Transcript Color, Shading, Light - Texas Tech University

Color, Shading, Light
Ian C. Scott-Fleming
Topics to Cover
• Paper: Computational Model for Color
Naming
– Some background a quick look at:
• Color, solar spectrum, eye as a detector
• Color spaces
– Summary of paper
Light, Spectrum, Eye
Interesting note: birds have 4
color receptors – they have one in
the UV. Which suggests that
other than when displaying for
humans, there’s no particular
reason to limit computer vision to
3 channels…
Note: The visible colorspace contains
colors not in the solar spectrum:
R+G = Yellow (~575 nm),
but R+B=Magenta, which is not the color
at 555 nm (Yellow-Green).
We perceive ‘chords’ as a single color.
Brightness and Color
• Difference between an Eye and a Camera:
– Eye adjusts pupil as light level changes
• Yellow stays yellow for eye
– Cameras (most) do not have an adjustable pupil.
• As Yellow gets brighter, Red & Green saturate at (255),
and color will shift towards (pale) blue
– Camera Detectors not as linear near saturation or
near zero, so colors can shift with light levels
Shading with Surface Angle
• Surface Brightness is proportional to cosine of
angle between surface normal and incident
light
• A Ball looks spherical even with 1 eye closed,
because we infer shape from shading
• We see shading of Soccer Ball, but perceive it
as all 1 color (orange) and spherical
• Computer sees flat (2-D) image of Soccer Ball
as range of colors.
Color Spaces
• We want a color space where color is
orthogonal to brightness, so we can (more
easily) detect color independent of lighting
level
• Some candidates:
– YCbCr (YPbPr)
– YUV
– HSL (HSV)
– LAB
RGB
• +: matches eye model nicely
• +: Easy to implement in hardware
• Put 3 photodetectors close together and deposit
different bandpass filters
• Digitize each pixel and send as color triplet
• -: Color and brightness are coupled in each
channel
YPbPr, YCbCr, YUV
•
•
•
•
Y: Brightness
Pb: difference between B & weighted sum of R,G
Pr: diff between R & B,G
Easy to implement:
– Software: simple matrix multiply, or:
– Hardware: a few op amps between readout & digitizer
YPbPr
========================================================
Y' = + 0.299
* R' + 0.587
* G' + 0.114 * B'
Pb = - 0.168736 * R' - 0.331264 * G' + 0.5
* B'
Pr = + 0.5
* R' - 0.418688 * G' - 0.081312 * B'
........................................................
R', G', B' in [0; 1]
Y' in [0; 1]
Pb in [-0.5; 0.5]
Pr in [-0.5; 0.5]
YCbCr identical to YPbPr except scaled 0-255 instead of 0-1
YUV like YPbPr, but U,V scaled slightly differently (used for analog TV signal
LAB
• Intended to encapsulate human perception of
color differences:
– Euclidean distance between colors in LAB
proportional (sort-of) to perceived difference
between colors
• Calculations complex:
– Matrix multiply followed by non-integer
exponentiation
HSL, HSV
• Hue (color)
– Represents angle on a color wheel
• Saturation – measure of pureness of color
– No saturation: pure color
– Some saturation: whitish (pastels)
– fully saturated: black/gray/white (no color)
• Value (or Lightness) – brightness (grayscale)
• +: Better Matches how we perceive color (light green, dark blue)
• -: Difficult to implement: requires series of if/then/else statements
• -: Color not completely decoupled from brightness
For HSV: H is same, V = max, S = (max-min)/max
Comparison of HSL (left)
&
HSV (right)
Paper: Color Naming
• Develop a human-color-perception naming
scheme that can be implemented on a
computer, and some metrics for measuring
distances in color space
• Demonstrate how it can be used (in
conjunction with other standard CV tools) to
segment an image and describe the colors in it
3 Parts to the Paper
• Develop a Language for color Naming
– in CS sense of language:
• Syntax, semantics, vocabulary
• Generate a Metric for mapping an arbitrary color
to a ‘nearest’ color prototype (aka focus)
– Locate nearest prototype
– Measure difference from prototype
• Demonstrate Segmentation and extracting color
composition
Develop Language
• Conducted some experiments with humans to
see how consistent naming is, how universal
color names are
• Created a well-defined syntax with several
levels of descriptiveness
Syntax:
•
Proposed language is basically a recursive systematization of HSL color space:
•
•
<color name>
<color name>:<chromatic name> | <achromatic name>
–
–
–
•
<lightness>: blackish | very dark | dark | medium | light | very light | whitish
–
•
Strong blue, vivid yellow, etc.
<hue>: <generic hue> | <halfway hue> | <quarterway hue>
–
–
–
–
•
•
Dark blue, light green, etc.
<saturation>:grayish | moderate| medium | strong | vivid
–
•
<chromatic name>: <lightness><saturation><hue> | <saturation><lightness><hue>
<achromatic name>:<lightness><achromatic term>
Red, Green, Orange, Brown, Cyan, etc.
