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Ground truth or reference data is the basis for performance analysis in computer vision and image processing. This paper summarizes design considerations for ground truth acquisition with a focus on accuracy and cost of different approaches. I argue that first and foremost, requirements engineering needs to be carried out and it needs to be decided whether quantitative performance analysis is beneficial to the field of research at all. Three categories of reference data are presented. Examples for each approach are given and advantages and downsides discussed. Finally, open issues are briefly summarized.
Daniel Kondermann (Mon,) studied this question.
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