Colour appearance and the interaction between texture and colour
|
|
- Posy Blankenship
- 5 years ago
- Views:
Transcription
1 Colour appearance and the interaction between texture and colour Maria Vanrell Martorell Computer Vision Center de Barcelona
2 2 Contents: Colour Texture Classical theories on Colour Appearance Colour and Texture for computer vision
3 3 Goal: How to deal with computer vision problems where we need to computationally represent the properties of colour-textured surfaces from a perceptual point of view?
4 4 Example 1: ATIC (Automatic Tile Classification) Goal: Automatic classification of porcelanic tiles where differences are due to small differences on texture and colour of uncontrolled conditions on the production process.
5 5 Example 2: CAOPPP: Colour Adjustment On Printed Paper Production First design Current design Last design Goal: Automatic adjustment of inks to get the same appearance on printed paper designs Improving time / similarity
6 6 Production Process: Color A + Texture 1 Texture 3 Texture 2 Texture 1 Color B + Texture 2 Color C + Texture 3 Printed Paper Platen 3 Platen 2 Platen 1 Neutral Paper Color C Color B Color A
7 7 In both cases, ATIC and CAOPPP, we need a computational representation of colour and texture that behaves as human perception does...
8 8 Questions to answer, how to work computationally with. COLOUR, TEXTURE, AND BOTH at the same time?
9 9 Questions to answer, how to work computationally with. COLOUR, TEXTURE, AND BOTH at the same time?
10 10 COLOUR, it has been deeply studied in Physics and there are a general agreement and a wide range of different spaces (RGB, HSI, CYM, ) to represent it and a lot of standard tools (Colorimeters, Spectroradiometers, Spectrophotometers, ) to measure colour in standard spaces (XYZ, CIELAB, CIELUV, CIECAM, )
11 11 Colour-matching experiment models the human colour perception and becomes the basis for the modern colour science (Wright 1929, Guild 1931, CIE 1931, CIE 1964, CIE 1971) 3 Primary lights Test light (monochromatic) Image taken from B. Wandell - SID Color Tutorial Notes
12 12 Conclusion: Human colour perception can be represented by a three-dimensional space given by three basic colours Y X Z that coincides with the number of different types of cones in the human retina
13 13.therefore, it is easy to build a computational representation of the colour property of a point using a three-dimensional numerical vector g ( X, Y, Z) ( L, u, v)k f ( R, G, B) ( H, L, S)K (Device-Independent) (Device-Dependent) f, g : Represent colour-conversion transformations that can be linear or non-linear.
14 14 There are different types of colour spaces: Uniforms (CIELAB, CIELUV) Device-dependent (RGB, CMY) With perceptual dimensions (HLS, HSV, ) Based on Phisiologycal evidences (Opponent) Distance Similarity ( R, G, B) = ( λ1r p, λ2gp, λ3b p ) ( R p, G p, B p ) primaries H V : : Hue Intensity O : 1 O O 2 3 L : S : Luminance Saturation Intensity : Red Green : Blue Yellow
15 15 Questions to answer, how to work computationally with. COLOUR, TEXTURE, AND BOTH at the same time?
16 16 Questions to answer, how to work computationally with. COLOUR, TEXTURE, AND BOTH at the same time?
17 17 TEXTURE, there is not an accepted definition for this visual cue, it is the property of some surfaces
18 18 We are referring to the appearance of texture images projected on the retina from a surface, not to physical/reflectance properties of a surface Yes Different texture images corresponding to the same physical surface α Not δ Images taken from
19 19 Interesting properties of texture images Existence of a textural primitive Existence of a window that is invariant to translations + Regular + Random Existence of a texture scale or texture resolution
20 20 But, there is a lack of an accepted representation R Y COLOUR Colour Science B TEXTURE? G X??? Z PSYCHOLOGY?
21 21 Interesting Overview: Theories of Visual Texture Perception [J.R. Bergen. 1991]
22 22 Theories of texture perception in psychology: B. Julesz J. Beck B. Julesz Global representation Local entities representation J. Beck Texton theory Spatial Frequency Channels
23 23 Texton theory [Julesz 81] Texture discrimination is based on differences on texton densities. Textons are visible local features that allows to segregate textures and which are considered the basic elements for preattentive perception. The textons are: Blobs, elongated or rounded and its attributes (orientation, length, width and colour). Terminators (ends of elongated blobs). Crossings (elongated blobs intersection).
24 24 Differences between them can be easily represented by differences in their texton/attributes densities
25 25 however, texton theory does not explain how to represent differences between these two: Densities of textons are identical for these two textures!! The differences rely on Emergent patterns!!!
26 26 however, texton theory does not explain how to represent differences between these two: Densities of textons are identical for these two textures!! The differences rely on Emergent patterns PERCEPTUALLY GROUPED PATTERNS
27 27 Different stimulation of spatial frequency channels Texture representation can not only depend on first-order statistics of textons, but also on the orientation, size and brightness of emergent structures [Beck-86] The emergent structures can be the result of a convolution of the stimulus with a receptive field. I F θ F θ = ο 0 ο 45 ο 90
28 28 Conclusion: Textures could be represented by the densities of both elements: Blob attributes, and the attributes of their emergent patterns
29 29
30 30 Representation based on blob attributes Dark blobs (Non-elongated, High contras Light blobs (Non-elongated, High contrast) Elongated blobs (Orientation=45º, Low contrast) Elongated Blobs (Random orientations High contrast)
31 31
32 32 Representation based on attributes of emergent patterns Circles (Bright, Low Structure) Striped (Dark,High Structure) Chequered (Dark, Structured) Circles (Bright, Structured)
33 33 Multi-scale approaches are probably those that can better consider both aspects...
