Finding objects, such as cars, bottles, glasses, hats etc. in large image collections, has been a common task in computer vision. The efficiency and accuracy of algorithms to retrieve objects or selected image regions from digital images, has lead to their application to art data. Data sets have been evaluated, studying recurring motifs over time and space, thus helping to reveal artistic relations and general commonalities in art.

 

The group has performed object detection on various data sets, including paintings of crucifixion, architectural prints or medieval manuscripts to assist with art historical research. The following site provides an overview over done work and demonstrates the efficiency of computational tools to detect regions and eventually to evaluate art.

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Click on the left images to get more information on our projects.

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Publications (selection)

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Lang, Sabine and Ommer, Björn (2018): Reconstructing Histories: Analyzing Exhibition Photographs with Computational Methods. Arts, Computational Aesthetics, 7:64, pp. 1-21.

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Monroy, Antonio, Kröger, Till, Arnold, Michael and Ommer, Björn (2011): Parametric Object Detection for Iconographic Analysis. Scientific Computing & Cultural Heritage.

 

Schlecht, Joseph, Carque, Bernd and Ommer, Björn (2011): Detecting Gestures in Medieval Images. Proceedings of the International Conference on Image Processing. IEEE, pp. 1309-1312.

 

Schlecht, Joseph and Ommer, Björn (2011): Contour-based Object Detection. Proceedings of the British Machine Vision Conference.

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