Abstract
The principal aim of machine vision is, naturally, to develop techniques and systems that allow computers to be aware of their surroundings and take actions consequent on what is seen. Many man-years of effort have been expended on this goal; although significant progress has been made, we are arguably not much closer to solving the basic problem. While it is undoubtedly an extremely difficult goal, we are all hampered to some extent by the fact that we do not necessarily know which technique works best in which situation. The realization that this problem needs to be addressed in order for vision to become an engineering discipline rather than purely a research area has been long in coming; and this idea of knowing what to use and when is the underlying tenet of performance characterization.
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Clark, A.F., Courtney, P. (2000). Databases for Performance Characterization. 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_3
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DOI: https://doi.org/10.1007/978-94-015-9538-4_3
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