Robust Pattern Matching performance evaluation dataset

This webpage illustrates the testbed used for a performance evaluation and comparison of robust measures in the context of pattern matching [1]. The aim of the research work presented in [1] was to test and compare, under challenging conditions and within a pattern matching scenario, several measures proposed in literature which are robust towards photometric distortions, partial occlusions and noise. The dataset used for the performance evaluation is publicly available at this page so to allow anyone interested to test and compare his own - as well as others - proposals. All images belonging to the dataset are explicitly affected only by real distortions, in order to recreate the conditions typically found along pattern matching applications. We also plan to extend the currently available dataset with additional images acquired under different conditions.


Currently the testbed includes three image datasets:

Together with the image datasets, the ground truth is also provided which allows for a direct comparison of the tested algorithms. In particular, the groundtruth is represented by the coordinate pair (x,y) denoting at each image-template instance the position of the pattern within the image search area. NOTE: The images of the datasets are in general acquired from slightly different poses, this resulting in the objects appearing in the scene having slightly different sizes and slight rotations with regards to the reference patterns. Hence, in a pattern matching task which considers only translations a unique position of the pattern within the image can not be always identified. For this reason, we suggest to always allow for a margin in the coordinates of the best matching position, allowing correct matches being also those found at plus/minus k pixels with respect to the ground truth coordinates (in [1], k was set to 5).

Terms of use and download

Researchers and, in general, anyone interested in this topic is invited to download and use this dataset which comes free and for public use. In case this dataset is used to present any kind of scientific work on a publication, we require to cite [1].

Dataset (zip file)


[1] F. Tombari, L. Di Stefano, S. Mattoccia, A. Galanti, “Performance evaluation of robust matching measures", 3rd International Conference on Computer Vision Theory and Applications (VISAPP 2008), January 22-25, 2008, Funchal-Madeira, Portugal