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A presentation on the digital image analysis of the Srhoud of Turin
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© 2008 R. Schneider 1
Digital Image Analysis of the
Shroud of Turin
An Ongoing Investigation
by
Ray Schneider
Assistant Professor
Math and Computer Science
Bridgewater College, VA
© 2008 R. Schneider 2
Motivation and Scope
• Inspired by Mario Latendresse’s JavaScript use of shroud images to make length measurements
• Availability of High Resolution Digital Images of the shroud
– Barrie Schwortz 1978 STURP pictures
– Durante 2000, 2002 scans provided by Guilio Fanti and others
http://www.bridgewater.edu/~rschneid/FocusProjects/Shroud/ShroudMeasure/shroudCal.html
© 2008 R. Schneider 3
Levels of Analysis
• Level 1: DATA
– quantitative observables, ex. RGB values
• Level 2: RECOGNITION
– categories of things, ex. cloth, image, blood
• Level 3: AGGREGATION
– integrative, ex. face, wrist, arms
• Level 4: MEANING
– context, ex. wounds, scourging, crucifixion, etc.
© 2008 R. Schneider 4
Initial Result• Suppressed Face Bands with color normalization
© 2008 R. Schneider 5
Long Range Objectives
• Develop a Comprehensive Image Study Program– Compare Multiple Images
– Feature Characterization
– Banding, Image, Blood, Scorch, and others
– Color Normalization to reduce banding and enhance image
– Blood Image Enhancement especially of Scourge Markings
– Additional Projects as Intermediate Research Suggests
– DEVELOP MEANS OF INVOLVING YOUNG RESEARCHERS
© 2008 R. Schneider 6
Methods and Tools• Feature Analysis and Pattern Recognition
• Tools (I’ve touched or tried, many are free)
– MATLAB Image Processing Toolbox
– CVIPtools
– ImageJ
– Photoshop Elements
– Python Imaging Library (PIL)
– ImageMagick
– and others (ex. Irfanview, GIMP, etc.)
YELLOW signifies commercial products.
© 2008 R. Schneider 7
Today’s Report
• Progress Report
• PRIMARY FOCUS
– IMAGE SEGMENTATION USING COLOR,
LUMINANCE, and WEAVE STRIATIONS
– TWO STEPS:
• Determine Classification Metrics for Samples
• Make Color Substitutions to Highlight Results
© 2008 R. Schneider 8
Example SamplesNote Striations (stripe and interstitials)
• blood, image, scorch, clean cloth
b3333,13352 b3989,15828 b3545,1609 b3231,13352 b5392,16734
b5148,15535 b2768,9341 b2931,17472 i3424,13312i3293,13666
i3693,3937
i3541,13625
i3304,13858 darkScorch s5100,10065 c5474,7284 c4928,7019c3646,14588
c2453,13512
© 2008 R. Schneider 9
Sample Sites for Stripe/Interstitial
Analyses
Sample Sites Used
in analysis:
blood b1 through b8
cloth c7 to c10
image i1 through i5
© 2008 R. Schneider 10
Blood Image Samples• Initially took point samples in stripe and
interstitial regions of samples
© 2008 R. Schneider 11
All Colors In All Places?
a complex affair
LIGHT
CLOTH
BLOOD
IMAGE
DIRT
So The ProblemEverything is Everywhere
© 2008 R. Schneider 12
Color Spaces Usedfor various purposes but primarily to isolate
color from intensity or luminance
• RGB True Color
• uint8, double
• rgbL unit vector and Luminance
• (φ, θ, L) phi, theta, luminance SCT
• binary (black and white)
red
φ
θ
blu
e
rgbL is a Cartesian Space where the rgb unit vector specifies color and L the
luminance, (φ, θ, L) is an equivalent space with the unit vector reduced to angular
coordinates
© 2008 R. Schneider 13
A Narrow Color Space
Phi
Lu
min
an
ce
The colors in the shroud take up a very
small part of the total number of colors so
that color alone is a difficult classifier.