34 34 e.g. Computational model for preatentive texture perception [Malik&Perona-91] Family of filters I * f ( σ ) I * f ( σ ) I * f ( σ ) I * f ( σ ) I * f ( σ ) I * f ( σ ) Differences of offset gaussians scales I I * f ( σ ) I * f ( σ ) I * f ( σ ) I * f ( σ ) I * f ( σ ) I * f ( σ ) * f ( σ ) I * f ( σ ) I * f ( σ ) I * f ( σ ) I * f ( σ ) I * f ( σ ) Non linear mechanism
35 35 It combines both approaches: Statistical measurements of image blobs from: Small scales (do not break image structure) after applying blob and bar detectors. Statistical measurements of emergent blobs from: Large scales (capturing emergent properties) after applying the same blob and bar detectors. and it still probably needs more complex high-level operators as: corner, t-junctions, x-junctions, circle detectors,.
36 36 Now, we have an idea on how to represent image textures computationally, but still two questions remain to be answered What is the true dimension of the texture space? (There can be a lot of redundancy when we use a multichannel representation of images)?? Which are they???
37 37 There are some hipothesis: From psychophysical data [Rao et al. - 96] Dimension Reduction PCA / MDS... Granularity Contrast / Direccionalit Randomness From computational representations [Vanrell et al. - 96] Dimension Reduction MDS... Orientation Scale Contrast
38 38 A lot of work remain to be done on gray-level texture representation...
39 39 A lot of work remain to be done on gray-level texture representation and color is not considered yet.
40 40 Questions to answer, how to work computationally with. COLOUR, TEXTURE, AND BOTH at the same time?
41 41 Questions to answer, how to work computationally with. COLOUR, TEXTURE, AND BOTH at the same time?
42 42 Contents: Colour Texture Classical theories on Colour Appearance Colour and Texture for computer vision
43 43 Question: Are colour and texture independent visual cues?
44 44 IN COLORIMETRY, a lot of different models to built three dimensional perceptual colour spaces (XYX, CIELAB, CIELUV,..) have been developed, and a lot of DEVICES based on the integration of reflected light from a surface area.
45 45 IN COMPUTER VISION, a lot of measurements are based on histograms of the digital image represented by the RGB space.
46 46 But, some both approaches are not enough!!!
47 47 Red (255,0,0) Cyan (0,255,255) Green (0,255,0) Magenta (255,0,255) Blue (0,0,255) Yellow (255,255,0) Grey (128,128,128) Black (0,0,0) White (255,255,255) The same representation for colorimetrist devices and similar histogram statistics!!
48 48 Conclusion: We need to consider the effects of localisation and grouping of colours to deal with colour appearance
49 49 In colorimetry the colour appearance phenomena is a current goal: Mark D. Fairchild, Color Appearance Models Addison-Wesley 1998
50 50 While de CIE system of colorimetry has proven to be exptremely useful, i is important to remember that it has limitations. Most of its limitations are inherent in the design of a system of tristimulus values based on color matching. Such a system can accurately predict color matches for an average observer, but it incorporates none of the information necessary for specifying the color appearance of those matching stimuli. Such is the realm of color appearance models. Tristimulus values can be considered as a nominal scale for color. They can be used to state whether two stimuli match. The specification of color differences requires interval scales, and the description of color appearance requires interval scales (for hue) and raito scales (for brightness, lightness, colorfulness, and chroma). Additional information is needed, in conjunction with tristimulus values, to derive these more sophisticated scales. [Mark D. Fairchild, Color appearance Models, p.163, Addison Wesley, 98]
51 51 In Computer Vision spatial frequencies are usually processed separately in RGB channels of images. Attention to the following effects!!
52 52 Given three different channels...
53 53 C1 C2 C3 C1,C2,C3 C1,C1,C2 C3,C1,C1 C2,C3,C2 C1*G4,C2,C3 C1,C2*G4,C3 C1,C2,C3*G4 C1*G4,C2*G4,C3*G4
54 54 R G σ G G σ B G σ
55 55 R G σ G G σ B G σ
56 56
57 57 Direct extension of known measurements for gray level images to the colour image channels seems not to be a good solution Let s go deeply into the colour appearance phenomena...
58 58 COLOUR APPEARANCE PHENOMENA Simultaneous Contrast: Two identical gray patches presented on different backgrounds appear distinct. The black background causes the gray patch to appear lighter, while the white background causes the gray patch to appear darker.
59 59 Classical examples:
60 60 COLOUR APPEARANCE PHENOMENA Crispening: Is the increase in perceived magnitude of color differences when the background on which the two stimuli are compared is similar in color to the stimuli themselves.
61 61 COLOUR APPEARANCE PHENOMENA Spreading: Is the apparent mixture of a color stimulus with its surround. This effect is complete at the point of spatial fusion when the stimuli are no longer viewed as discrte but fuse into a single stimulus.
62 62 Why does it happen?
63 63 Color appearance depends on many different elements of the visual pathways, including the optical aberrations of the eye, light adaptation, and the neural computations that interpret objects, light sources, and istance relationships. [K-H Bäuml and Brian A. Wandell, Color appearance of Mixture Gratings, Vision Research, 96.
64 64 Causes: Light adaptation Chromatic aberration Neural computations to interpret objects... we will see the first two...
65 65 Light adaptation: Maximum range It is impossible to act on all this range simultaneously
66 66 Biological causes: Chromatic Aberration f Photoreceptor Mosaic does not present a uniform distribution
67 67 What does colorimetry propose?
68 68 Colour appearance models Define colour transformations that allow to go from colorimetric coordinates under one set of viewing conditions to colorimetric coordinates under a second set of viewing conditions VC 1 Colour appearancemodel ( X, Y, Z) ( X, Y, Z) VC 2
69 69 Colour viewing conditions: Spectral power distribution of the light source Luminance level Surround colour and relative luminance Background colour and relative luminance Image size and viewing distance Viewing geometry involves a large range of psychophysical experimentation.
