Digital Image Analysis of the Shroud of Turin

An Ongoing Investigation by Ray Schneider Assistant Professor Math and Computer Science Bridgewater College, VA

Digital Image Analysis of the Shroud of Turin Š 2008 R. Schneider

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Motivation and Scope • Inspired by Mario Latendresse’s JavaScript use of shroud images to make length measurements http://www.bridgewater.edu/~rschneid/FocusProjects/Shroud/ShroudMeasure/shroudCal.html

• Availability of High Resolution Digital Images of the shroud – Barrie Schwortz 1978 STURP pictures – Durante 2000, 2002 scans provided by Guilio Fanti and others © 2008 R. Schneider

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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

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Initial Result • Suppressed Face Bands with color normalization

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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

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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.) © 2008 R. Schneider

YELLOW signifies commercial products.

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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

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Example Samples Note Striations (stripe and interstitials) • blood, image, scorch, clean cloth

c2453,13512

i3541,13625 b3333,13352 b3989,15828 b3545,1609 b3231,13352 b5392,16734

b5148,15535 b2768,9341

i3693,3937

b2931,17472 i3424,13312 i3293,13666

i3304,13858 darkScorch

s5100,10065 c5474,7284 c4928,7019 Š 2008 R. Schneider

c3646,145888

Sample Sites for Stripe/Interstitial Analyses Sample Sites Used in analysis: blood b1 through b8 cloth c7 to c10 image i1 through i5

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Blood Image Samples • Initially took point samples in stripe and interstitial regions of samples

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So The Problem

Everything is Everywhere LIGHT

BLOOD IMAGE

DIRT

CLOTH

All Colors In All Places? a complex affair Š 2008 R. Schneider

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Color Spaces Used blue

for 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)

θ φ

red

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 © 2008 R. Schneider 12 coordinates

A Narrow Color Space 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.

Luminance

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

Phi

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Luminance Helps b3545,1609

c1 cloth

ic image cheeks & nose

GRN image space across cheeks RED blood

LUMINANCE

LUMINANCE

BLK=full color space of FC BLU=cloth represented by c1

fc face crop of primary image

PHI

THETA Š 2008 R. Schneider

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False Color Substitution • EXAMPLE – Find a classification color range for blood and substitute a false color everywhere a pixel falls into the color range

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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.

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Color Segmentation image decomposition by color contrast enhanced original

luminance

color unit vector

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color unit vector

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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

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Nose Image Nose Image from D2000

Color Unit Vectors Contrast Stretched

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Unit Vector Color Segmentation

R

G

B

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.

GB

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False Color By Luminance Baseline 24 indexed color WHT and BLK

d3

b12

b5

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b12d5

b8

b12d8

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Combining Unit Vectors and Luminance (angle and interval)

0.3-0.7

3 degrees

0.4-0.8 3 degrees Š 2008 R. Schneider

0.5-0.9 3 degrees 22

Image Pixels

5 degrees 0.7 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

6 degrees 0.8 to 0.9

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Left and Right Cheeks

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Cloth & Image Stripes and Interstitials i3541,13625

c5474,7284 i3293,13666

i3424,13312 i3693,3937

c4928,7019

i3304,13858 c2453,13512

Generated by sorting pixels by luminance and binary splitting at the median

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c3646,14588 24

Blood & Scorch Stripe and Interstitial

b3545,1609

b3989,15828

b3231,13352

b3333,13352

b5148,15535

b5392,16734

b2768,9341

darkScorch

b2931,17472

s5100,10065

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Phi Theta Luminance • (φ,θ, L) convenient coordinate system where (φ,θ) defines the color and L the intensity INTERSTITIAL

original

interstitial WHT STRIPE

blood RED

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)

combination RED/WHT © 2008 R. Schneider

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Transference

• Using intervals from one sample on another • Wrist wound intervals applied to chest wound

original

interstitial WHT

blood RED

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combined RED and WHT 27

Color & Luminance Blood Luminance Stripe

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 wrist

Luminance Interstitial

note median cuts

Stripe

φ,θ Color Space High Overlap

Interstitial © 2008 R. Schneider

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Mean of Stripe & Interstitials blood image

cloth

• 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. © 2008 R. Schneider

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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:

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cs: 85% white ci: white bs: red [1 0 0] bi: white 60% gray is: orange [1 .4 0] ii: flesh [1 .8 .6]

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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

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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

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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

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Thankyou All for Listening Š 2008 R. Schneider

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Additional Slides Not In Talk

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Color & Luminance Cloth c7 r c8 b c9 g c10 m

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Color & Luminance Image

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30 Blood Sample Unit Vectors

Cluster Relatively Tightly

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Same Measures in RGB 0..255

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Blood and Open Cloth 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

Scatter of Blood and Cloth Unit Color Vector Samples from 30 Blood Samples and 136 Cloth Sample Points Š 2008 R. Schneider

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RGB Plot of Image Samples b (blue) = tip of nose g (green) = left cheek r (red) = right eye c (cyan) = right cheek m (magenta) = right calf

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Clean and Image Cloth Hard to Separate Image and Cloth

Bands on Side of Face

Tip of Nose

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Example Banding

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sample c2

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General Coordinates A Natural System Dorsal

613

2

6373

Ventral 1

12133

1

17894

2

23654

R C

L R=Right C=Center L=Left D=Dorsal V=Ventral

c=cloth b=blood i=image s=scorch w=waterstain margins m=miscellaneous

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 Š 2008 R. Schneider 44 bone weave, i.e. /// slanted up and to the right

Cloth Samples Š 2008 R. Schneider

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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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