Abstract
In this paper, we are interested in stitching specific types of images such as schemes, cartographies, documents or drawings that have been acquired using a scanner. Because of the size of these documents, it is not possible to make one acquisition even using large scanners. The result of the acquisition is then an image mosaic that needs to be stitched to obtain the entire image. For that purpose, we propose an adaptation of feature based methods that are not directly usable with the images we want to process. Indeed, points of interest (POIs) extraction on the entire image requires too much memory and matching are not always pertinent because of the particularity of these documents. To demonstrate the good performance of our proposition, we present quantitative and qualitative results obtained using two datasets: a set of images divided synthetically and a set of images that have been acquired manually using a scanner.
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Acknowledgments
The piXL project is supported by the “Fonds national pour la Société Numérique” of the French State by means of the “Programme d’Investissements d’Avenir”, and referenced under PIA-FSN2-PIXL. For more details and resources, visit http://valconum.fr/index.php/les-projets/pixl
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Paulhac, L., Domenger, JP. (2014). A Stitching Method for Large Document Images. In: Lamiroy, B., Ogier, JM. (eds) Graphics Recognition. Current Trends and Challenges. GREC 2013. Lecture Notes in Computer Science(), vol 8746. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44854-0_12
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DOI: https://doi.org/10.1007/978-3-662-44854-0_12
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