70 70 Examples of colour appearance models: Bradford-Hunt 96S (simple model). Bradford-Hunt 96C (comprehensive model). CIECAM97s model. ZLAB colour appearance model.
71 71 but, in computer vision we usually do not know viewing conditions then, we propose a more computational approach...
72 72 To this end, let us go deeply on colour texture interactions
73 73 Image taken from
74 74
75 75
76 76
77 77
78 78
79 79 What happens? Human perception presents the colour induction phenomena that changes the colour appearance of a stimulus due to the influence of the scene contents.
80 80 Different types of colour induction: Colour adaptation Colour contrast Colour assimilation Others...
81 81 Different types of colour induction: Colour adaptation Colour contrast Colour assimilation Others... Colour Constancy ability of the HVS due to global Illuminant effects
82 82 Image taken from articles/colour.html
83 83 Image taken from articles/colour.html
84 84
85 85 Different types of colour induction: Colour adaptation Colour contrast Colour assimilation More related to colour-texture interactions
86 86 Colour Contrast Appears when the chromaticity of the test stimulus changes away from the chromaticity of the inducing stimulus. Colour Assimilation Appears when the chromaticity of the test stimulus changes towards the chromaticity of the inducing stimulus.
87 87 Colour Contrast S1 S2 TS TS P1 P2
88 88 Colour Contrast S1 S2 S2 S2 P1 TS TS S1 TS TS S1 P2 r+g+b=1 P1 P2 r+g+b=1
89 89 Colour Assimilation
90 90 Colour Assimilation r+g+b=1 r+g+b=1
91 91
92 92 Conclusion: Colour contrast & Colour assimilation seem to be complementary effects Question: When they occur?
93 93 Some experiments show a relationship between spatial frequency of patterns and colour induction [Pokorny et al-2001]
94 94 Some experiments shows a relationship between spatial frequency of patterns and colour induction [Pokorny et al-2001] Assimilation Threshold Frecuency 4cpd Contrast
95 95...Now, the question is: How to consider induction effects on a computational colour-texture representation?
96 96...Now, the question is: How to consider induction effects on a computational colour-texture representation? Assimilation Blurring operator e.g. I A, σ = I G σ Contrast Sharpening operator 2 I C, σ = I e.g ( I G ) ( I G ) I = σ + σ σ 2 2 x I y
97 97 With these two operators we can simulate human inspection of colour texture surfaces Looking from a long distance a short distance Assimilation or Blurring Contrast or Sharpening Applications
98 98 Colour-Texture perception as a vision process: Short distance perception: I = I I A, σ n = I G A, σ1 σ1 = M I G σ n Global colour representation that consider texture influence Long distance perception: I = I I C C, σ n = I = I 2, σ σ 1 1 M 2 σn I I Local blob attributes as a texture representation that considers colour influence
99 99 Proposed Schema for a: Computational approach to colour texture perception Blurr p q Blurring output Global Measurements Sharp... I Blurr p q Colour-Texture representation Sharp Blurr Sharp p... q Sharpening output Local Measurements Blob Segmentation p assimilation scales q assimilation scales
100 100 Observer distancie + Chromatic Assimilation d < d δ Original Image Chromatic Contrast - d < d δ
101 101 Colour Perceptual Tower
102 102 but, there are still some important problems to be solved before to have a complete colour-texture perceptual approach
103 103 Problem 1: Which are the parameters of the operators that fit with human behaviour on contrast and assimilation? Spatial-CIELAB, has been proposed for colour assimilation by [Wandell et al.-96 ] I A, σ = I 3 i= 1 G σ i σ i : Measured and tabulated from psychophysical experimentation
104 104 Spatial CIELAB: models human colour assimilation process as a set of gaussian filters in an opponent space f ωi = ω k exp x 2 + y 2 i 2 k = 1K3 σ k i i i : viewing distance factor : normalisation factor ki Example:
105 105 Convolution with perceptual kernels f = k i w i E i O1 w i σ i E i = k i 2 ( x + y e i 2 )/σ i O Where the parameters are: O
106 106 Model behaviour (50, 150, 250) (50, 150, 250) (250, 250, 0) (250, 250, 0) (250, 250, 0) (50, 150, 250) (130, 190, 149) (240, 245, 12) Mean values of gratings after perceptual blurring
107 107 APPLICATION TO COMPUTER VISION [Boukouvalas-Petrou 96) 1. Remove PSF from the camera sensor 2. Convert from RGB to XYZ standard 3. Convert from XYZ to opponent colour 4. Compute a Perceptual Blurring 5. Compute all the measurements
108 108 Problem: colour contrast has not been psychophysically measured yet
109 109 Problem 2: What colour space is the best to fit the parameters? Colour opponent space has presented good properties to fit the parameters for colour assimilation [Wandell et al.-96]
110 110 Opponent space: represents the HVS pathways. - γ 3 γ 2 B G γ 1 β I β 2 + β 1 R α 1 α 2 α 3 + Opp( p) = p Example: t RGB Opponent b y g r k w Opp(I)
111 111 OPPONENT-COLOUR MODEL O1= Y = 0.279X Y Z O2= RG = 0.279X Y Z O3= YB = 0.279X Y Z RG Y YB
112 112 Different blurring effects on the opponent-colour channels: (Y,RG,YB) (Y*G,RG,YB) (Y,RG*G,YB) (Y,RG,YB*G)
113 113 Problem 3: The sharpening operator needs to be improved for a true perceptual behaviour Some perceptual sharpening operators have been proposed by [Baldrich-01] I C, σ = I Interpolation( I) 2
114 114 A good sharpening operator has to consider colour contrast and mantain the structural properties of the image blobs. Example:
115 115 original Perceptual sharpening MS PhotoEditor moderate MS PhotoEditor strong Corel PhotoPaint
116 116 Example: blob segmentation with and without the perceptual sharpening original without Perceptually sharpened with
117 117 Proposed Schema for a: Computational approach to colour texture perception Blurr p q Blurring output Global Measurements Sharp... I Blurr p q Colour-Texture representation Sharp Blurr Sharp p... q Sharpening output Local Measurements Blob Segmentation p assimilation scales q assimilation scales
Lecture 1 Image Formation.
