Virtual Reality ll. Visual Imaging in the Electronic Age. Donald P. Greenberg November 16, 2017 Lecture #22
|
|
- Cecilia Newman
- 5 years ago
- Views:
Transcription
1 Virtual Reality ll Visual Imaging in the Electronic Age Donald P. Greenberg November 16, 2017 Lecture #22
2 Fundamentals of Human Perception Retina, Rods & Cones, Physiology Receptive Fields Field of View Visual Acuity of Resolution Opponent Color Theory Compression Bandwidth Limitations Saccades
3 The Optomotor Cycle
4 Extraocular Muscles Foundations of Sensation and Perception. Mather, George
5 Saccade Control Saccade control is the ability of the eye(s) to move quickly from one fixation point to another ( ms) To obtain a complete picture, normal adults perform 3-5 saccades ( snapshots ) per second Fixation restops are ms
6 Saccadic Motion The eye jumps, comes to rest momentarily (producing a small dot on the record), then jumps to a new locus of interest. - David H. Hubel. EYE, BRAIN, AND VISION, 1988 Scientific American Books, Inc. p. 80.
7 Saccadic Masking Visual saccadic suppression The brain selectively blocks visual processing during eye movements Neither the motion of the eye or subsequent motion blur of the image nor the time gap in visual perception is noticeable to the viewer
8 Saccadic Masking There are two major types of saccadic masking or suppression Flash suppression is the inability of the light to see a flash of light during a saccade Suppression of image displacement is characterized by the inability to perceive whether a target has moved during a saccade.
9 Peak Angular Velocity Wikipedia
10 Human Depth Perception Depth Perception Oculomotor Visual Binocular Monocular Binocular Monocular Convergence Accommodation Stereopsis Static Cues Motion Parallax Perspective Familiarity, Relative Size Motion, Position Occlusion Texture Gradient Shading, Shadows, Highlights Atmospheric Blur
11 Monocular Human Depth Perception Depth Perception Oculomotor Visual Binocular Monocular Binocular Monocular Convergence Accommodation Stereopsis Static Cues Motion Parallax Perspective Familiarity, Relative Size Motion, Position Occlusion Texture Gradient Shading, Shadows, Highlights Atmospheric Blur
12 Monoscopic Depth Cues Perspective Depth from Motion, Relative Size, Position, Familiarity Occlusion Texture Gradient Parallax from Motion Shadows and Specular Highlights Atmospheric Blur
13 Monoscopic Depth Cues Perspective Depth from Motion, Relative Size, Position, Familiarity Occlusion Texture Gradient Parallax from Motion Shadows and Specular Highlights Atmospheric Blur
14 Monoscopic Depth Cues Perspective Depth from Motion, Relative Size, Position, Familiarity Occlusion Texture Gradient Parallax from Motion Shadows and Specular Highlights Atmospheric Blur
15 Monoscopic Depth Cues Perspective Depth from Motion, Relative Size, Position, Familiarity Occlusion Texture Gradient Parallax from Motion Shadows and Specular Highlights Atmospheric Blur
16 Monoscopic Depth Cues Perspective Depth from Motion, Relative Size, Position, Familiarity Occlusion Texture Gradient Parallax from Motion Shading, Shadows, and Specular Highlights Atmospheric Blur Viewpoint A Viewpoint B Viewpoint C
17 Monoscopic Depth Cues Perspective Depth from Motion, Relative Size, Position, Familiarity Occlusion Texture Gradient Parallax from Motion Shading, Shadows, and Specular Highlights Atmospheric Blur
18 Monoscopic Depth Cues Perspective Depth from Motion, Relative Size, Position, Familiarity Occlusion Texture Gradient Parallax from Motion Shading, Shadows, and Specular Highlights Atmospheric Blur
19 Monoscopic Depth Cues Perspective Depth from Motion, Relative Size, Position, Familiarity Occlusion Texture Gradient Parallax from Motion Shadows and Specular Highlights Atmospheric Blur
20 Monoscopic Depth Cues Perspective Depth from Motion, Relative Size, Position, Familiarity Occlusion Texture Gradient Parallax from Motion Shadows and Specular Highlights Atmospheric Blur Accommodation Note change in lens shape
21 Accommodation This is the process by which the vertebrate eye changes optical power to maintain a clear image or focus on an object as its distance varies.
22 Accommodation The reflex can be controlled but cannot be felt Accommodation amplitude declines with age
23 Vergence The simultaneous movement of the pupils of the eyes toward or away from one another during focusing. This measure of the convergence or divergence of a pair of light rays is defined as vergence.
