Zhong Jin Nov

School of Computing Science Simon Fraser University

Lecture 30: Light, color, and reflectance CS4670: Computer Vision Noah Snavely.

Lecture 8: Fourier Series and Fourier Transform

2D Fourier Theory for Image Analysis Mani Thomas CISC 489/689.

CAP4730: Computational Structures in Computer Graphics Basic Graphics Hardware.

Spatio-chromatic image content descriptors and their analysis using Extreme Value theory Vasileios Zografos and Reiner Lenz

Relevance Feedback Content-Based Image Retrieval Using Query Distribution Estimation Based on Maximum Entropy Principle Irwin King and Zhong Jin The Chinese.

Wavelet Analysis for Engineered Log Jams William Chen Eco-informatics Summer Institute 22 August

©2013 Cengage Learning. All Rights Reserved. Business Management, 13e Data Analysis and Decision Making Mathematics and Management Basic.

Sensation and Perception

Image Processing Fourier Transform 1D Efficient Data Representation Discrete Fourier Transform - 1D Continuous Fourier Transform - 1D Examples.

FlowString: Partial Streamline Matching using Shape Invariant Similarity Measure for Exploratory Flow Visualization Jun Tao, Chaoli Wang, Ching-Kuang Shene.

Image Enhancement in the Frequency Domain Spring 2006, Jen-Chang Liu.

Basic signals Why use complex exponentials? – Because they are useful building blocks which can be used to represent large and useful classes of signals.

Wireless and Mobile Computing Transmission Fundamentals Lecture 2.

Texture. Texture is an innate property of all surfaces (clouds, trees, bricks, hair etc…). It refers to visual patterns of homogeneity and does not result.

How Capacity Limits of Attention Influence Information Visualization Effectiveness IEEE INFOVIS 2012 Steve Haroz, University of California, Davis David.

Lecture 2b Readings: Kandell Schwartz et al Ch 27 Wolfe et al Chs 3 and 4.

Signal Processing and Representation Theory Lecture 4.

Layout of Multiple Views for Volume Visualization: A User Study Daniel Lewis, Steve Haroz, Kwan-Liu Ma University of California, Davis.

Stylization and Abstraction of Photographs Doug Decarlo and Anthony Santella.

How natural scenes might shape neural machinery for computing shape from texture? Qiaochu Li (Blaine) Advisor: Tai Sing Lee.

Digital Image Processing, 2nd ed. © 2002 R. C. Gonzalez & R. E. Woods Background Any function that periodically repeats itself.

Basics Course Outline, Discussion about the course material, reference books, papers, assignments, course projects, software packages, etc.

Lecture 7: Sampling Review of 2D Fourier Theory We view f(x,y) as a linear combination of complex exponentials that represent plane waves. F(u,v) describes.

Chapter 3: Neural Processing and Perception. Neural Processing and Perception Neural processing is the interaction of signals in many neurons.

Understanding Users Cognition & Cognitive Frameworks

Part 4 Chapter 16 Fourier Analysis PowerPoints organized by Prof. Steve Chapra, University All images copyright © The McGraw-Hill Companies, Inc. Permission.

Optical Holography Martin Janda, Ivo Hanák Introduction Wave Optics Principles Optical holograms Optical Holography Martin Janda, Ivo Hanák Introduction.

Judith C. Brown Journal of the Acoustical Society of America,1991 Jain-De,Lee.

Nautral Visualization Steve Haroz* and Kwan-Liu Ma* *University of California at Davis EuroVis 2006.

Scaling Studies of Perceived Source Width Juha Merimaa Institut für Kommunikationsakustik Ruhr-Universität Bochum.

Autonomous Robots Vision © Manfred Huber 2014.

Lecture 34: Light, color, and reflectance CS4670 / 5670: Computer Vision Noah Snavely.

Math Review Towards Fourier Transform

02/05/2002 (C) University of Wisconsin 2002, CS 559 Last Time Color Quantization Mach Banding –Humans exaggerate sharp boundaries, but not fuzzy ones.

CS559: Computer Graphics Lecture 3: Digital Image Representation Li Zhang Spring 2008.

