Rolando Raqueno, Advisor Credits for Winter Quarter, 2002: 2 Visual Enhancement of the Archimedes Palimpsest Using a Target Detection Algorithm or Trying to read ancient Greek written over a thousand years ago that’s been burned, erased, overwritten, torn apart, put back together, and has mold growing on it. By GaryHoffmann Roger Easton, Advisor Rolando Raqueno, Advisor Credits for Winter Quarter, 2002: 2

Contents Introduction Specific Aims Background and Significance Experimental Design and Methods Resources and Environment Timetable Budget

Introduction Old manuscripts often damaged: fading of ink presence of mold deliberate defacement

Methods have been developed to restore documents of cultural significance 3 band color image (left) and 6 band ‘Super-visual’ image (right)

Hyperspectral Imagery Hundreds of bands Provides more information than a multispectral image Could be used to improve document restoration techniques Target detection methods could be used on hyperspectral images of old manuscripts

Specific Aims Continue Verification of Kyungsuk Lee’s algorithm for target detection Test algorithm on different subsections of AVIRIS scene used during development Different AVIRIS images Different sensors - MISI, Hyperion, HYDICE, etc. Possible thresholds that can be used to automate the algorithm

Specific Aims Capture more hyperspectral imagery of Archimedes Palimpsest using tunable filter/CCD and an Analytical Spectral Device (ASD) Sensor/illuminant geometry Samples per line with ASD and lines for a given area ASD spot size and time required to acquire data for a given area Constant illumination Transmission of glass plate Compare to ink spectra previously measured ASD dark image

Specific Aims Analyze data and implement algorithm on manuscript Usable spectral features in ink Use MODTRAN to find appearance of ink given all possible geometries Apply algorithm to collected images

Background The Archimedes Palimpsest

Method to locate underwriting: Material identification with hyperspectral imagery High spectral-dimensionality Can differentiate materials that cannot be differentiated using sensors with fewer bands Detection of materials made difficult by variations in illumination across image Sensor/illuminant geometry Atmospheric conditions (remote sensing) Etc.

Glenn Healey’s Invariant Target Detection Method Dimensionality of possible spectral radiance vectors for target less than dimensionality of sensor Vector set is invariant to illumination (spans all possible appearances of target for different conditions) Some set of basis vectors will span this set Any target vector is linear combination of basis vectors n = L(l) - Stiai

Determining invariant target space: Use of physics based models All parameters varied across all extremes Apply to target reflectance vector Resulting space invariant to illumination

Kyungsuk Lee’s Target Detection Method Extends to subpixel scale Find basis vectors for background, as well Hypothesis test: Each pixel is linear combination background basis vectors only (target not present) Each pixel is linear combination of target basis vectors and background basis vectors (target present) n0 = L(l) - Sbngn n1 = L(l) - [Sbngn + Stmam]

Kyungsuk Lee’s Target Detection Method Find probability of each hypothesis Maximize ratio of the probabilities at each pixel location Threshold to locate targets B A

Significance Verify algorithm for use in hyperspectral remote sensing Prove algorithm’s potential application to areas other than remote sensing Prove potential usage of hyperspectral imaging in document restoration New tool for scholars Locate spectral features useful in future research Make data available to other researchers in the field of document restoration

Experimental Design and Methods Verification of Algorithm As described under specific aims - different images and targets Concentrate on thresholds Collection of ink spectra Travel to Walters Art Gallery in Baltimore, MD Tungsten-halogen illumination Take direct reflectance measurements of Palimpsest with ASD Image with CCD mounted with tunable filter ranging from 400-2500 nm

Experimental Design and Methods Implementation of algorithm on palimpsest Creation of invariant space Method 1 - use MODTRAN to characterize illumination variation due to sensor/illuminant geometry, then find basis vectors Method 2 - flat-field images to eliminate variations in illumination, use scene pixels to determine target basis vectors

Experimental Design and Methods Implementation of algorithm on palimpsest Search for underwriting ink Method A - use underwriting ink as target Method B - if under/overwriting inks are not spectrally distinct, use “ink” as target and subtract overwriting ink from final image Method C - combine above with spatial methods of document restoration (approximate matched filters, etc.)

Resources and Environment SUN workstations and existing images of Palimpsest Analytical Spectral Device Spectral range: 350-2500 nm, 1.5 - 2 nm increments Fiber optic cable used alone or with lenses to reduce field of view (from 5 degrees to 3 degrees or 1 degree) X-Y translation table, CCD camera, liquid crystal tunable filter

This may change depending on arrangements with the museum and my availability.

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