24 Vergence Accommodation Conflict
25 Human Depth Perception Depth Perception Oculomotor Visual Binocular Monocular Binocular Monocular Convergence Accommodation Stereopsis Static Cues Motion Parallax Perspective Familiarity, Relative Size Motion, Position Occlusion Texture Gradient Shading, Shadows, Highlights Atmospheric Blur
26 Binocular Vision Binocular Vision, which is the basis for stereopsis is important for depth perception and covers 114 degrees (horizontally) of the human visual field. The remaining sixty to seventy degrees have no binocular vision (because only one eye can see those portions of the visual field)
27 Stereoscopic Depth Cues Stereopsis Horizontal Parallax Occlusion Revelation Shape Change Convergence
28 Stereoscopic Depth Cues Stereopsis Shape Change Standard Stereo HypoStereo (Gigantism) HyperStereo (Dwarfism) Convergence Standard HypoStereo HyperStereo
29 Stereoscopic Depth Cues Stereopsis Shape Change Convergence Maintain single binocular vision Fusion Vision and Visual Disabilities An Introduction, by Gerd Waloszek, SAP User Experience
30 Moore s Law Chip density doubles every 18 months. Processing Power (P) in 15 years:
31 Exponential Laws of Computing Growth
32 War for the Planet of the Apes
33 Off-line/On-line RRRRRRRRRR 10 hoooooooo 10 mmmmmmmmmmmmmmmmmmmmmmmm = (10 hrrrr.)(60 mmmmmm hrr = /eye = ssssss )(60 mmmmmm )(1,000mmmm 10 mmmm ssssss )
34 Potential Improvements Eye tracking and foveal rendering Multi resolution displays Monoscopic vs. stereoscopic level of detail Asynchronous spacewarp
35 Increasing Densities (ppi) of OLED Displays
36 Field of View of the Human Eye Wikipedia
37 Foveal Eye Tracking Constraints 2016 Speed- needs to be fast enough to meet update requirements (currently 11 milliseconds, 90 Hz) Accuracy- Gaze direction is < 0.5 degree Foveal direction accuracy can be ~ 1.0 arc minutes (1/60 of a degree) Non-invasive measurements- still need to see entire visual field
38 Eye Tracking
39 Sensing Methods: Retinal Tracking Hard problem with current technologies Extremely difficult to illuminate Must bounce light off of retina Light comes back through iris Light must be extremely bright Too much exposure will damage retina Typically done in ophthalmological setting Presently can only detect faint images of blood vessels, companies working on it Very high angular resolution, but would presently require occlusion of vision
40 Purkinje Reflections
41 Purkinje Reflections Cornsweet and Crane 1973
42 Purkinje Reflections
43 1 st and 4 th Purkinje Reflections
44 No Foveated Rendering Roadtovr.com
45 Contemporary blur foveated rendering Roadtovr.com
46 NVIDIA s contrast preserving rendering Roadtovr.com
47 Research on Foveated Displays
48 Alaskan Moose Diorama
49 Dall Sheep Restoration
50 Alaska Brown Bear Diorama
51 Plan of Typical Diorama
52 LOD Image Based Primitives Layered Depth Images
53 Space & Time Warping When frame rates are not met, there are several types of solutions, but all have their deficiencies Visual artifacts appear because of loss of accuracy e.g. Imperfect extrapolation, Object Disocclusion trails
54 Asynchronous Spacewarp
55 Asynchronous Spacewarp
56 Asynchronous Spacewarp
57 Asynchronous Spacewarp
58 Asynchronous Spacewarp
59 End
Basic distinctions. Definitions. Epstein (1965) familiar size experiment. Distance, depth, and 3D shape cues. Distance, depth, and 3D shape cues
Distance, depth, and 3D shape cues Pictorial depth cues: familiar size, relative size, brightness, occlusion, shading and shadows, aerial/ atmospheric perspective, linear perspective, height within image,
More informationPERCEIVING DEPTH AND SIZE
PERCEIVING DEPTH AND SIZE DEPTH Cue Approach Identifies information on the retina Correlates it with the depth of the scene Different cues Previous knowledge Slide 3 Depth Cues Oculomotor Monocular Binocular
More informationGoogle Daydream
Current VR Devices Current VR Devices Most of these new devices include slight improvements, primarily involved with tracking (both location and orientation) and obtaining better accuracy Google Daydream
More informationStereovision. Binocular disparity
Stereovision Binocular disparity Retinal correspondence Uncrossed disparity Horoptor Crossed disparity Horoptor, crossed and uncrossed disparity Wheatsteone stereoscope (c. 1838) Red-green anaglyph How
More informationPerception, Part 2 Gleitman et al. (2011), Chapter 5
Perception, Part 2 Gleitman et al. (2011), Chapter 5 Mike D Zmura Department of Cognitive Sciences, UCI Psych 9A / Psy Beh 11A February 27, 2014 T. M. D'Zmura 1 Visual Reconstruction of a Three-Dimensional
More informationRealtime 3D Computer Graphics Virtual Reality
Realtime 3D Computer Graphics Virtual Reality Human Visual Perception The human visual system 2 eyes Optic nerve: 1.5 million fibers per eye (each fiber is the axon from a neuron) 125 million rods (achromatic
More informationImportant concepts in binocular depth vision: Corresponding and non-corresponding points. Depth Perception 1. Depth Perception Part II
Depth Perception Part II Depth Perception 1 Binocular Cues to Depth Depth Information Oculomotor Visual Accomodation Convergence Binocular Monocular Static Cues Motion Parallax Perspective Size Interposition
More informationThe Human Visual System!
