C. Richard Johnson, Paul Messier, William A. Sethares, Andrew G. Klein, Christopher Brown, Anh Hoang Do, Philip Klausmeyer, Patrice Abry, Stephane Jaffard, Herwig Wendt, Stephane Roux, Nelly Pustelnik (+9 others)
2014 Journal of the American Institute for Conservation  
Surface texture is a critical feature in the manufacture, marketing, and use of photographic paper. Raking light reveals texture through a stark rendering of highlights and shadows. Though close-up raking light images effectively document surface features of photographic paper, the sheer number and diversity of textures used for historic papers prohibits efficient visual classification. This work provides evidence that automatic, computer-based classification of texture documented with raking
more » ... ght is feasible by demonstrating an encouraging degree of success sorting a set of  images made from samples of historic silver gelatin paper. Using this dataset, four university teams applied different image-processing strategies for automatic feature extraction and degree of similarity quantification. All four approaches successfully detected strong affinities and outliers built into the dataset. The creation and deployment of the algorithms was carried out by the teams without prior knowledge of the distributions of similarities and outliers. These results indicate that automatic classification of silver gelatin photographic paper based on close-up texture images is feasible and should be pursued. To encourage the development of other classification schemes, the  sample 'training' dataset used in this work is available to other academic researchers at http://www.PaperTextureID.org.
doi:10.1179/1945233014y.0000000024 fatcat:ytzcvsalwvax7ktqsrqitzq2ra