black = all pixels in FC
blue = pixels in c1 cloth sample
green = pixels across cheeks
and nose in face
red = pixels in blood sample b1
© 2008 R. Schneider 14
b3545,1609c1 cloth ic image cheeks & nose
Luminance Helps
fc face crop of
primary image
BLK=full color space of FC
BLU=cloth represented by c1
GRN image space across cheeks
RED blood
PHI THETA
LU
MIN
AN
CE
LU
MIN
AN
CE
© 2008 R. Schneider 15
False Color
Substitution
• EXAMPLE
– Find a classification
color range for blood
and substitute a false
color everywhere a
pixel falls into the
color range
© 2008 R. Schneider 16
False Color Injection
Using Indexed Images
Image was converted to unit
color vectors, this was then
compressed to eight colors.
Three of these were correlated
to blood and RED [1,0,0] was
injected for these the rest
remained unchanged.
© 2008 R. Schneider 17
Color Segmentation
image decomposition by colororiginal luminance color unit vector
contrast enhanced
color unit vector
© 2008 R. Schneider 18
Luminance/Unit Vector• Image converted to a
luminance image and a unit vector color image (2 images)
• Image at right is color stretched view of unit vector color image
• Suggests general feasibility of color segmentation by color alone if contrast stretch is used
© 2008 R. Schneider 19
Nose Image
Nose Image from D2000
Color Unit Vectors
Contrast Stretched
© 2008 R. Schneider 20
Unit Vector Color Segmentation
R G B
GB
Unit Vector Color Image Contrast
Stretched by Color Plane and converted
to 16 color indexed image and false-color
BLACK substituted for RGB pixels with
greatest R, G, B or both G & B values.
All images were positive, note the negative
effect particularly in GB substitution.
© 2008 R. Schneider 21
False Color By LuminanceBaseline 24 indexed color WHT and BLK
d3 b5 b8
b12 b12d5 b12d8
© 2008 R. Schneider 22
Combining Unit Vectors and
Luminance (angle and interval)
3 degrees
0.3-0.7
3 degrees
0.4-0.8
3 degrees
0.5-0.9
© 2008 R. Schneider 23
Image
Pixels
5 degrees 0.7 to 0.9 6 degrees 0.8 to 0.9
Triple false color substitution
used to narrow color vector
WHT = brightest pixels
RED = darkest pixels
GRN = intermediate pixels
used to narrow color unit vector Left and Right Cheeks
© 2008 R. Schneider 24
Cloth & Image Stripes and Interstitials
i3304,13858
i3293,13666
i3424,13312
i3541,13625
i3693,3937
c2453,13512
c5474,7284
c4928,7019
c3646,14588Generated by sorting pixels by
luminance and binary splitting at the median
© 2008 R. Schneider 25
Blood & Scorch Stripe and Interstitial
b3545,1609
b3989,15828
b3231,13352
b3333,13352
b5148,15535
b5392,16734
b2768,9341
b2931,17472
s5100,10065
darkScorch
© 2008 R. Schneider 26
Phi Theta Luminance• (φ,θ, L) convenient coordinate system where
(φ,θ) defines the color and L the intensity
False color substitution
using a set of intervals
BLOOD(RED)
(φ: 0.52-.7
θ: 1.0467-1.17
L: 0.35 – 0.7166)
INTERSTITIAL(WHT)
(φ: 0.62-0.733
θ: 1.02-1.1
L: 0.73-1.0)
original interstitial WHT
blood RED combination RED/WHT
STRIPE
INTERSTITIAL
© 2008 R. Schneider 27
Transference• Using intervals from one sample on another
• Wrist wound intervals applied to chest wound
original interstitial WHT blood REDcombined RED
and WHT
© 2008 R. Schneider 28
Color & Luminance Blood
b1 r foot
b2 b chest
b3 g base E
b4 m bk head
b5 c elbow
b6 k elbow out
b7 y scourge
b8 k wristφ,θ Color Space High Overlap
note median cuts
Luminance Stripe
Luminance
Interstitial
Stripe
Interstitial
© 2008 R. Schneider 29
Mean of Stripe & Interstitials
• Blood, Image,
and Cloth have
different colors
on average, but
they are very
close together
Plot in φ,θ space of means of stripe & interstitial
colors. Large ambiguity when variance is
considered. Luminance reduces this.