Lecture 1 Image Formation peimt@bit.edu.cn 1 Part 3 Color 2 Color v The light coming out of sources or reflected from surfaces has more or less energy at different wavelengths v The visual system responds
More informationColor Vision. Spectral Distributions Various Light Sources
Color Vision Light enters the eye Absorbed by cones Transmitted to brain Interpreted to perceive color Foundations of Vision Brian Wandell Spectral Distributions Various Light Sources Cones and Rods Cones:
More informationColor Appearance in Image Displays. O Canada!
Color Appearance in Image Displays Mark D. Fairchild RIT Munsell Color Science Laboratory ISCC/CIE Expert Symposium 75 Years of the CIE Standard Colorimetric Observer Ottawa 26 O Canada Image Colorimetry
More informationCHAPTER 1 Introduction 1. CHAPTER 2 Images, Sampling and Frequency Domain Processing 37
Extended Contents List Preface... xi About the authors... xvii CHAPTER 1 Introduction 1 1.1 Overview... 1 1.2 Human and Computer Vision... 2 1.3 The Human Vision System... 4 1.3.1 The Eye... 5 1.3.2 The
More informationMeet icam: A Next-Generation Color Appearance Model
Meet icam: A Next-Generation Color Appearance Model Why Are We Here? CIC X, 2002 Mark D. Fairchild & Garrett M. Johnson RIT Munsell Color Science Laboratory www.cis.rit.edu/mcsl Spatial, Temporal, & Image
More informationColour computer vision: fundamentals, applications and challenges. Dr. Ignacio Molina-Conde Depto. Tecnología Electrónica Univ.
Colour computer vision: fundamentals, applications and challenges Dr. Ignacio Molina-Conde Depto. Tecnología Electrónica Univ. of Málaga (Spain) Outline Part 1: colorimetry and colour perception: What
More informationThe ZLAB Color Appearance Model for Practical Image Reproduction Applications
The ZLAB Color Appearance Model for Practical Image Reproduction Applications Mark D. Fairchild Rochester Institute of Technology, Rochester, New York, USA ABSTRACT At its May, 1997 meeting in Kyoto, CIE
More informationELEC Dr Reji Mathew Electrical Engineering UNSW
ELEC 4622 Dr Reji Mathew Electrical Engineering UNSW Dynamic Range and Weber s Law HVS is capable of operating over an enormous dynamic range, However, sensitivity is far from uniform over this range Example:
More informationSpectral Adaptation. Chromatic Adaptation
Spectral Adaptation Mark D. Fairchild RIT Munsell Color Science Laboratory IS&T/SID 14th Color Imaging Conference Scottsdale 2006 Chromatic Adaptation Spectra-to-XYZ-to-LMS Chromatic adaptation models
More informationLecture #13. Point (pixel) transformations. Neighborhood processing. Color segmentation
Lecture #13 Point (pixel) transformations Color modification Color slicing Device independent color Color balancing Neighborhood processing Smoothing Sharpening Color segmentation Color Transformations
More informationColor and Shading. Color. Shapiro and Stockman, Chapter 6. Color and Machine Vision. Color and Perception
Color and Shading Color Shapiro and Stockman, Chapter 6 Color is an important factor for for human perception for object and material identification, even time of day. Color perception depends upon both
More informationLecture 12 Color model and color image processing
Lecture 12 Color model and color image processing Color fundamentals Color models Pseudo color image Full color image processing Color fundamental The color that humans perceived in an object are determined
More informationBrightness, Lightness, and Specifying Color in High-Dynamic-Range Scenes and Images
Brightness, Lightness, and Specifying Color in High-Dynamic-Range Scenes and Images Mark D. Fairchild and Ping-Hsu Chen* Munsell Color Science Laboratory, Chester F. Carlson Center for Imaging Science,
More informationIntroduction to color science
Introduction to color science Trichromacy Spectral matching functions CIE XYZ color system xy-chromaticity diagram Color gamut Color temperature Color balancing algorithms Digital Image Processing: Bernd
More informationChapter 5 Extraction of color and texture Comunicação Visual Interactiva. image labeled by cluster index
Chapter 5 Extraction of color and texture Comunicação Visual Interactiva image labeled by cluster index Color images Many images obtained with CCD are in color. This issue raises the following issue ->
More informationComputer Graphics. Bing-Yu Chen National Taiwan University The University of Tokyo
Computer Graphics Bing-Yu Chen National Taiwan University The University of Tokyo Introduction The Graphics Process Color Models Triangle Meshes The Rendering Pipeline 1 What is Computer Graphics? modeling
More informationOpponent Color Spaces
EE637 Digital Image Processing I: Purdue University VISE - May 1, 2002 1 Opponent Color Spaces Perception of color is usually not best represented in RGB. A better model of HVS is the so-call opponent
More informationDigital Image Processing
Digital Image Processing 7. Color Transforms 15110191 Keuyhong Cho Non-linear Color Space Reflect human eye s characters 1) Use uniform color space 2) Set distance of color space has same ratio difference
More informationColour Reading: Chapter 6. Black body radiators
Colour Reading: Chapter 6 Light is produced in different amounts at different wavelengths by each light source Light is differentially reflected at each wavelength, which gives objects their natural colours
More informationCS635 Spring Department of Computer Science Purdue University
Color and Perception CS635 Spring 2010 Daniel G Aliaga Daniel G. Aliaga Department of Computer Science Purdue University Elements of Color Perception 2 Elements of Color Physics: Illumination Electromagnetic
More informationChapter 2 CIECAM02 and Its Recent Developments
Chapter 2 CIECAM02 and Its Recent Developments Ming Ronnier Luo and Changjun Li The reflection is for the colors what the echo is for the sounds Joseph Joubert Abstract The development of colorimetry can
More informationReading. 2. Color. Emission spectra. The radiant energy spectrum. Watt, Chapter 15.
