visual enhancement of the archimedes palimpsest using a target detection algorithm

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Visual Enhancement of the Visual Enhancement of the Archimedes Palimpsest Using Archimedes Palimpsest Using a Target Detection Algorithma Target Detection Algorithm

By GaryHoffmannBy GaryHoffmann

Roger Easton, AdvisorRoger Easton, Advisor

Rolando Raqueno, AdvisorRolando Raqueno, Advisor

Credits for Winter Quarter, 2002: 2Credits for Winter Quarter, 2002: 2

orTrying 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.


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Contents• Introduction

• Specific Aims

• Background and Significance

• Experimental Design and Methods

• Resources and Environment

• Timetable

• Budget


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IntroductionOld manuscripts oftendamaged:• fading of ink• presence of mold• deliberate defacement


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Methods have been developed to restoredocuments of cultural significance

3 band color image (left) and 6 band ‘Super-visual’ image (right)


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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


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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


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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


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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


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BackgroundThe Archimedes Palimpsest


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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.


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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() - tii


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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


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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() - bnn

n1 = L() - [bnn + tmm]


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

A

B


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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


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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


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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


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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.)


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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


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Timetable

Aug. 2002 – Feb. 2003: Continue testing and verification of Lee’s algorithm on remotelysensed data.

i. Dec. 2002, weeks 1-3 – Test algorithm on diff erent subsections of AVIRIS imageused in Lee’s dissertation

ii. Dec. 2002, weeks 1-3 – Test algorithm on diff erent AV IRIS images, as availableiii. Feb. 2003 – Test algorithm on images from different sensors, as available

Nov. 2002 – Dec. 2002: Determine best way measure ink spectra prior to trip toBaltimore. See Specific Aims for detail s.• Nov. 2002, week 2 – Set upAS Dwit h x-y translati ontable, determine

senso/r illuminan tgeometry• Nov. 2002, week 3 – Determine samples pe r line and number of scan lines for a given

area of document• Nov. 2002, week 4 – Optimize spo t size, examine transmissi on o f glass plate• Dec. 2002, week 1 – Practice set upi nentirety

Dec. 2002 or J . 2003an – Tri pto Baltimor eto t ake ASD measurements and captureadditional image s oft he palimpses .t

Ja . 2003n – Apr il 2003: Implement the algorith m onimages of the palimpsest usi ng datacollected wit h ASD.• Ja . 2003n – Search for features i nink/backgr oundspectra• Feb. 2003 – Exami ne possible use of MODTRAN to creat einvarian t targe t space• March. 2003 – A pply algorithm t o collect edimagesApril 2003 – Additional research/study, as needed


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

Budget

Travel –

6 days in Baltimore, staying 7 nights with a day of driving each way.

Hotel, $100 per night for 7 nights - $700Food, $30 per day for 7 days - $210Gas, $40 each way, $40 for travel in the city - $120Tolls, $10 each way - $20

Total - $1050


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For additional information:Gypson, Matthew. “Rendering Multispectral Data as Useful ‘Super-Visual’ Images.”

Senior Thesis, Rochester Institute of Technology, Center for Imaging Science,2000.

Lee, Kyungsuk. “A Subpixel Scale Target Detection Algorithm for HyperspectralImaging.” Ph.D. Dissertation, Rochester Institute of Technology, Center forImaging Science, 2002.

The Archimedes Palimpsest. The Walters Art Gallery. Accessed Oct. 1, 2002.<http://www.thewalters.org/archimedes/frame.html>.

Slater, D. and Healey, G. “Models and Methods for Automated Material Identification inHyperspectral Imagery Acquired Under Unknown Illumination and AtmosphericConditions.” IEEE Transactions on Geoscience and Remote Sensing, Vo l. 37, No.6, pp. 2706-2717, November 1999.

Schott, J. R., Lee, K., Raqueno, R. V. , Hoffmann, G. D. “Use of Physics Based Models inHyperspectral Image Exploitation.” AIPR 2002 Workshop, Washington, D.C.,IEEE Computer Society Press. In Press.

Walvoord, Derek. “Quality of Signal Detection Using an Approximate Matched Filter.”Senior Thesis, Rochester Institute of Technology, Center for Imaging Science,2002.

DIRS Equipment: ASD Fieldmaster. The Digital Imaging and Remote Sensing (DIRS)Group. Accessed Oct. 28, 2002.<http://www.cis.rit.edu/research/dirs/resources/equipment/asd.html>

visual enhancement of the archimedes palimpsest using a target detection algorithm

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