SPECULAR FLOW AND THE PERCEPTION OF SURFACE REFLECTANCE

Slides:



Advertisements
Similar presentations
Presentation of Illusory Haptic Shape via Contact Location Trajectory on the Fingerpad Hanifa Dostmohamed and Vincent Hayward Haptics Laboratory, Center.
Advertisements

Dynamic Occlusion Analysis in Optical Flow Fields
Depth from Structured Light II: Error Analysis
SPECULAR FLOW AND THE PERCEPTION OF SURFACE REFLECTANCE Stefan Roth * Fulvio Domini † Michael J. Black * * Computer Science † Cognitive and Linguistic.
Illumination Model How to compute color to represent a scene As in taking a photo in real life: – Camera – Lighting – Object Geometry Material Illumination.
Perception of illumination and shadows Lavanya Sharan February 14th, 2011.
Perception of illumination and shadows Lavanya Sharan February 14th, 2011.
1 Lecture 9 Lighting Light Sources Reflectance Camera Models.
1 Lecture 11 Scene Modeling. 2 Multiple Orthographic Views The easiest way is to project the scene with parallel orthographic projections. Fast rendering.
Computer Vision Spring ,-685 Instructor: S. Narasimhan PH A18B T-R 10:30am – 11:50am Lecture #13.
More Perception Introduction to Cognitive Science.
Does the Quality of the Computer Graphics Matter When Judging Distances in Visually Immersive Environments? Authors: Thompson, Creem-Regehr, et al. Presenter:
Radiance Workshop, October 1-2, 2007 Perceived shininess and rigidity - Measurements of shape-dependent specular flow of rotating objects Katja Doerschner.
Goal and Motivation To study our (in)ability to detect inconsistencies in the illumination of objects in images Invited Talk! – Hany Farid: Photo Forensincs:
CSE 185 Introduction to Computer Vision Stereo. Taken at the same time or sequential in time stereo vision structure from motion optical flow Multiple.
Color and Brightness Constancy Jim Rehg CS 4495/7495 Computer Vision Lecture 25 & 26 Wed Oct 18, 2002.
1Ellen L. Walker 3D Vision Why? The world is 3D Not all useful information is readily available in 2D Why so hard? “Inverse problem”: one image = many.
Illumination Model How to compute color to represent a scene As in taking a photo in real life: – Camera – Lighting – Object Geometry Material Illumination.
Tal Amir Advanced Topics in Computer Vision May 29 th, 2015 COUPLED MOTION- LIGHTING ANALYSIS.
DAILY QUESTION March 12, State the law of reflection.
Basics Reflection Mirrors Plane mirrors Spherical mirrors Concave mirrors Convex mirrors Refraction Lenses Concave lenses Convex lenses.
Computer Graphics Lecture 30 Mathematics of Lighting and Shading - IV Taqdees A. Siddiqi
Date of download: 6/29/2016 The Association for Research in Vision and Ophthalmology Copyright © All rights reserved. From: Judging the shape of.
Computer Graphics: Illumination
Stimuli were presented on a 17 inch monitor (in a dimly lit room), operating at 60 Hz with a resolution of 1280 x Two objects of the same type (teapot.
Computer vision: geometric models Md. Atiqur Rahman Ahad Based on: Computer vision: models, learning and inference. ©2011 Simon J.D. Prince.
Visual Estimation of Three- and Four-body Center of Mass Jay Friedenberg Psychology Department Manhattan College Jay Friedenberg Psychology Department.
Motion of specularities on smooth random surfaces
Advanced Computer Graphics
From: Disparity sensitivity in man and owl: Psychophysical evidence for equivalent perception of shape-from-stereo Journal of Vision. 2010;10(1):10. doi: /
Think-Pair-Share What visual or physiological cues help us to perceive 3D shape and depth?
Unit 4: Perceptual Organization and Interpretation
Photorealistic Rendering vs. Interactive 3D Graphics
Journal of Vision. 2008;8(13):9. doi: / Figure Legend:
UCMAS: A cognition tool
Mental Rotation of Naturalistic Human Faces
From: Occlusion-related lateral connections stabilize kinetic depth stimuli through perceptual coupling Journal of Vision. 2009;9(10):20. doi: /
Piercing of Consciousness as a Threshold-Crossing Operation
Contribution of spatial and temporal integration in heading perception
Bram-Ernst Verhoef, Rufin Vogels, Peter Janssen  Neuron 
Common Classification Tasks
Neural Correlates of Shape from Shading
Within a Mixed-Frequency Visual Environment
Categorizing sex and identity from the biological motion of faces
Volume 60, Issue 4, Pages (November 2008)
Illumination Model How to compute color to represent a scene
Bram-Ernst Verhoef, Rufin Vogels, Peter Janssen  Neuron 
A preference for global convexity in local shape perception
Satoru Suzuki, Marcia Grabowecky  Neuron 
Walking Modulates Speed Sensitivity in Drosophila Motion Vision
Are there separate explicit and implicit memory systems?
A Vestibular Sensation: Probabilistic Approaches to Spatial Perception
Depth Affects Where We Look
Visual Motion and the Perception of Surface Material
Colin J. Palmer, Colin W.G. Clifford  Current Biology 
Fear Conditioning in Humans
Satoru Suzuki, Marcia Grabowecky  Neuron 
Multi-level Crowding and the Paradox of Object Recognition in Clutter
Dynamic Coding for Cognitive Control in Prefrontal Cortex
Modality-Independent Coding of Spatial Layout in the Human Brain
Neural Mechanisms of Visual Motion Perception in Primates
Walking Modulates Speed Sensitivity in Drosophila Motion Vision
Volume 14, Issue 3, Pages (February 2004)
What we perceive is reality
Optics 14.1 Light tends to travel in straight lines. 14.2
John T. Serences, Geoffrey M. Boynton  Neuron 
CS 480/680 Computer Graphics Shading.
Occlusion and smoothness probabilities in 3D cluttered scenes
Physical Problem of Simultaneous Linear Equations in Image Processing
Judging Peripheral Change: Attentional and Stimulus-Driven Effects
Presentation transcript:

SPECULAR FLOW AND THE PERCEPTION OF SURFACE REFLECTANCE SA73 SPECULAR FLOW AND THE PERCEPTION OF SURFACE REFLECTANCE Stefan Roth* Fulvio Domini† Michael J. Black* BROWN UNIVERSITY *Computer Science †Cognitive and Linguistic Sciences I. Motivation and Introduction III. Experiment 1 Results Single motion pattern with either diffuse (only dots) or specular reflection (dots or lines) visible 10 random convex trials per stimulus and subject, 10 random concave trials, 8 subjects overall Subjects view each stimulus for at most 2.5 seconds before the animation stops. Subjects make forced choice between convex and concave shapes Specular flow: Dense (or semi-dense) image motion of specular reflections (as opposed to just specular highlights). Specularities have been shown to be used by humans for: Judging material properties (Hartung & Kersten, VSS ’01) Static stereo perception (Blake & Bülthoff, Nature 1990) Open question: Is specular flow used in the perception of 3D shape? Figure 1: Objects with diffuse and specular reflections. Here: We study in the absence of spatial cues whether specular flow improves discrimination capabilities between convex and concave shapes when combined with diffuse motion. Our results show that combining specular and diffuse flow does improve this discrimination capability. No significant level of distinction between convex and concave shapes from just diffuse reflection Specular reflections cause a strong bias towards concave. Specular motion patterns show significant differences between the perception of convex and concave. II. Basic Stimuli convex concave Using a sphere as basic shape Exterior of sphere as convex shape Interior (“bowl”) as concave shape. Diffuse reflection is shown using random-dot displays with uniform dot density. Specular reflections are displayed using random-dot or random-line displays. Specularities originate from a fixed illumination sphere around the shape. The camera is rotating around the object and views the scene through a round aperture. Stimuli are generated in real-time using hardware-accelerated OpenGL (Nvidia GeForce). IV. Experiment 2 Results * = specular and diffuse geometry agree Diffuse and specular motion patterns are superimposed Both can either be convex or concave (i.e., they can agree or disagree) The number of trials per stimulus, the number of subjects, and the duration of trials remains unchanged. Subjects still make a forced choice between convex and concave shape. diffuse * * * specular * The combination of diffuse and specular flow allows significant discrimination between convex and concave shapes. Convex/concave combinations show a strong bias towards convex. r=1 d=5 camera random dots on illumination sphere d=25 random dots on surface aperture Figure 2: Motion traces showing the flow of random dots corresponding to diffuse and specular motion. Observations References V. Conclusions Specular flow applied to random dots or random lines is not perceived as “shiny”. As the illumination sources become more “natural” the object is increasingly perceived as “shiny”. The further away the illumination sphere is, the less reflective the objects seem. Hartung, B., & Kersten, D. Distinguishing Shiny from Matte. Vision Sciences 2002. Blake, A., & Bülthoff, H. Does the brain know the physics of specular reflection? Nature, 343, 1990. Simple specular flow patterns are not perceived as “shiny”, but nevertheless significantly improve the ability to distinguish convex and concave shapes when combined with diffuse motion cues. Acknowledgements This material is based upon work supported by the National Science Foundation under Grant No. 78441. Figure 3: Scene setup (left – convex shape, right – concave shape).