Reading Watt, Chapter 15. Brian Wandell. Foundations of Vision. Chapter 4. Sinauer Associates, Sunderland, MA, pp. 69-97, 1995. 2. Color 1 2 The radiant energy spectrum We can think of light as waves,
More informationA New Time-Dependent Tone Mapping Model
A New Time-Dependent Tone Mapping Model Alessandro Artusi Christian Faisstnauer Alexander Wilkie Institute of Computer Graphics and Algorithms Vienna University of Technology Abstract In this article we
More informationColor, Edge and Texture
EECS 432-Advanced Computer Vision Notes Series 4 Color, Edge and Texture Ying Wu Electrical Engineering & Computer Science Northwestern University Evanston, IL 628 yingwu@ece.northwestern.edu Contents
More information3D graphics, raster and colors CS312 Fall 2010
Computer Graphics 3D graphics, raster and colors CS312 Fall 2010 Shift in CG Application Markets 1989-2000 2000 1989 3D Graphics Object description 3D graphics model Visualization 2D projection that simulates
More informationReprint (R30) Accurate Chromaticity Measurements of Lighting Components. Reprinted with permission from Craig J. Coley The Communications Repair depot
Reprint (R30) Accurate Chromaticity Measurements of Lighting Components Reprinted with permission from Craig J. Coley The Communications Repair depot June 2006 Gooch & Housego 4632 36 th Street, Orlando,
More informationCOLOR FIDELITY OF CHROMATIC DISTRIBUTIONS BY TRIAD ILLUMINANT COMPARISON. Marcel P. Lucassen, Theo Gevers, Arjan Gijsenij
COLOR FIDELITY OF CHROMATIC DISTRIBUTIONS BY TRIAD ILLUMINANT COMPARISON Marcel P. Lucassen, Theo Gevers, Arjan Gijsenij Intelligent Systems Lab Amsterdam, University of Amsterdam ABSTRACT Performance
More informationPattern recognition. Classification/Clustering GW Chapter 12 (some concepts) Textures
Pattern recognition Classification/Clustering GW Chapter 12 (some concepts) Textures Patterns and pattern classes Pattern: arrangement of descriptors Descriptors: features Patten class: family of patterns
More informationDigital Image Processing COSC 6380/4393. Lecture 19 Mar 26 th, 2019 Pranav Mantini
Digital Image Processing COSC 6380/4393 Lecture 19 Mar 26 th, 2019 Pranav Mantini What is color? Color is a psychological property of our visual experiences when we look at objects and lights, not a physical
More informationGame Programming. Bing-Yu Chen National Taiwan University
Game Programming Bing-Yu Chen National Taiwan University What is Computer Graphics? Definition the pictorial synthesis of real or imaginary objects from their computer-based models descriptions OUTPUT
More informationVisual Evaluation and Evolution of the RLAB Color Space
Visual Evaluation and Evolution of the RLAB Color Space Mark D. Fairchild Munsell Color Science Laboratory, Center for Imaging Science Rochester Institute of Technology, Rochester, New York Abstract The
More informationColor Content Based Image Classification
Color Content Based Image Classification Szabolcs Sergyán Budapest Tech sergyan.szabolcs@nik.bmf.hu Abstract: In content based image retrieval systems the most efficient and simple searches are the color
More informationLecture 11. Color. UW CSE vision faculty
Lecture 11 Color UW CSE vision faculty Starting Point: What is light? Electromagnetic radiation (EMR) moving along rays in space R(λ) is EMR, measured in units of power (watts) λ is wavelength Perceiving
More informationCSE 167: Lecture #6: Color. Jürgen P. Schulze, Ph.D. University of California, San Diego Fall Quarter 2012
CSE 167: Introduction to Computer Graphics Lecture #6: Color Jürgen P. Schulze, Ph.D. University of California, San Diego Fall Quarter 2012 Announcements Homework project #3 due this Friday, October 19
More informationOne image is worth 1,000 words
Image Databases Prof. Paolo Ciaccia http://www-db. db.deis.unibo.it/courses/si-ls/ 07_ImageDBs.pdf Sistemi Informativi LS One image is worth 1,000 words Undoubtedly, images are the most wide-spread MM
More informationSources, Surfaces, Eyes
Sources, Surfaces, Eyes An investigation into the interaction of light sources, surfaces, eyes IESNA Annual Conference, 2003 Jefferey F. Knox David M. Keith, FIES Sources, Surfaces, & Eyes - Research *
More informationCS452/552; EE465/505. Color Display Issues
CS452/552; EE465/505 Color Display Issues 4-16 15 2 Outline! Color Display Issues Color Systems Dithering and Halftoning! Splines Hermite Splines Bezier Splines Catmull-Rom Splines Read: Angel, Chapter
More informationImage Formation. Ed Angel Professor of Computer Science, Electrical and Computer Engineering, and Media Arts University of New Mexico
Image Formation Ed Angel Professor of Computer Science, Electrical and Computer Engineering, and Media Arts University of New Mexico 1 Objectives Fundamental imaging notions Physical basis for image formation
More informationThe process of a junior designer
For some fun The process of a junior designer https://medium.com/the-year-of-the-looking-glass/junior-designers-vs-senior-designers-fbe483d3b51e The process of a senior designer https://medium.com/the-year-of-the-looking-glass/junior-designers-vs-senior-designers-fbe483d3b51e
More informationThe Display pipeline. The fast forward version. The Display Pipeline The order may vary somewhat. The Graphics Pipeline. To draw images.