<generic hue>: red | orange | brown | yellow | green | blue | purple | pink | beige | olive
<halfway hue>: <generic hue> - <generic hue>
<quarterway hue>: <ish form><generic hue>
<ish form>: reddish | brownish | yellowish | greenish | bluish | purplish | pinkish
<achromatic term>: <generic achromatic term> | <ish form><generic achromatic term>
<generic achromatic term>: gray | black | white
Multiple Levels for different
descriptive requirements:
•
•
•
•
Fundamental Level: a generic hue
Course level: add a luminance descriptor
Medium level: add saturation
Minute level: Complete color syntax ( including
halfway & quarterway hues?)
• Appropriate level used depended upon situation.
– The car is green
– The sky is light blue while the lake is dark blue
– minute level only when needing to be very specific
Color Metric
Figure 1 & Table 1 assert that LAB distance (DLAB)not a good distance measure. Author
reports that ThetaHLS and DHLS correlate better with perceived distance. Also, with
sparse set of color names nearest prototype color not necessarily appropriate.
Example: Nearest Prototype in LAB space maps some grays to “Whitish Pink”, etc.
Author develops a metric which weighs ThetaHLS more heavily than LAB distance does in
calculating distance
Modified Distance Measure
Geometric rationale for
measure:
1. Find a point, Copt , along
prototype color’s (C0) vector
from origin, which best
represents color Cx .
2. Adjust Copt so that it’s
distance from projection of
Cx incorporates penalty for
size of thetaHSL
Note: Author doesn’t provide clear justification for why this is a better measure.
Simply presents statistic that it matches human judgment of color differences better
than DLAB . Nor does she explain in what sense Copt is “optimal”.
Example Application: ColorSegmenting an image and obtaining
Color Labels
• First, a “Textbook case” of how to rigorously segment a color image
– Steps correspond in some sense to human perception process to
adapt to light levels and assign colors
– Adjust for camera nonlinearities (gamma correction, “Color Constancy
Issues”)
• Most images include gamma encoding so they will display properly on a CRT
screen.
– Median-Filter image to remove noise
– Remove zero-offset (black bias) and do contrast stretch for maximum
dynamic range to adapt to lighting
– Adaptive filtering to smooth uniform and textured areas
• Not clear whether she returns to original pixel values for filtering, or works
with reduced-color image at this point.
– Segment image using Mean Shift
• references Comanicu & Meers paper (1st reading assmt)
Finally, use Naming
• After segmenting, then for each segmentation
color, apply naming algorithm to find nearest
prototype color and assign its name to that
region of segmented image
• Personal Note: I found the segmenting steps a little distracting here –
they take focus away from use of color naming. Presumably the
description of how she does the segmenting was to be rigorous.
• The naming could be applied to any segmented image, regardless of how
the segmentation was done.
Questions
• (AC) What exactly is being shown in table 1?
– Distance measures for five colors of figure 1
• Dlab, Dhsl, thetaLAB and thetaHLS
– Author is trying to show that Dlab is not best measure. Would help if
we had color copy of paper…
• What is a color foci?
– One of the 267 prototype colors of the NBS color dictionary
• In the section about color naming results, they speak about coarse,
medium and minute, why do they have three different levels
– Context-specific level to apply naming language
• Pictures typically described at coarse or medium
– Green ball or light blue car
• Specifics of an object, or differentiating objects, may require minute (full)
language
• What is fig.2 trying to say?
– Illustrates steps and terms used in section B.
• What is meaning of equation number 5 in page 696?
– Attempt to describe mathematically how to choose the blackest and
whitest pixel for the contrast stretch/lighting correction.
• What is hypothesis of graded (fuzzy ) membership ?
– (?) analog to author’s levels (fundamental, coarse, medium, minute)
•
What is color vocabulary? what is the difference between color
naming and color vocabulary ?
• What is Munsell color order system and what made it widely used
in spite of its lack of color vocabulary?
– First (1905) color space separating Hue (color), value (brightness) and
chroma (~saturation)
– First attempt to define color measure with a meaningful difference
measure.
– Lack of vocabulary: limited number of colors.
– Still in use because of entrenched applications (Brewers use it to define
beer color, USGS uses it to define soil colors, etc.)
• I do not really understand why it is so important
to name colors, instead of just calling them a
number.
– Automatic labeling of colors in images for DB
matching, human understanding, etc.. See examples
in section VI, Results, Discussion, Conclusions
• How are prototypes (foci) decided when the
metric computes the distance between input
color and prototypes? (Section III, page4)
– Prototypes are a given (input). Author worked from
NBS dictionary of 267 colors, but notes that other
color dictionaries could be used.
• Could you please explain the algorithm in
Designing the Color-Naming Metric on page 5?