! The Human Visual System! Gordon Wetzstein! Stanford University! EE 267 Virtual Reality! Lecture 5! stanford.edu/class/ee267/!! nautilus eye, wikipedia! Dawkins, Climbing Mount Improbable,! Norton & Company,
More informationStereo Graphics. Visual Rendering for VR. Passive stereoscopic projection. Active stereoscopic projection. Vergence-Accommodation Conflict
Stereo Graphics Visual Rendering for VR Hsueh-Chien Chen, Derek Juba, and Amitabh Varshney Our left and right eyes see two views, which are processed by our visual cortex to create a sense of depth Computer
More informationProf. Feng Liu. Spring /27/2014
Prof. Feng Liu Spring 2014 http://www.cs.pdx.edu/~fliu/courses/cs510/ 05/27/2014 Last Time Video Stabilization 2 Today Stereoscopic 3D Human depth perception 3D displays 3 Stereoscopic media Digital Visual
More informationVisual Rendering for VR. Stereo Graphics
Visual Rendering for VR Hsueh-Chien Chen, Derek Juba, and Amitabh Varshney Stereo Graphics Our left and right eyes see two views, which are processed by our visual cortex to create a sense of depth Computer
More informationBinocular cues to depth PSY 310 Greg Francis. Lecture 21. Depth perception
Binocular cues to depth PSY 310 Greg Francis Lecture 21 How to find the hidden word. Depth perception You can see depth in static images with just one eye (monocular) Pictorial cues However, motion and
More informationThink-Pair-Share. What visual or physiological cues help us to perceive 3D shape and depth?
Think-Pair-Share What visual or physiological cues help us to perceive 3D shape and depth? [Figure from Prados & Faugeras 2006] Shading Focus/defocus Images from same point of view, different camera parameters
More informationMulti-View Geometry (Ch7 New book. Ch 10/11 old book)
Multi-View Geometry (Ch7 New book. Ch 10/11 old book) Guido Gerig CS-GY 6643, Spring 2016 gerig@nyu.edu Credits: M. Shah, UCF CAP5415, lecture 23 http://www.cs.ucf.edu/courses/cap6411/cap5415/, Trevor
More informationDEPTH PERCEPTION. Learning Objectives: 7/31/2018. Intro & Overview of DEPTH PERCEPTION** Speaker: Michael Patrick Coleman, COT, ABOC, & former CPOT
DEPTH PERCEPTION Speaker: Michael Patrick Coleman, COT, ABOC, & former CPOT Learning Objectives: Attendees will be able to 1. Explain what the primary cue to depth perception is (vs. monocular cues) 2.
More informationlecture 10 - depth from blur, binocular stereo
This lecture carries forward some of the topics from early in the course, namely defocus blur and binocular disparity. The main emphasis here will be on the information these cues carry about depth, rather
More informationUSE R/G GLASSES. Binocular Combina0on of Images. What can happen when images are combined by the two eyes?
Binocular Combina0on of Images 3D D USE R/G GLASSES What can happen when images are combined by the two eyes? Fusion- when images are similar and disparity is small Monocular Suppression / Rivalry- when
More informationProcessing Framework Proposed by Marr. Image
Processing Framework Proposed by Marr Recognition 3D structure; motion characteristics; surface properties Shape From stereo Motion flow Shape From motion Color estimation Shape From contour Shape From
More informationMahdi Amiri. May Sharif University of Technology
Course Presentation Multimedia Systems 3D Technologies Mahdi Amiri May 2014 Sharif University of Technology Binocular Vision (Two Eyes) Advantages A spare eye in case one is damaged. A wider field of view
More informationRobert Collins CSE486, Penn State Lecture 08: Introduction to Stereo
Lecture 08: Introduction to Stereo Reading: T&V Section 7.1 Stereo Vision Inferring depth from images taken at the same time by two or more cameras. Basic Perspective Projection Scene Point Perspective
More informationStereoscopic Systems Part 1
Stereoscopic Systems Part 1 Terminology: Stereoscopic vs. 3D 3D Animation refers to computer animation created with programs (like Maya) that manipulate objects in a 3D space, though the rendered image
More informationStereo CSE 576. Ali Farhadi. Several slides from Larry Zitnick and Steve Seitz
Stereo CSE 576 Ali Farhadi Several slides from Larry Zitnick and Steve Seitz Why do we perceive depth? What do humans use as depth cues? Motion Convergence When watching an object close to us, our eyes
More informationExperimental Humanities II. Eye-Tracking Methodology
Experimental Humanities II Eye-Tracking Methodology Course outline 22.3. Introduction to Eye-Tracking + Lab 1 (DEMO & stimuli creation) 29.3. Setup and Calibration for Quality Data Collection + Lab 2 (Calibration
More informationProject 4 Results. Representation. Data. Learning. Zachary, Hung-I, Paul, Emanuel. SIFT and HoG are popular and successful.