View volume The fast forward version The Display pipeline Computer Graphics 1, Fall 2004 Lecture 3 Chapter 1.4, 1.8, 2.5, 8.2, 8.13 Lightsource Hidden surface 3D Projection View plane 2D Rasterization
More informationChapter 6 Color Image Processing
Image Comm. Lab EE/NTHU 1 Chapter 6 Color Image Processing Color is a powerful descriptor Human can discern thousands of color shades. "color" is more pleasing than "black and white. Full Color: color
More informationFall 2015 Dr. Michael J. Reale
CS 490: Computer Vision Color Theory: Color Models Fall 2015 Dr. Michael J. Reale Color Models Different ways to model color: XYZ CIE standard RB Additive Primaries Monitors, video cameras, etc. CMY/CMYK
More informationComputational Perception. Visual Coding 3
Computational Perception 15-485/785 February 21, 2008 Visual Coding 3 A gap in the theory? - - + - - from Hubel, 1995 2 Eye anatomy from Hubel, 1995 Photoreceptors: rods (night vision) and cones (day vision)
More informationNeurophysical Model by Barten and Its Development
Chapter 14 Neurophysical Model by Barten and Its Development According to the Barten model, the perceived foveal image is corrupted by internal noise caused by statistical fluctuations, both in the number
More informationImage Formation. Camera trial #1. Pinhole camera. What is an Image? Light and the EM spectrum The H.V.S. and Color Perception
Image Formation Light and the EM spectrum The H.V.S. and Color Perception What is an Image? An image is a projection of a 3D scene into a 2D projection plane. An image can be defined as a 2 variable function
More informationCSE 167: Lecture #6: Color. Jürgen P. Schulze, Ph.D. University of California, San Diego Fall Quarter 2011
CSE 167: Introduction to Computer Graphics Lecture #6: Color Jürgen P. Schulze, Ph.D. University of California, San Diego Fall Quarter 2011 Announcements Homework project #3 due this Friday, October 14
More informationMarble classification using scale spaces
Marble classification using scale spaces G.Dislaire & E.Pirard Université de Liège, GeomaC, Géoressources Minérales, Liège, Belgium. M.Vanrell Universitat Autònoma de Barcelona, Spain. ABSTRACT: Marble
More informationColor in Image & Video Processing Applications
Color in Image & Video Processing Applications DAGM 2010 Darmstadt Joost van de Weijer Universitat Autonoma de Barcelona Computer Vision Center Why use Color? photometric invariance discriminative power
More informationColor. making some recognition problems easy. is 400nm (blue) to 700 nm (red) more; ex. X-rays, infrared, radio waves. n Used heavily in human vision
Color n Used heavily in human vision n Color is a pixel property, making some recognition problems easy n Visible spectrum for humans is 400nm (blue) to 700 nm (red) n Machines can see much more; ex. X-rays,
More informationCharacterizing and Controlling the. Spectral Output of an HDR Display
Characterizing and Controlling the Spectral Output of an HDR Display Ana Radonjić, Christopher G. Broussard, and David H. Brainard Department of Psychology, University of Pennsylvania, Philadelphia, PA
More informationDesign & Use of the Perceptual Rendering Intent for v4 Profiles
Design & Use of the Perceptual Rendering Intent for v4 Profiles Jack Holm Principal Color Scientist Hewlett Packard Company 19 March 2007 Chiba University Outline What is ICC v4 perceptual rendering? What
More informationDigital Image Processing. Introduction
Digital Image Processing Introduction Digital Image Definition An image can be defined as a twodimensional function f(x,y) x,y: Spatial coordinate F: the amplitude of any pair of coordinate x,y, which
More informationIntroduction to Computer Graphics with WebGL
Introduction to Computer Graphics with WebGL Ed Angel Professor Emeritus of Computer Science Founding Director, Arts, Research, Technology and Science Laboratory University of New Mexico Image Formation
More informationColour appearance modelling between physical samples and their representation on large liquid crystal display
Colour appearance modelling between physical samples and their representation on large liquid crystal display Chrysiida Kitsara, M Ronnier Luo, Peter A Rhodes and Vien Cheung School of Design, University
More informationA SYNOPTIC ACCOUNT FOR TEXTURE SEGMENTATION: FROM EDGE- TO REGION-BASED MECHANISMS
A SYNOPTIC ACCOUNT FOR TEXTURE SEGMENTATION: FROM EDGE- TO REGION-BASED MECHANISMS Enrico Giora and Clara Casco Department of General Psychology, University of Padua, Italy Abstract Edge-based energy models
More informationContrast and Color. Jean-Michel Morel, Ana Belen Petro and Catalina Sbert. November 3, 2010
Contrast and Color Jean-Michel Morel, Ana Belen Petro and Catalina Sbert November 3, 2010 1 Color: description and representation 1.1 Introduction The study of Color involves several branches of knowledge:
More informationIllumination and Shading
Illumination and Shading Light sources emit intensity: assigns intensity to each wavelength of light Humans perceive as a colour - navy blue, light green, etc. Exeriments show that there are distinct I
More informationThe Elements of Colour
Color science 1 The Elements of Colour Perceived light of different wavelengths is in approximately equal weights achromatic.
