Abstract
Benchmark data sets consisting of image pairs and ground truth homographies are used for evaluating fundamental computer vision challenges, such as the detection of image features. The mostly used benchmark provides data with only low resolution images. This paper presents an evaluation benchmark consisting of high resolution images of up to 8 megapixels and highly accurate homographies. State of the art feature detection approaches are evaluated using the new benchmark data. It is shown that existing approaches perform differently on the high resolution data compared to the same images with lower resolution.
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Cordes, K., Rosenhahn, B., Ostermann, J. (2013). High-Resolution Feature Evaluation Benchmark. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds) Computer Analysis of Images and Patterns. CAIP 2013. Lecture Notes in Computer Science, vol 8047. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40261-6_39
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DOI: https://doi.org/10.1007/978-3-642-40261-6_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40260-9
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