Project 4 Results Representation SIFT and HoG are popular and successful. Data Hugely varying results from hard mining. Learning Non-linear classifier usually better. Zachary, Hung-I, Paul, Emanuel Project
More informationNatural Viewing 3D Display
We will introduce a new category of Collaboration Projects, which will highlight DoCoMo s joint research activities with universities and other companies. DoCoMo carries out R&D to build up mobile communication,
More informationMultidimensional image retargeting
Multidimensional image retargeting 9:00: Introduction 9:10: Dynamic range retargeting Tone mapping Apparent contrast and brightness enhancement 10:45: Break 11:00: Color retargeting 11:30: LDR to HDR 12:20:
More informationCS 563 Advanced Topics in Computer Graphics Stereoscopy. by Sam Song
CS 563 Advanced Topics in Computer Graphics Stereoscopy by Sam Song Stereoscopy Introduction Parallax Camera Displaying and Viewing Results Stereoscopy What is it? seeing in three dimensions creates the
More informationRecap: Features and filters. Recap: Grouping & fitting. Now: Multiple views 10/29/2008. Epipolar geometry & stereo vision. Why multiple views?
Recap: Features and filters Epipolar geometry & stereo vision Tuesday, Oct 21 Kristen Grauman UT-Austin Transforming and describing images; textures, colors, edges Recap: Grouping & fitting Now: Multiple
More informationMiniature faking. In close-up photo, the depth of field is limited.
Miniature faking In close-up photo, the depth of field is limited. http://en.wikipedia.org/wiki/file:jodhpur_tilt_shift.jpg Miniature faking Miniature faking http://en.wikipedia.org/wiki/file:oregon_state_beavers_tilt-shift_miniature_greg_keene.jpg
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 informationLecture 7: Depth/Occlusion
Lecture 7: Depth/Occlusion Information Visualization CPSC 533C, Fall 2006 Tamara Munzner UBC Computer Science 3 October 2006 Readings Covered Ware, Chapter 8: Space Perception and the Display of Data in
More informationComputer and Machine Vision
Computer and Machine Vision Lecture Week 4 Part-2 February 5, 2014 Sam Siewert Outline of Week 4 Practical Methods for Dealing with Camera Streams, Frame by Frame and De-coding/Re-encoding for Analysis
More informationCS5670: Computer Vision
CS5670: Computer Vision Noah Snavely Light & Perception Announcements Quiz on Tuesday Project 3 code due Monday, April 17, by 11:59pm artifact due Wednesday, April 19, by 11:59pm Can we determine shape
More informationVisual areas in the brain. Image removed for copyright reasons.
Visual areas in the brain Image removed for copyright reasons. Image removed for copyright reasons. FOVEA OPTIC NERVE AQUEOUS HUMOR IRIS CORNEA PUPIL RETINA VITREOUS HUMOR LENS What do you see? Why? The
More informationABSTRACT Purpose. Methods. Results.
ABSTRACT Purpose. Is there a difference in stereoacuity between distance and near? Previous studies produced conflicting results. We compared distance and near stereoacuities using identical presentation
More informationVS 117 LABORATORY III: FIXATION DISPARITY INTRODUCTION
VS 117 LABORATORY III: FIXATION DISPARITY INTRODUCTION Under binocular viewing, subjects generally do not gaze directly at the visual target. Fixation disparity is defined as the difference between the
More informationCS4670: Computer Vision
CS4670: Computer Vision Noah Snavely Lecture 30: Light, color, and reflectance Light by Ted Adelson Readings Szeliski, 2.2, 2.3.2 Light by Ted Adelson Readings Szeliski, 2.2, 2.3.2 Properties of light
More informationStereo. Shadows: Occlusions: 3D (Depth) from 2D. Depth Cues. Viewing Stereo Stereograms Autostereograms Depth from Stereo
Stereo Viewing Stereo Stereograms Autostereograms Depth from Stereo 3D (Depth) from 2D 3D information is lost by projection. How do we recover 3D information? Image 3D Model Depth Cues Shadows: Occlusions:
More informationVideo Communication Ecosystems. Research Challenges for Immersive. over Future Internet. Converged Networks & Services (CONES) Research Group
Research Challenges for Immersive Video Communication Ecosystems over Future Internet Tasos Dagiuklas, Ph.D., SMIEEE Assistant Professor Converged Networks & Services (CONES) Research Group Hellenic Open
More informationStereo: Disparity and Matching
CS 4495 Computer Vision Aaron Bobick School of Interactive Computing Administrivia PS2 is out. But I was late. So we pushed the due date to Wed Sept 24 th, 11:55pm. There is still *no* grace period. To
More informationImage Formation. CS418 Computer Graphics Eric Shaffer.