ECE 8443 – Pattern Recognition EE 3512 – Signals: Continuous and Discrete Objectives: Eigenfunctions Fourier Series of CT Signals Trigonometric Fourier.

Change Blindness Images Li-Qian Ma 1, Kun Xu 1, Tien-Tsin Wong 2, Bi-Ye Jiang 1, Shi-Min Hu 1 1 Tsinghua University 2 The Chinese University of Hong Kong.

Chapter 2 Data in Science. Section 1: Tools and Models.

Non-Linear Transformations Michael J. Watts

Grenoble Images Parole Signal Automatique Modeling of visual cortical processing to estimate binocular disparity Introduction - The objective is to estimate.

2D Fourier Transform.

Understanding Psychophysics: Spatial Frequency & Contrast

ECE 8443 – Pattern Recognition ECE 3163 – Signals and Systems Objectives: Eigenfunctions Fourier Series of CT Signals Trigonometric Fourier Series Dirichlet.

Independent Component Analysis features of Color & Stereo images Authors: Patrik O. Hoyer Aapo Hyvarinen CIS 526: Neural Computation Presented by: Ajay.

DATA VISUALIZATION BOB MARSHALL, MD MPH MISM FAAFP FACULTY, DOD CLINICAL INFORMATICS FELLOWSHIP.

Elements of Art & Principles of Design. Elements of Art – (building blocks of visual art)

Image Perception ‘Let there be light! ‘. “Let there be light”

General Principles: The senses as physical instruments

Outline Linear Shift-invariant system Linear filters

Image Segmentation Techniques

Data Visualization

Wavelet Based Real-time Smoke Detection In Video

Outline Linear Shift-invariant system Linear filters

Intensity Transformation

Presentation transcript:

Natural Visualization Steve Haroz & Kwan-Liu Ma University of California at Davis

Outline Purpose Math Background Applying and extending existing theories InfoVis Contest Application to GUIs Summary and Conclusion

Outline Purpose Math Background Applying and extending existing theories InfoVis Contest Application to GUIs Summary and Conclusion

Purpose What makes for a good Visualization? –Aesthetics? –Color? –Complexity? –Beginner or Expert? Intuitive? Can understanding the process of visualization help?

The Process … Visualization Complete?

Which Representation Is Best? Who can prove by experience the non-existence of a cause when all that experience tells us is that we do not perceive it?

The Process … Visualization

Hubel 1988 The Forgotten Stage of Visualization

Purpose Applicability of visual system knowledge –Retina tuned to natural images Certain images more easily perceptible? Is interaction aided by these natural GUIs ?

Outline Purpose Math Background Applying and extending existing theories InfoVis Contest Application to GUIs Summary and Conclusion

Spatial Frequencies Similar to auditory frequencies Varying intensity (light) over space

Fourier Transform Sum of sin/cos waves

Spatial Frequencies of Natural Images Take Fourier transform along each orientation and average f -2 pattern Pattern is prevalent in all natural scenes Plot on log-log scale

Unnatural images

Natural Images

Size Distribution This pattern is explained by a collage of objects occluding each other These objects have a power distribution area = 2 x

Outline Purpose Math Background Applying and extending existing theories InfoVis Contest Application to GUIs Summary and Conclusion

power exponential linear constant

Plot of spatial frequencies

Linear Trend

Images Without Occlusion You cant visualize what is not visible Images with adjacent squares Same sizing applies

power exponential linear constant

Trend – no occlusion

Outline Purpose Math Background Applying and extending existing theories InfoVis Contest Application to GUIs Summary and Conclusion

Naturalness Metric 1.Closeness to f -2 2.Linearity

InfoVis 2004 Contest

InfoVis 2005 Contest

Outline Purpose Math Background Applying and extending existing theories InfoVis Contest Application to GUIs Summary and Conclusion

Image Analysis for GUI Study Applications with hierarchical data Analyze screenshots Compare with usage data (user study) Use statistics to find behavioral patterns

Correlation with Response Time

Outline Purpose Math Background Applying and extending existing theories InfoVis Contest Application to GUIs Summary and Conclusion

Visualization preference correlates with a property of the visual system Bias-free metric may help vis generation Utility or aesthetics? More visual properties

Acknowledgements Bruno Olshausen Yue Wang