More informationIMAGE PROCESSING >FILTERS AND EDGE DETECTION FOR COLOR IMAGES UTRECHT UNIVERSITY RONALD POPPE
IMAGE PROCESSING >FILTERS AND EDGE DETECTION FOR COLOR IMAGES UTRECHT UNIVERSITY RONALD POPPE OUTLINE Filters for color images Edge detection for color images Canny edge detection FILTERS FOR COLOR IMAGES
More informationECE-161C Color. Nuno Vasconcelos ECE Department, UCSD (with thanks to David Forsyth)
ECE-6C Color Nuno Vasconcelos ECE Department, UCSD (with thanks to David Forsyth) Color so far we have talked about geometry where is a 3D point map mapped into, in terms of image coordinates? perspective
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 informationRobust color segmentation algorithms in illumination variation conditions
286 CHINESE OPTICS LETTERS / Vol. 8, No. / March 10, 2010 Robust color segmentation algorithms in illumination variation conditions Jinhui Lan ( ) and Kai Shen ( Department of Measurement and Control Technologies,
More informationCOMP3421. Global Lighting Part 2: Radiosity
COMP3421 Global Lighting Part 2: Radiosity Recap: Global Lighting The lighting equation we looked at earlier only handled direct lighting from sources: We added an ambient fudge term to account for all
More informationColor. Reading: Optional reading: Chapter 6, Forsyth & Ponce. Chapter 4 of Wandell, Foundations of Vision, Sinauer, 1995 has a good treatment of this.
Today Color Reading: Chapter 6, Forsyth & Ponce Optional reading: Chapter 4 of Wandell, Foundations of Vision, Sinauer, 1995 has a good treatment of this. Feb. 17, 2005 MIT 6.869 Prof. Freeman Why does
More informationLast update: May 4, Vision. CMSC 421: Chapter 24. CMSC 421: Chapter 24 1
Last update: May 4, 200 Vision CMSC 42: Chapter 24 CMSC 42: Chapter 24 Outline Perception generally Image formation Early vision 2D D Object recognition CMSC 42: Chapter 24 2 Perception generally Stimulus
More informationRefinement of the RLAB Color Space
Mark D. Fairchild Munsell Color Science Laboratory Center for Imaging Science Rochester Institute of Technology 54 Lomb Memorial Drive Rochester, New York 14623-5604 Refinement of the RLAB Color Space
More informationColor Space Transformations
Color Space Transformations Philippe Colantoni and Al 2004 1 Introduction This document defines several color concepts and all the mathematic relations used in ColorSpace. The first version of this document
More informationCS681 Computational Colorimetry
9/14/17 CS681 Computational Colorimetry Min H. Kim KAIST School of Computing COLOR (3) 2 1 Color matching functions User can indeed succeed in obtaining a match for all visible wavelengths. So color space
More informationWhen this experiment is performed, subjects find that they can always. test field. adjustable field
COLORIMETRY In photometry a lumen is a lumen, no matter what wavelength or wavelengths of light are involved. But it is that combination of wavelengths that produces the sensation of color, one of the
More information3D Visualization of Color Data To Analyze Color Images
r IS&T's 2003 PICS Conference 3D Visualization of Color Data To Analyze Color Images Philippe Colantoni and Alain Trémeau Laboratoire LIGIV EA 3070, Université Jean Monnet Saint-Etienne, France Abstract
More informationPattern recognition. Classification/Clustering GW Chapter 12 (some concepts) Textures
Pattern recognition Classification/Clustering GW Chapter 12 (some concepts) Textures Patterns and pattern classes Pattern: arrangement of descriptors Descriptors: features Patten class: family of patterns
More informationModule 3. Illumination Systems. Version 2 EE IIT, Kharagpur 1
Module 3 Illumination Systems Version 2 EE IIT, Kharagpur 1 Lesson 14 Color Version 2 EE IIT, Kharagpur 2 Instructional Objectives 1. What are Primary colors? 2. How is color specified? 3. What is CRI?
More informationColor and Brightness: Contrast and Context
Color and Brightness: Contrast and Context Steven K. Shevell Visual Sciences Center, University of Chicago, Chicago IL Introduction This paper is about human perception of color and brightness. It is well
More informationCSE 167: Lecture #7: Color and Shading. Jürgen P. Schulze, Ph.D. University of California, San Diego Fall Quarter 2011
CSE 167: Introduction to Computer Graphics Lecture #7: Color and Shading Jürgen P. Schulze, Ph.D. University of California, San Diego Fall Quarter 2011 Announcements Homework project #3 due this Friday,
More informationLighting. Camera s sensor. Lambertian Surface BRDF
Lighting Introduction to Computer Vision CSE 152 Lecture 6 Special light sources Point sources Distant point sources Strip sources Area sources Common to think of lighting at infinity (a function on the
More informationUsing modern colour difference formulae in the graphic arts
Using modern colour difference formulae in the graphic arts Funded project: Evaluating modern colour difference formulae. AiF-Nr.: 14893 N 1 Agenda 1. Graphic arts image assessment 2. Impact of the background
More informationComputer Vision I - Basics of Image Processing Part 1
Computer Vision I - Basics of Image Processing Part 1 Carsten Rother 28/10/2014 Computer Vision I: Basics of Image Processing Link to lectures Computer Vision I: Basics of Image Processing 28/10/2014 2
More information(0, 1, 1) (0, 1, 1) (0, 1, 0) What is light? What is color? Terminology
lecture 23 (0, 1, 1) (0, 0, 0) (0, 0, 1) (0, 1, 1) (1, 1, 1) (1, 1, 0) (0, 1, 0) hue - which ''? saturation - how pure? luminance (value) - intensity What is light? What is? Light consists of electromagnetic
More informationColor Image Processing
Color Image Processing Inel 5327 Prof. Vidya Manian Introduction Color fundamentals Color models Histogram processing Smoothing and sharpening Color image segmentation Edge detection Color fundamentals
More informationSequential Maximum Entropy Coding as Efficient Indexing for Rapid Navigation through Large Image Repositories
Sequential Maximum Entropy Coding as Efficient Indexing for Rapid Navigation through Large Image Repositories Guoping Qiu, Jeremy Morris and Xunli Fan School of Computer Science, The University of Nottingham
More informationMinimalist surface-colour matching
Perception, 2005, volume 34, pages 1007 ^ 1011 DOI:10.1068/p5185 Minimalist surface-colour matching Kinjiro Amano, David H Foster Computational Neuroscience Group, Faculty of Life Sciences, University
More informationCS 556: Computer Vision. Lecture 18
CS 556: Computer Vision Lecture 18 Prof. Sinisa Todorovic sinisa@eecs.oregonstate.edu 1 Color 2 Perception of Color The sensation of color is caused by the brain Strongly affected by: Other nearby colors
More informationA Multiscale Model of Adaptation and Spatial Vision for Realistic Image Display
A Multiscale Model of Adaptation and Spatial Vision for Realistic Image Display Sumanta N. Pattanaik James A. Ferwerda Mark D. Fairchild Donald P. Greenberg Program of Computer Graphics, Cornell University
More informationImproving visual function diagnostic metrics. Charles Campbell
Improving visual function diagnostic metrics Charles Campbell Metrics - What are they? What are they used for? A metric assigns a numerical value or a set of values to characterize some chosen phenomenon
More informationTWO APPROACHES IN SCANNER-PRINTER CALIBRATION: COLORIMETRIC SPACE-BASED VS. CLOSED-LOOP.