Image Formation CS418 Computer Graphics Eric Shaffer http://graphics.cs.illinois.edu/cs418/fa14 Some stuff about the class Grades probably on usual scale: 97 to 93: A 93 to 90: A- 90 to 87: B+ 87 to 83:
More informationSubjects - Forty-nine students from Escola Superior de Tecnologia da Saúde de Lisboa
INTRODUCTION: AIM: Stereopsis is the perception of depth based on retinal disparity that can be fused for single vision (fine stereopsis). Global stereopsis depends on the process of random dot stimuli
More informationzspace Developer SDK Guide - Introduction Version 1.0 Rev 1.0
zspace Developer SDK Guide - Introduction Version 1.0 zspace.com Developer s Guide Rev 1.0 zspace, Inc. 2015. zspace is a registered trademark of zspace, Inc. All other trademarks are the property of their
More informationStereoscopic media. 3D is hot today. 3D has a long history. 3D has a long history. Digital Visual Effects Yung-Yu Chuang
3D is hot today Stereoscopic media Digital Visual Effects Yung-Yu Chuang 3D has a long history 1830s, stereoscope 1920s, first 3D film, The Power of Love projected dual-strip in the red/green anaglyph
More informationHAMED SARBOLANDI SIMULTANEOUS 2D AND 3D VIDEO RENDERING Master s thesis
HAMED SARBOLANDI SIMULTANEOUS 2D AND 3D VIDEO RENDERING Master s thesis Examiners: Professor Moncef Gabbouj M.Sc. Payman Aflaki Professor Lauri Sydanheimo Examiners and topic approved by the Faculty Council
More informationLecture 14: Computer Vision
CS/b: Artificial Intelligence II Prof. Olga Veksler Lecture : Computer Vision D shape from Images Stereo Reconstruction Many Slides are from Steve Seitz (UW), S. Narasimhan Outline Cues for D shape perception
More informationComparison of Accommodation and Convergence by Simultaneous Measurements during 2D and 3D Vision Gaze
Comparison of Accommodation and Convergence by Simultaneous Measurements during 2D and 3D Vision Gaze Hiroki Hori 1, Tomoki Shiomi 1, Tetsuya Kanda 1, Akira Hasegawa 1, Hiromu Ishio 1, Yasuyuki Matsuura
More informationGraphics Hardware and Display Devices
Graphics Hardware and Display Devices CSE328 Lectures Graphics/Visualization Hardware Many graphics/visualization algorithms can be implemented efficiently and inexpensively in hardware Facilitates interactive
More informationCS6670: Computer Vision
CS6670: Computer Vision Noah Snavely Lecture 20: Light, reflectance and photometric stereo Light by Ted Adelson Readings Szeliski, 2.2, 2.3.2 Light by Ted Adelson Readings Szeliski, 2.2, 2.3.2 Properties
More informationExample Videos. Administrative 2/3/2014. Comp/Phys/Apsc 715. Pre-Attentive Characteristics: Information that Pops Out
Comp/Phys/Apsc 715 Pre-Attentive Characteristics: Information that Pops Out 1 Example Videos Linked feature-map and 3D views for DTMRI Parallel Coordinates, slice, 3D for Astro-Jet Vis 2011: Waser: Ensemble
More informationEvaluation of Geometric Depth Estimation Model for Virtual Environment
Evaluation of Geometric Depth Estimation Model for Virtual Environment Puneet Sharma 1, Jan H. Nilsen 1, Torbjørn Skramstad 2, Faouzi A. Cheikh 3 1 Department of Informatics & E-Learning (AITeL), Sør Trøndelag
More informationConflict? What conflict?
