Abstract
Computer vision as any other scientific field can be divided into theoretical and experimental parts. A set of reasoned ideas proposed in the theoretical part is verified by experiments. Furthermore, discernible discrepancies between theory and real-world facts and events are discovered during experiments. Thus, both theoretical and experimental parts are coupled creating a spiral on which a theoretical model is improved in the sense that discrepancies become smaller and smaller. The discrepancies are often measured by differences between theoretical and measured quantities if both are well established as they are in physics. However, we are still searching for such simple quantities in a number of fields including computer vision. The traditional solution is to define a set of performance indices and interpretation rules. From this point of view, it sounds rather curiously that performance evaluation of computer vision algorithms measuring discrepancies is not wide supported and must be still justified (Forstner, 1996).
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Mařík, R. (2000). Quality in Computer Vision. In: Klette, R., Stiehl, H.S., Viergever, M.A., Vincken, K.L. (eds) Performance Characterization in Computer Vision. Computational Imaging and Vision, vol 17. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-9538-4_4
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DOI: https://doi.org/10.1007/978-94-015-9538-4_4
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