blood
image
cloth
© 2008 R. Schneider 30
Cloth, Blood, Image
Nearest Neighbor Substitution
Mean PTL color vectors from
stripe and interstitials of cloth,
blood, and image samples were
used as reference colors
matched with false colors:
cs: 85% white
ci: white
bs: red [1 0 0]
bi: white 60% gray
is: orange [1 .4 0]
ii: flesh [1 .8 .6]
© 2008 R. Schneider 31
Conclusions So Far
• Shroud is characterized by a very narrow color/luminance space which makes classification by color alone difficult
• Contrast Stretching May Ameliorate this Problem (requires further work)
• Region Analysis of Stripe and Interstitials Separately May Improve Segmentation
• The Image Area Shows a Strong Affinity with the Interstitial Blood Modes as well as having pixels that are likely evidence of blood on nose, mustache, and beard
© 2008 R. Schneider 32
Further Work
• Extend work by exploring more selective substitution schemes– ex. Add localized region statistics to classifier
• Explore Fine Tuning using color and luminance gradients
• Explore Stripe/Interstitial Relationship Further by Category (cloth, image, blood, etc.)
• Extend work to other features– scorch, water stain margins, detritus (dirt, droppings)
• Explore patterns of dirt in otherwise pristine regions
© 2008 R. Schneider 33
Acknowledgements
• Mario Latendresse whose work on using pixel coordinates got me thinking
• Barrie Schwortz for his images and friendship– Schwortz 1978
• Giulio Fanti for providing me with high resolution images used in this study and others I hope to use in the future– Durante 2000
• All the shroud people who have inspired me over the years, especially Dan Scavone who was always so generous with his time and knowledge
© 2008 R. Schneider 34
Thankyou All for Listening
© 2008 R. Schneider 35
Additional Slides
Not In Talk
© 2008 R. Schneider 36
Color & Luminance Cloth
c7 r
c8 b
c9 g
c10 m
© 2008 R. Schneider 37
Color & Luminance Image
© 2008 R. Schneider 38
30 Blood Sample Unit Vectors
Cluster Relatively Tightly
© 2008 R. Schneider 39
Same Measures in RGB 0..255
© 2008 R. Schneider 40
Blood and Open Cloth
Scatter of Blood and Cloth Unit Color Vector Samples
from 30 Blood Samples and 136 Cloth Sample Points
red = blood
blue = lighter cloth
green = darker cloth
Lighter and darker are
relative in the same
sample, top of threads
and between threads
of weave.
Color Unit Vector Space
© 2008 R. Schneider 41
RGB Plot of Image Samplesb (blue) = tip of nose
g (green) = left cheek
r (red) = right eye
c (cyan) = right cheek
m (magenta) = right calf
© 2008 R. Schneider 42
Clean and Image ClothHard to Separate Image and Cloth
Bands on Side of Face
Tip of Nose
© 2008 R. Schneider 43
Example Banding sample c2
© 2008 R. Schneider 44
General CoordinatesA Natural System
R=Right C=Center L=Left D=Dorsal V=Ventral
Pixel Coordinates used to locate samples so a
sample is classified as type followed by a region
and pixel location, ex. cLD1R1055x9408 would be a
cloth sample (i.e. not image or blood, etc. in the Left
Dorsal 1 region and the trailing R is the Right herring
bone weave, i.e. /// slanted up and to the right
Dorsal Ventral
1 1 22
R
C
L
613 6373 12133 17894 23654
c=cloth
b=blood
i=image
s=scorch
w=waterstain margins
m=miscellaneous
© 2008 R. Schneider 45
Cloth Samples
© 2008 R. Schneider 46
3 Samples in RGB Space
The problem is that
all samples potentially
contain all kinds of
elements:
1) blood,
2) cloth
3) image
A lot of color overlap
and hence ambiguity.
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