Conflict? What conflict? Peter Howarth, PhD Environmental Ergonomics Research Centre What s the problem? According to a study of 400 filmgoers by L Mark Carrier, of California State University, 3D
More informationCOMPUTER-GENERATED STEREOSCOPIC DISPLAYS
QUENTIN E. DOLECEK COMPUTER-GENERATED STEREOSCOPIC DISPLAYS Although stereoscopy has been with us for over 150 years, the preparation of computer-generated perspective views and formation of stereoscopic
More informationCS6670: Computer Vision
CS6670: Computer Vision Noah Snavely Lecture 21: Light, reflectance and photometric stereo Announcements Final projects Midterm reports due November 24 (next Tuesday) by 11:59pm (upload to CMS) State the
More informationStereo Optical Company Vision Tester Slide Package:
Stereo Optical Company Vision Tester Slide Package: Ophthalmic Prescreening Slide Package For complete vision screening needs, preschool through adult. Ideal for School, Athletic Physicals, Employment
More informationTITMUS Vision Screener S n e l l e n O c c u p a t i o n a l. S l i d e I n f o r m a t i o n b r o c h u r e
TITMUS Vision Screener S n e l l e n O c c u p a t i o n a l S l i d e I n f o r m a t i o n b r o c h u r e 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 TITMUS Vision Screener Snellen Occupational S l i d e I
More informationOptimizing Monocular Cues for Depth Estimation from Indoor Images
Optimizing Monocular Cues for Depth Estimation from Indoor Images Aditya Venkatraman 1, Sheetal Mahadik 2 1, 2 Department of Electronics and Telecommunication, ST Francis Institute of Technology, Mumbai,
More informationCSc I6716 Spring D Computer Vision. Introduction. Instructor: Zhigang Zhu City College of New York
Introduction CSc I6716 Spring 2012 Introduction Instructor: Zhigang Zhu City College of New York zzhu@ccny.cuny.edu Course Information Basic Information: Course participation p Books, notes, etc. Web page
More informationComputational Foundations of Cognitive Science
Computational Foundations of Cognitive Science Lecture 16: Models of Object Recognition Frank Keller School of Informatics University of Edinburgh keller@inf.ed.ac.uk February 23, 2010 Frank Keller Computational
More informationDepth. Common Classification Tasks. Example: AlexNet. Another Example: Inception. Another Example: Inception. Depth
Common Classification Tasks Recognition of individual objects/faces Analyze object-specific features (e.g., key points) Train with images from different viewing angles Recognition of object classes Analyze
More informationLimitations of Projection Radiography. Stereoscopic Breast Imaging. Limitations of Projection Radiography. 3-D Breast Imaging Methods
Stereoscopic Breast Imaging Andrew D. A. Maidment, Ph.D. Chief, Physics Section Department of Radiology University of Pennsylvania Limitations of Projection Radiography Mammography is a projection imaging
More information3D-TV Content Creation: Automatic 2D-to-3D Video Conversion Liang Zhang, Senior Member, IEEE, Carlos Vázquez, Member, IEEE, and Sebastian Knorr
372 IEEE TRANSACTIONS ON BROADCASTING, VOL. 57, NO. 2, JUNE 2011 3D-TV Content Creation: Automatic 2D-to-3D Video Conversion Liang Zhang, Senior Member, IEEE, Carlos Vázquez, Member, IEEE, and Sebastian
More informationExample Videos. Administrative 2/1/2012. Comp/Phys/Mtsc 715. Pre-Attentive Characteristics: Information that Pops Out
Comp/Phys/Mtsc 715 Pre-Attentive Characteristics: Information that Pops Out 1 Example Videos Linked feature-map and 3D views for DTMRI Parallel Coordinates, slice, 3D for Astro-Jet Vis 2011: Waser: Ensemble
More informationAssessing vergence-accommodation conflict as a source of discomfort in stereo displays
Assessing vergence-accommodation conflict as a source of discomfort in stereo displays Joohwan Kim, Takashi Shibata, David Hoffman, Martin Banks University of California, Berkeley * This work is published
More informationS U N G - E U I YO O N, K A I S T R E N D E R I N G F R E E LY A VA I L A B L E O N T H E I N T E R N E T
S U N G - E U I YO O N, K A I S T R E N D E R I N G F R E E LY A VA I L A B L E O N T H E I N T E R N E T Copyright 2018 Sung-eui Yoon, KAIST freely available on the internet http://sglab.kaist.ac.kr/~sungeui/render
More informationLight. Properties of light. What is light? Today What is light? How do we measure it? How does light propagate? How does light interact with matter?
Light Properties of light Today What is light? How do we measure it? How does light propagate? How does light interact with matter? by Ted Adelson Readings Andrew Glassner, Principles of Digital Image
More informationSID Display Week Yasuhiro Takaki. Institute of Symbiotic and Technology. Tokyo Univ. of Agri. & Tech.
