Hyperspectral Imaging applications in art and archaeology PRESENTING: OMER PAPARO JANUARY 2013.

Hyperspectral Imaging applications in art and

archaeology

PRESENTI NG:OMER PAPAROJANUARY 2013

Agenda

Introduction Motivation Limitations

Spectral imaging systemsPigment identification

The Kubelka-Munk theory of reflectanceInvestigating materials present on artifactsRevealing hidden information

In paintings Studying archaeological manuscripts

Art conservation Conserving paintings Best illuminants for viewing art

Introduction

Motivation Traditional methods are often invasive

E.g., micro-chemical analysis of images HI is non invasive

Can be carried out essentially on any object Can be carried out anywhere on an object

Limitations Requires exposure to light

Some artifacts suffer light-induced ageing Not always as accurate as traditional methods

Invasive chemical analyses, for instance, almost always yield more chemically specific information

Spectral imaging systems

Illumination Light exposure must be kept at a minimum (both duration

and intensity) Assuming the reciprocity principle ~200 lux for oil paintings, ~50 lux for manuscripts

Wavelength selection Wavelength selection through illumination

Only a selected wavelength range of light is incident on the object at a time Economic exposure, yet sensitive to background light

Wavelength selection in the reflected light Light reflected from the object can be separated spectrally Can collect spectral data sequentially or simultaneously

Detector


Working scheme:


Measurement at the Uffizi Gallery, Florence, Italy - Leonardo room

Pigment identification

Introduction – What are paintings made of? A pigment is a colored material ground into a fine

powder After the grinding it is suspended in some type of media

that acts as a binder to hold the dry pigments pigment together E.g. linseed oil for oil paints

Over the eras, many different pigments were used


E.g., the late gothic palette


E.g., the late Italian Renaissance palette


The main challenge is unmixing measured reflectance to separate reflectances of different materials Linear unmixing won’t work here – the mixing is not

linear (materials can be mixed almost to atomic level)


Measuring reflectance is relatively easy Suppose we’ve measured for some pixel, for same

wavelength , the reflectance The ratio between the outgoing light and the incoming light Now what?

That reflectance, R, must be a combination of reflectances of more than one material found in that pixel But how can we separate them?

Maybe the combined reflectance is a linear combination of those reflectances?• Well, not exactly

Introducing the Kubelka-Munk theory of reflectance


The Kubelka-Munk Theory of Reflectance: dx +Sdx dx +Sdx

Where K is theabsorption coefficientand S is thescattering coefficient


The Kubelka-Munk Theory of Reflectance (cont’d): It thus can be achieved that Defining as the reflectance of the sheet and we get

that , hence


The Kubelka-Munk Theory of Reflectance (cont’d): Rearranging and integrating we get: . Solving this

yields , where and So assuming:

(no light gets to the back) (particle sizes are much smaller than the thickness of

the layer)

We can achieve that


The Kubelka-Munk Theory of Reflectance (cont’d): Other than cases in which the absorption is very high

or the scattering is very low, a mixture of different paint components can be modeled as a linear combination of K/S (weights are according to concentrations)

Can predict components of mixture! Graph shows mixture of

read earth and azuritein egg tempera

KM can fail E.g., in the mix of pure indigo and orpiment Would not have failed if the indigo was mixed with

lead white


But generally, KM is robust Can handle varying:

Concentrations Binding medium Particles size

Investigating materials present on artifacts

Similarly to pigment identification, we can perform analysis on 3D objects E.g., exploring the surface of

Michelangelo's David Basically the sculpture is made of

marble, but over the years some“guests” have joined


Collecting and analyzing the data A UV ( = 337 nm) excitation light is provided by a

nitrogen laser that generates 1 ns pulses N pulses are delivered and the emission is measured

Assuming mono-exponential behavior of the fluorescent emission, f, we get that (per pixel) Where is the amplitude and is the effective lifetime

Given the pulse was provided with delay d, we can acquire the fluence: Where is the gate width and is a constant dependent on

the efficiency of the detection system


Collecting and analyzing the data (cont’d) The effective lifetime and the amplitude can be

reconstructed by least mean squares fit performance on N time samples:

Can build matrices of and


Results Spectral signature is different than the one of “pure

marble”


Results (cont’d) Can identify organic compounds


Results (cont’d) Can identify remains of beeswax

David’s surface underwent aconservation treatmentbased on beeswax in 1813

Revealing hidden information

For paintings: Maximum penetration of most paints can be achieved

at wavelengths of around 2 μm At wavelengths around 1-2 μm, the common drawing

materials, namely iron gall ink and sepia, become invisible

Can use this to see underdrawings and preparatory sketches


A Byzantine icon at 640nm (a) and 1000nm(b)


Pablo Picasso –“The Tragedy”


The optimal spectral window to visualize such features varies with the material used as well as the thickness of the paint layer

Man, ~1100nm

Horse, ~1350nm

Sketch, ~1600nm


A painting by Sellaio

520nm 885nm RGB


Studying archaeological manuscripts “Soft media” ancient documents (i.e. documents

written on soft materials such as leather or papyrus) are often unreadable The carbon-black ink is faded beyond recognition The carbon-black ink indistinguishable from the surface Not to mention the document itself is found in shreds


Studying archaeological manuscripts Can use IR to read previously invisible texts and

scripts The dead sea scrolls can only be seen through IR light

Art conservation

Conserving Paintings Can fix damage using hidden information revealing

techniques The color image is derived from inter-band comparisons

Art conservation

Conserving Paintings (cont’d) Conservation monitoring

Can identify continual damage to paintings, for example From a lamp in front of the painting From a pipe going through the ceiling

Art conservation

Best illuminants for viewing art Which one looks better?

Art conservation

Best illuminants for viewing art (cont’d) An illuminant for

appreciating art isconsidered betterif number ofdiscernible coloursis greater

Illuminants aremeasured indegrees Kalvin

Art conservation

Best illuminants for viewing art (cont’d) The experiment:

1. Collect hyperspectral data from five different paintings

Art conservation


2. Calculate the illuminant spectra 3. Compute the painting representation in CIELAB

Art conservation


4. Count the number of non-empty unit cubes in the CIELAB space, and select best illuminant

Concluding

Today we have seen: Basic structures of spectral imaging systems for art

and manuscripts Uses for hyperspectal imaging in art and archeology:

Identifying pigments used for paintings Investigating materials present on artifacts Viewing underlying sketches for paintings Studying old and corrupted-by-time documents Conserving art

Protecting art from harm Viewing in with best illuminant

References

H. Liang - Advances in multispectral and hyperspectral imaging for archaeology and art conservation – 2011

C. Fischer and I. Kakoulli - Multispectral and hyperspectral imaging technologies in conservation: current research and potential applications – 2006

J. K. Delaney et al - Visible and Infrared Reflectance Imaging Spectroscopy of Paintings: Pigment Mapping and Improved Infrared Reflectography – 2009

F. Voltolini et al - Integration of non-invasive techniques for documentation and preservation of complex architectures and artwork

J.A. Carvalhal et al - Estimating the best illuminants for appreciation of art paintings

G.H. Bear-man et al - Archeological Applications of Advanced imaging Techniques

D. Comelli et al - Fluorescence Lifetime Imaging and Fourier Transform Infrared Spectroscopy of Michelangelo’s David - 2005

Thank you

Questions?

Hyperspectral Imaging applications in art and archaeology PRESENTING: OMER PAPARO JANUARY 2013.

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