TWO APPROACHES I SCAER-PRITER CALIBRATIO: COLORIMETRIC SPACE-BASED VS. CLOSED-LOOP. V. Ostromoukhov, R.D. Hersch, C. Péraire, P. Emmel, I. Amidror Swiss Federal Institute of Technology (EPFL) CH-15 Lausanne,
More informationWhy does a visual system need color? Color. Why does a visual system need color? (an incomplete list ) Lecture outline. Reading: Optional reading:
Today Color Why does a visual system need color? Reading: Chapter 6, Optional reading: Chapter 4 of Wandell, Foundations of Vision, Sinauer, 1995 has a good treatment of this. Feb. 17, 2005 MIT 6.869 Prof.
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 informationImage Analysis and Formation (Formation et Analyse d'images)
Image Analysis and Formation (Formation et Analyse d'images) James L. Crowley ENSIMAG 3 - MMIS Option MIRV First Semester 2010/2011 Lesson 4 19 Oct 2010 Lesson Outline: 1 The Physics of Light...2 1.1 Photons
More informationChapter 1. Light and color
Chapter 1 Light and color 1.1 Light as color stimulus We live immersed in electromagnetic fields, surrounded by radiation of natural origin or produced by artifacts made by humans. This radiation has a
More informationPart 3: Image Processing
Part 3: Image Processing Image Filtering and Segmentation Georgy Gimel farb COMPSCI 373 Computer Graphics and Image Processing 1 / 60 1 Image filtering 2 Median filtering 3 Mean filtering 4 Image segmentation
More informationDetecting Salient Contours Using Orientation Energy Distribution. Part I: Thresholding Based on. Response Distribution
Detecting Salient Contours Using Orientation Energy Distribution The Problem: How Does the Visual System Detect Salient Contours? CPSC 636 Slide12, Spring 212 Yoonsuck Choe Co-work with S. Sarma and H.-C.
More informationSIFT - scale-invariant feature transform Konrad Schindler
SIFT - scale-invariant feature transform Konrad Schindler Institute of Geodesy and Photogrammetry Invariant interest points Goal match points between images with very different scale, orientation, projective
More informationCOS Lecture 10 Autonomous Robot Navigation
COS 495 - Lecture 10 Autonomous Robot Navigation Instructor: Chris Clark Semester: Fall 2011 1 Figures courtesy of Siegwart & Nourbakhsh Control Structure Prior Knowledge Operator Commands Localization
More informationCIE L*a*b* color model
CIE L*a*b* color model To further strengthen the correlation between the color model and human perception, we apply the following non-linear transformation: with where (X n,y n,z n ) are the tristimulus
More informationCSE 167: Introduction to Computer Graphics Lecture #6: Colors. Jürgen P. Schulze, Ph.D. University of California, San Diego Fall Quarter 2013
CSE 167: Introduction to Computer Graphics Lecture #6: Colors Jürgen P. Schulze, Ph.D. University of California, San Diego Fall Quarter 2013 Announcements Homework project #3 due this Friday, October 18
More informationImplementation of colour appearance models for comparing colorimetrically images using a calibrated digital camera
Implementation of colour appearance models for comparing colorimetrically images using a calibrated digital camera Elisabeth Chorro Calderón MSc Dissertation Colour and Vision Group, University of Alicante
More informationColor Local Texture Features Based Face Recognition
Color Local Texture Features Based Face Recognition Priyanka V. Bankar Department of Electronics and Communication Engineering SKN Sinhgad College of Engineering, Korti, Pandharpur, Maharashtra, India
More informationSpectral Color and Radiometry
Spectral Color and Radiometry Louis Feng April 13, 2004 April 13, 2004 Realistic Image Synthesis (Spring 2004) 1 Topics Spectral Color Light and Color Spectrum Spectral Power Distribution Spectral Color
More informationthis is processed giving us: perceived color that we actually experience and base judgments upon.
color we have been using r, g, b.. why what is a color? can we get all colors this way? how does wavelength fit in here, what part is physics, what part is physiology can i use r, g, b for simulation of
More informationPerceptual Effects in Real-time Tone Mapping
Perceptual Effects in Real-time Tone Mapping G. Krawczyk K. Myszkowski H.-P. Seidel Max-Planck-Institute für Informatik Saarbrücken, Germany SCCG 2005 High Dynamic Range (HDR) HDR Imaging Display of HDR
More information