Three-Dimensional i Displays: Present and Future Yasuhiro Takaki Institute of Symbiotic and Technology Tokyo University it of Agriculture and Technology 1 Outline 1. Introduction 2. Human Factors 3. Current
More information3D Computer Vision. Introduction. Introduction. CSc I6716 Fall Instructor: Zhigang Zhu City College of New York
Introduction CSc I6716 Fall 2010 3D Computer Vision Introduction Instructor: Zhigang Zhu City College of New York zzhu@ccny.cuny.edu Course Information Basic Information: Course participation Books, notes,
More information3D Display and AR. Linda Li, Rokid Rlab Dec. 27, 2016
3D Display and AR Linda Li, Rokid Rlab Dec. 27, 2016 1 Five OLED Trends of Future Tactile / Haptic Touch Displays Higher Pixel Density Glasses-free 3D Virtue Reality and Augmented Reality 1 Stereoscopic
More informationLecture 15: Shading-I. CITS3003 Graphics & Animation
Lecture 15: Shading-I CITS3003 Graphics & Animation E. Angel and D. Shreiner: Interactive Computer Graphics 6E Addison-Wesley 2012 Objectives Learn that with appropriate shading so objects appear as threedimensional
More informationAll human beings desire to know. [...] sight, more than any other senses, gives us knowledge of things and clarifies many differences among them.
All human beings desire to know. [...] sight, more than any other senses, gives us knowledge of things and clarifies many differences among them. - Aristotle University of Texas at Arlington Introduction
More informationTITMUS Vision Screener O c c u p a t i o n a l. S l i d e I n f o r m a t i o n b r o c h u r e
TITMUS Vision Screener O c c u p a t i o n a l S l i d e I n f o r m a t i o n b r o c h u r e 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 TITMUS Vision Occupational S l i d e I n f o r m a t i o n b r o c h u
More informationComputational Aesthetics for Rendering Virtual Scenes on 3D Stereoscopic Displays
Computational Aesthetics for Rendering Virtual Scenes on 3D Stereoscopic Displays László SZIRMAY-KALOS, Pirkko OITTINEN, and Balázs TERÉKI Introduction Computer graphics builds virtual scenes that are
More informationComputer Vision. I-Chen Lin, Assistant Professor Dept. of CS, National Chiao Tung University
Computer Vision I-Chen Lin, Assistant Professor Dept. of CS, National Chiao Tung University About the course Course title: Computer Vision Lectures: EC016, 10:10~12:00(Tues.); 15:30~16:20(Thurs.) Pre-requisites:
More informationWe are IntechOpen, the world s leading publisher of Open Access books Built by scientists, for scientists. International authors and editors
We are IntechOpen, the world s leading publisher of Open Access books Built by scientists, for scientists 3,800 116,000 120M Open access books available International authors and editors Downloads Our
More informationP1: OTA/XYZ P2: ABC c01 JWBK288-Cyganek December 5, :11 Printer Name: Yet to Come. Part I COPYRIGHTED MATERIAL
Part I COPYRIGHTED MATERIAL 1 Introduction The purpose of this text on stereo-based imaging is twofold: it is to give students of computer vision a thorough grounding in the image analysis and projective
More informationTITMUS Vision Screener T N O O c c u p a t i o n a l
TITMUS Vision Screener T N O O c c u p a t i o n a l S l i d e I n f o r m a t i o n b r o c h u r e 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 TITMUS Vision Screener TNO Occupational S l i d e I n f o r m a
More informationAutomatic 2D-to-3D Video Conversion Techniques for 3DTV
Automatic 2D-to-3D Video Conversion Techniques for 3DTV Dr. Lai-Man Po Email: eelmpo@cityu.edu.hk Department of Electronic Engineering City University of Hong Kong Date: 13 April 2010 Content Why 2D-to-3D
More informationArbib: Slides for TMB2 Section 7.2 1
Arbib: Slides for TMB2 Section 7.2 1 Lecture 20: Optic Flow Reading assignment: TMB2 7.2 If, as we walk forward, we recognize that a tree appears to be getting bigger, we can infer that the tree is in
More informationHuman Perception of Objects
Human Perception of Objects Early Visual Processing of Spatial Form Defined by Luminance, Color, Texture, Motion, and Binocular Disparity David Regan York University, Toronto University of Toronto Sinauer
More informationThe Importance of Visual Attention in Improving the 3D-TV Viewing Experience: Overview and New Perspectives
The Importance of Visual Attention in Improving the 3D-TV Viewing Experience: Overview and New Perspectives Quan Huynh-Thu, Marcus Barkowsky, Patrick Le Callet To cite this version: Quan Huynh-Thu, Marcus
More informationComplex Sensors: Cameras, Visual Sensing. The Robotics Primer (Ch. 9) ECE 497: Introduction to Mobile Robotics -Visual Sensors
Complex Sensors: Cameras, Visual Sensing The Robotics Primer (Ch. 9) Bring your laptop and robot everyday DO NOT unplug the network cables from the desktop computers or the walls Tuesday s Quiz is on Visual
More informationHolojackets. Semester Update: Fall 2017 December 4, 2017
Holojackets Semester Update: Fall 2017 December 4, 2017 Agenda Team Focus Problem Statement Background Research Team Structure Progress Conclusion/Project Plan 2 Team Focus Prototype 3D displays that allow
More informationScaling of Rendered Stereoscopic Scenes
University of West Bohemia in Pilsen Department of Computer Science and Engineering Univerzitni 8 30614 Pilsen Czech Republic Scaling of Rendered Stereoscopic Scenes Master Thesis Report Ricardo José Teixeira
More informationRender all data necessary into textures Process textures to calculate final image
Screenspace Effects Introduction General idea: Render all data necessary into textures Process textures to calculate final image Achievable Effects: Glow/Bloom Depth of field Distortions High dynamic range
More informationQuantitative Measurement of Eyestrain on 3D Stereoscopic Display Considering the Eye Foveation Model and Edge Information
Sensors 2014, 14, 8577-8604; doi:10.3390/s140508577 Article OPEN ACCESS sensors ISSN 1424-8220 www.mdpi.com/journal/sensors Quantitative Measurement of Eyestrain on 3D Stereoscopic Display Considering
More informationReal-Time Graphics Architecture
RealTime Graphics Architecture Kurt Akeley Pat Hanrahan http://www.graphics.stanford.edu/courses/cs448a01fall About Kurt Personal history B.E.E. Univeristy of Delaware, 1980 M.S.E.E. Stanford, 1982 SGI
More informationLecturer Athanasios Nikolaidis
Lecturer Athanasios Nikolaidis Computer Graphics: Graphics primitives 2D viewing and clipping 2D and 3D transformations Curves and surfaces Rendering and ray tracing Illumination models Shading models
More informationCSE 4392/5369. Dr. Gian Luca Mariottini, Ph.D.
