Visual computation of lightness in simple and complex images

Slides:



Advertisements
Similar presentations
Lecture 16 – Lightness Perception
Advertisements

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,
Effective Color Contrast: Designing for People with Partial Sight and Color Deficiencies CSE 491 Michigan State University Fall 2007 Kraemer.
Lightness and Retinex Theory Journal of the Optical Society of America Vol. 61, No. 1 Jan Edwin H. Land & John J. McCann.
776 Computer Vision Jan-Michael Frahm, Enrique Dunn Fall 2014.
Seeing is NOT Believing: Color Magic Seeing is NOT Believing: Visual and Color Magic Foothill Technology High School.
Version 0.10 (c) 2007 CELEST VISI  N Star Light, Star Bright, Let’s Explore Light How You Perceive Light How many black dots can you count?
Lecture 30: Light, color, and reflectance CS4670: Computer Vision Noah Snavely.
Apparent Greyscale: A Simple and Fast Conversion to Perceptually Accurate Images and Video Kaleigh SmithPierre-Edouard Landes Joelle Thollot Karol Myszkowski.
Perception of illumination and shadows Lavanya Sharan February 14th, 2011.
What is color for?.
Perception of illumination and shadows Lavanya Sharan February 14th, 2011.
Color, lightness & brightness Lavanya Sharan February 7, 2011.
Colour Constancy T.W. Hung. Colour Constancy – Human A mechanism enables human to perceive constant colour of a surface over a wide range of lighting.
Objectives Learn to shade objects so their images appear three- dimensional Learn to shade objects so their images appear three- dimensional Introduce.
Dynamic Range Compression & Color Constancy Democritus University of Thrace
Types of Color Theories 1. 1.Subtractive Theory The subtractive, or pigment theory deals with how white light is absorbed and reflected off of colored.
Design Elements Form & Value To understand and apply the design elements Form & Value.
UNIT THREE PACKAGE PRINTING AND DECORATING 单元三 包装印刷与装潢 Lesson 9 Color 颜色 Introduction Color Perception Color Terminology Viewing Color.
Vision: change, lightness, acuity
1 Introduction to Computer Graphics – CGS-1586C Spring Quarter 2011 Instructor: Amanda Dickinson Tues/Thurs from 6:00PM to 7:50 PM.
Value Scale & Monochromatic Scale
G52IIP, School of Computer Science, University of Nottingham 1 Summary of Topic 2 Human visual system Cones Photopic or bright-light vision Highly sensitive.
Perceptual Constancy Module 19. Perceptual Constancy Perceiving objects as stable or constant –having consistent lightness, color, shape, and size even.
Version 0.10 (c) 2007 CELEST VISI  N BRIGHTNESS CONTRAST CLASSROOM PRESENTATION PRE-EXPERIMENT How many black dots can you count?
Lightness filtering in color images with respect to the gamut School of Electrical Engineering and Computer Science Kyungpook National Univ. Fourteenth.
Shade & Shadow Figure 1-3, Page 11
Chapter 9: Perceiving Color. Figure 9-1 p200 Figure 9-2 p201.
To Students in Psychology Some of you have used part of at least one Trinity Day learning about color perception on your own. The internet is.
Computer Graphics: Illumination
Version 0.10 (c) 2007 CELEST VISI  N BRIGHTNESS CONTRAST CLASSROOM PRESENTATION PRE-EXPERIMENT How many black dots can you count?
Illumination and Shading. Illumination (Lighting) Model the interaction of light with surface points to determine their final color and brightness OpenGL.
From local motion estimates to global ones - physiology:
Set Design.
Value (Element of Art) is the lightness and darkness of a color.
Shading CS 465 Lecture 4 © 2004 Steve Marschner • 1.
Lecture 6 - Chapter 7 Colour Vision Stimulus (what is colour?)
Perception.
Prof. Riyadh Al_Azzawi F.R.C.Psych
3D Graphics Rendering PPT By Ricardo Veguilla.
Presentation on Gestalt Theory for Visual Design-
Image Enhancement.
Computer Vision Lecture 4: Color
ID 242 Portfolio Development
Michael Tanaya , Hua ming Chen
DDP II Aim: What is Visual Analysis in Reverse Engineering?
T F L O S S Value Means light and dark.
Forging new generations of engineers
The Dynamic Range of Human Lightness Perception
1/8/13 Pick-up a slide packet from the front counter (in warm-up page basket). Make sure you have something with which to write (pen/pencil) Write your.
Visual Design Principles and Elements
Prof. Riyadh Al_Azzawi F.R.C.Psych
How you perceive your surroundings
Perception We have previously examined the sensory processes by which stimuli are encoded. Now we will examine the ultimate purpose of sensory information.
Visual neuroscience: Illuminating the dark corners
Space and Value.
Physical Properties of light
Color Theory.
The Element of Value The lightness and darkness of tones (grays) and colors to make objects appear 3-dimensional.
Color Model By : Mustafa Salam.
Color Models l Ultraviolet Infrared 10 Microwave 10
Grey Level Enhancement
F. Y. B. A. G1: General Psychology (TERM I)
Prof. Riyadh Al_Azzawi F.R.C.Psych
Short-Term Memory for Figure-Ground Organization in the Visual Cortex
A good logo is: Simple Well Drawn Interesting.
CS 480/680 Computer Graphics Shading.
Stereoscopic Surface Perception
Best Practices for Great Presentations
Jala Rizeq Data Visualization January 31st, 2019
Presentation transcript:

Visual computation of lightness in simple and complex images Alan Gilchrist National Science Foundation: BCS-9906747 Public Health Service: GM 60826-02

Perceived white, gray, or black shade of a surface. What is Lightness? Perceived white, gray, or black shade of a surface.

The problem of lightness constancy: Adelson’s checkered shadow These two squares are identical

Luminance is ambiguous Any absolute luminance can appear as any shade of gray

1948 - Wallach’s solution: Relative luminance

1948 - Wallach’s solution: 1 5 50 10 Relative luminance Disks appear equal when luminance ratios are equal

Near condition Far condition

without an anchoring rule, But, without an anchoring rule, luminance ratios are also ambiguous 1 5 This local luminance ratio. . . . . . is consistent with any of these:

THE ANCHORING PROBLEM Given: the scale of A range of luminances WHITE GRAY BLACK SELF- LUMINOUS the scale of perceived gray shades Given: A range of luminances in the image How to map these onto….

Two proposed anchoring rules Wallach, Land & McCann SELF- LUMINOUS WHITE Highest luminance Rule Average luminance Rule GRAY Helson, Buchsbaum Gray world assumption BLACK

Which rule is correct? Another rule Bipolar anchoring Koffka,Rock SELF- LUMINOUS Koffka,Rock WHITE Bipolar anchoring GRAY BLACK Which rule is correct?

Challenge: pit these rules against each other in the simplest possible image

Two surfaces of different gray that fill the entire visual field. What is a simple image? Heinemann: Disk/annulus in a dark room Gilchrist: Disk/annulus is too complex Simplest image: Two surfaces of different gray that fill the entire visual field. Wallach: "Opaque colors which deserve to be called white or gray, in other words ‘surface colors,’ will make their appearance only when two regions of different light intensity are in contact with each other..."

1 2 3

Highest Luminance Rule wins Anchoring under minimal conditions: Two surfaces fill entire visual field Physical Stimulus 5.5 2.5 4.5 9.5 Li & Gilchrist, 1999 Appearance Highest Luminance Rule wins

Highest luminance rule: The highest luminance within a framework appears white and darker regions are computed relative to this value.

Three rules of anchoring in simple images: Physical Stimulus Appearance 1. Highest Luminance Rule Highest luminance appears white Physical Stimulus Appearance 2. Area Rule. The larger the lighter 3. Scale Normalization Rule. The perceived range of grays tends toward that between black and white. (30:1) Physical Stimulus Appearance

Two problems for the highest luminance rule Self-luminosity perception Upward induction/downward induction problem

What color is the ceiling? Highest luminance rule fails

When the luminance difference between two adjacent regions increases: Upward induction/downward induction problem When the luminance difference between two adjacent regions increases: Does the darker one appear to get darker? Or does the lighter one appear to get lighter still? Downward induction Upward induction

