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Multi-Dimensional Analysis of An Oil Painting

Multi-Dimensional Analysis of An Oil Painting. Center of Imaging Science Rochester Institute of Technology Pano Spiliotis May 7,1999. Overview. Background Procedure Results Evaluation of data Conclusion. Background. Multi-dimensional segmentation.

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Multi-Dimensional Analysis of An Oil Painting

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  1. Multi-Dimensional Analysis of An Oil Painting Center of Imaging Science Rochester Institute of Technology Pano Spiliotis May 7,1999

  2. Overview • Background • Procedure • Results • Evaluation of data • Conclusion

  3. Background • Multi-dimensional segmentation. • Using image capturing protocols and multi-dimensional segmentation, artifacts that are present in works of art will be easily identified. • New approach in finding artifacts in oil paintings.

  4. Overview • Background • Procedure • Results • Evaluation of data • Conclusion

  5. Camera Setup • Monochrome CCD camera • Ultraviolet Black Light (Blue light) • Tungsten Light Source • Infrared Filter Important that images are in registration.

  6. Illumination • Three types • Ultraviolet • Visible • Infrared

  7. Ultraviolet Illumination • Ultraviolet Illumination • New Varnish • Over-painting

  8. Visible Illumination • Topographical Inspection • Canvas Repair • Buckling of Canvas • Over-painting

  9. Infrared Illumination • Infrared filter is presented to the system. • Under-drawing • Areas that are not present in the UV and VIS.

  10. Flat Fielding • Procedure taking a non-uniformly illuminated image and resulting in an evenly illuminated image. Flat-fielded image non-uniformly illuminated image

  11. Image Segmentation • Looking for common areas. • Segmentation of a common area. • Trial and error. • Save common area as an image and pixel value.

  12. Overview • Background • Procedure • Results • Evaluation of data • Conclusion

  13. Results Visible image Infrared image Ultraviolet image

  14. 2-D Segmentation Program Visible Image Visible Image Infrared Image Infrared Image

  15. Pseudo Coloring Visible Image Infrared Image

  16. After Segmentation Visible Image Visible Image Infrared Image Infrared Image

  17. Overview • Background • Procedure • Results • Evaluation of data • Conclusion

  18. Evaluation of results • Areas that have been segmented reveal artifacts. • John Sutton, Associate Conservator from The West Lake Conservators, was called in to prove these findings. • The halo is not the original halo painted.

  19. Conclusion • Research proved to be successful. • 2-D Segmentation provides art conservator’s with a tool to validate their discoveries.

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