Abstract
Abstract. This contribution shows the generation of a benchmark dataset using historical images. The difficulties when working with historical images are pointed out and structured in three categories. Especially large viewpoint differences, image artifacts and radiometric differences lead to weak matching results with classical feature matching approaches. The necessity of publishing an own benchmark dataset is emphasized when comparing to existing datasets which are partly using synthetic data, well-known orientation or strictly categorized image differences. The presented image dataset consists at the moment of 24 images which are oriented in image triples using the properties of the Trifocal Tensor as a more stable image geometry. In the following, three different feature detectors and descriptors that have already been proven well on historical images (MSER, ORB, RIFT) are evaluated using the new benchmark dataset. Then, several outlier removal methods were applied on the detected features. The tests show that for the entirety of image pairs RIFT performs slightly better than the other two methods. Nonetheless, for some image pairs MSER significantly improves the matching score but even so, historical image pairs are difficult to be matched with the presented methods due to challenging outlier removal. Still, the estimated projective relative orientation could be used in an autocalibration approach to place the images in a metric scene.
Highlights
This contribution presents the generation of a benchmark dataset for the evaluation of different feature matching methods on historical images
Some of the historical images even have large viewpoint or illumination changes like in the Extreme View Dataset or the Ultra Wide Baseline Dataset (Mishkin et al, 2015). These existing datasets are using the fact that “the images are either of planar scenes or the camera position is fixed during acquisition, so that in all cases the images are related by homographies [..] and this mapping is used to determine ground truth matches [..].” (Mikolajczyk et al, 2005). This is not possible when using historical data, so the presented benchmark dataset is described by the predefined corresponding points and the Trifocal Tensor determining the relative orientation between image triples
Difficulties determining the relative orientation of the data arise due to large image differences and unknown camera parameters
Summary
This contribution presents the generation of a benchmark dataset for the evaluation of different feature matching methods on historical images. The work is placed in the context of a 4D web application (3D models and related historical images and data) of the city of Dresden as an alternative media repository for e.g. art historians. Oriented images and methods to match historical images provide the basis for the placement of the images in such a 3D space. The images for the benchmark dataset were redigitized for this purpose and show various buildings. The images have different properties ranging from small viewpoint and radiometric changes to large differences. These properties can be summarized in the following three categories
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