1. An E-team on statistical techniques for unsupervised segmentation and classification E. Salerno CNR – Istituto di Scienza e Tecnologie dell’Informazione Pisa, Italy Muscle Joint WP5-WP7 Focus Meeting, Rocquencourt, December 2005

2. Overview Unsupervised processing: Why? Statistical approach What we have done What we propose What we would like to share with partners

3. Unsupervised processing: why? Unsupervised processing is often essential in important applications Document image analysis Showthrough cancellation Yo no quiero encarecerte el servicio que te hago en darte a conocer tan notable y tan honorado caballero; pero quiero que me agradezcas... OCR Remote sensing Thematization Classification

4. Statistical approach Problem setting A data model A source model A statistically significant data sample Learn the model (use statistics) Estimate the sources (inverse problem)

5. Statistical approach Methods Independent component analysis Dependent component analysis Bayesian approaches Applications Multispectral data analysis Multisensor data analysis Multiview data analysis

6. What we have done in document image analysis Original Recovery of bleed-through Color decorrelation

7. Attenuation of stains What we have done in document image analysis Color decorrelation

8. Data Output 1 Output 3 Output 2 What we have done in document image analysis Independent component analysis Text extraction from ancient palimpsests © The Owner of the Archimedes Palimpsest

9. Text separation from color document scans Edge-preserving Bayesian approach What we have done in document image analysis Main text pattern at convergence Show-through outline at convergence Main text outline at convegence Show-through pattern at convergence

10. What we have done in document image analysis Other document image processing applications Watermark extraction Joint deblurring and separation Color restoration Show-through cancellation/extraction from recto-verso grayscale scans

11. What we propose E-team on statistical techniques for unsupervised segmentation and classification We are looking for partners with similar interests to collaborate in Extensive experimentation of available procedures on multispectral document data Development of specific data models for color/multispectral or grayscale recto-verso document images Ad-hoc registration procedures for recto and verso pages Joint deblurring-segmentation Training (exploit MUSCLE fellowships)

12. What we propose What we would like to share with partners ICA software for text extraction Expertise in separation and deblurring procedures Graylevel recto-verso test database (Gerolamo Cardano’s Contradicentium Medicorum, 1663)

13. What we propose People at ISTI Anna Tonazzini Ercan Kuruoglu Emanuele Salerno MUSCLE Fellow(s) Research collaborators