Digital Image Analysis of the Shroud of Turin

© 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.