Downward induction Upward induction

The answer lies in relative area

Method Nine stimulus domes: Each viewed by a different group of 15 subjects Matches made from immediate memory using a Munsell chart

Standard Deviations Perceived Log reflectance White Gray Black 1.89 1.69 1.49 Gray 1.29 1.09 0.89 0.69 Black 0.49

Degrees of dark gray Perceived Log reflectance White Gray Black 1.85 1.65 1.45 Gray 1.25 1.05 0.85 0.65 Black 0.45 50 100 150 200 250 300 350 Degrees of dark gray

lightens as it gets larger The area rule: The darker region lightens as it gets larger As the darker region becomes very large, the lighter region appears first super-white, and then self-luminous..

2 degree square 7 x 9 degree rectangle

WHITE GRAY BLACK 1 8 6 4 2 3 9 1 3 2 TARGET LUMINANCE (cd /m ) 2 4 6 8 1 3 TARGET LUMINANCE (cd /m ) % LUMINOSITY REPORTS 50% BACKGROUND LIGHTNESS 3 1 9 2 TARGET LUMINANCE (cd /m ) WHITE GRAY BLACK

Theoretical significance: Inconsistent with inverse optics Neurally plausible

Scale normalization rule: The perceived range of grays within a framework tends toward that between black and white If the range is truncated (less than 30:1), expansion occurs. Coefficient of expansion proportional to the degree of truncation. The expansion shows up at the bottom of the range, not the top, which is anchored at white. Similar to MacLeod and Brown’s gamut expansion

EXPANSION COMPRESSION Disk/Ganzfeld Percentage Rescaling 160 140 120 100 4.8 80 60 40 1 10 40 Stimulus: Disk/Ganzfeld Range full Gilchrist & Bonato (1995)

Three rules of anchoring in simple images: Physical Stimulus Appearance 1. Highest Luminance Rule Highest luminance appears white Physical Stimulus Appearance 2. Area Rule. The larger the lighter 3. Scale Normalization Rule. The perceived range of grays tends toward that between black and white. (30:1) Physical Stimulus Appearance

What about complex images?

What is the relationship between simple and complex images? Contrast era: Findings from simple images can be directly applied to complex images Arend (1994): Disk/annulus displays are too simple to tell us anything useful about lightness perception. Gilchrist: Simple and complex images are related in a systematic way. Applicability assumption Co-determination principle

The applicability assumption: Rules of lightness computation in simple images can be applied to frameworks embedded within complex images

The co-determination principle Lightness is determined by computations both in the relevant framework and in adjacent and/or superordinate frameworks Lajos Kardos ... brilliant but largely-unknown Gestalt psychologist.

Applicability assumption: Highest luminance rule 2. Area function 3. Scale normalization

A B Corrugated Mondrian (Adelson)

Now for a Live Demo!

LOG T/H LOG PERCEIVED REFLECTANCE WHITE WHITE GRAY BLACK GLOBAL LOCAL 2 WHITE WHITE GLOBAL 1.8 1.6 1.4 GRAY 1.2 1 LOCAL 0.8 BLACK 0.6 0.4 -1.6 -1.4 -1.2 -1 -0.8 -0.6 -0.4 -0.2 LOG T/H

Applicability assumption: Highest luminance rule 2. Area function 3. Scale normalization

Applicability assumption: Highest luminance rule 2. Area function 3. Scale normalization

Frameworks of illumination exist in the visual environment Photo: Cartier-Bresson

Cartier-Bresson

Cartier-Bresson

We can explore frameworks using a probe disk of constant luminance:

Conclusions: Lightness computation in simple images described by three rules: 3. Scale Normalization Rule. 2. Area Rule. Physical Stimulus Appearance 1. Highest Luminance Rule

Conclusions: These rules can be applied to frameworks embedded with complex images (the applicability assumption) GLOBAL LOCAL Lightness is co-determined by computations in multiple frameworks.

Thank You

Standard Deviations Perceived Log reflectance White Gray Black 1.89 PR=(100-Ad)/50 x (Lt/Lh x 90%)+(Ad-50)/50 x 90% Ad - Area of darker region Lt - Luminance of target Lh - Highest luminance 1.69 1.49 Gray 1.29 1.09 0.89 0.69 Black 0.49

Visual Field