Abstract
Several image mosaicing algorithms claiming to advance the state of the art have been proposed so far. Though sometimes improvements can be recognised without quantitative evidences, the importance of a principled methodology to compare different algorithms is essential as this discipline evolves. Which is the best? What means the best? How to ascertain the supremacy? To answer such questions, in this paper we propose an evaluation methodology including standard data sets, ground-truth information and performance metrics. We also compare three variants of a well-known mosaicing algorithm according to the proposed methodology.
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Azzari, P., Di Stefano, L., Mattoccia, S. (2008). An Evaluation Methodology for Image Mosaicing Algorithms. In: Blanc-Talon, J., Bourennane, S., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2008. Lecture Notes in Computer Science, vol 5259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88458-3_9
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DOI: https://doi.org/10.1007/978-3-540-88458-3_9
Publisher Name: Springer, Berlin, Heidelberg
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