University of Texas at Arlington CSE 4392/5369 Introduction to Vision Sensing Dr. Gian Luca Mariottini, Ph.D. Department of Computer Science and Engineering University of Texas at Arlington WEB : http://ranger.uta.edu/~gianluca
More informationProspective Novel 3D Display Technology Development
1 Prospective Novel 3D Display Technology Development Yasuhiro Takaki Institute of Engineering Tokyo University of Agriculture and Technology TUAT 2 Tokyo University of Agriculture and Technology http://www.tuat.ac.jp
More informationHuman factors and performance considerations of visual spatial skills in medical context tasks
Graduate Theses and Dissertations Iowa State University Capstones, Theses and Dissertations 2015 Human factors and performance considerations of visual spatial skills in medical context tasks Marisol Martinez
More informationComputer Vision Lecture 17
Computer Vision Lecture 17 Epipolar Geometry & Stereo Basics 13.01.2015 Bastian Leibe RWTH Aachen http://www.vision.rwth-aachen.de leibe@vision.rwth-aachen.de Announcements Seminar in the summer semester
More informationComputer Vision Lecture 17
Announcements Computer Vision Lecture 17 Epipolar Geometry & Stereo Basics Seminar in the summer semester Current Topics in Computer Vision and Machine Learning Block seminar, presentations in 1 st week
More informationInline Computational Imaging: Single Sensor Technology for Simultaneous 2D/3D High Definition Inline Inspection
Inline Computational Imaging: Single Sensor Technology for Simultaneous 2D/3D High Definition Inline Inspection Svorad Štolc et al. svorad.stolc@ait.ac.at AIT Austrian Institute of Technology GmbH Center
More informationVision Screening Products Catalog
Vision Screening Products Catalog 800.344.9500 stereooptical.com New online store: stereoptical.com/store THE NEW GENERATION ALL-IN-ONE SMART VISION SCREENER Easily control your test by choosing your conditions
More informationHigh-Fidelity Augmented Reality Interactions Hrvoje Benko Researcher, MSR Redmond
High-Fidelity Augmented Reality Interactions Hrvoje Benko Researcher, MSR Redmond New generation of interfaces Instead of interacting through indirect input devices (mice and keyboard), the user is interacting
More informationThanks to Chris Bregler. COS 429: Computer Vision
Thanks to Chris Bregler COS 429: Computer Vision COS 429: Computer Vision Instructor: Szymon Rusinkiewicz TA: Linjie Luo smr@cs.princeton.edu linjiel@cs.princeton.edu Course web page http://www.cs.princeton.edu/courses/archive/fall09/cos429/
More informationComp/Phys/Apsc 715. Example Videos. Pop Quiz! 1/30/2014
Comp/Phys/Apsc 715 Graphics System, Human Visual System Characteristics, and Illusions: Lighting, Surface Perception, Texture, Acuities, Receptive Fields, Brightness Illusions, Simultaneous Contrast, Constancy
More informationUnit 3: Optics Chapter 4
Unit 3: Optics Chapter 4 History of Light https://www.youtube.com/watch?v=j1yiapztlos History of Light Early philosophers (Pythagoras) believed light was made up of tiny particles Later scientist found
More informationBinocular Stereo Vision
Binocular Stereo Vision Properties of human stereo vision Marr-Poggio-Grimson multi-resolution stereo algorithm CS332 Visual Processing Department of Computer Science Wellesley College Properties of human
More information