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Lines are one of the basic features that are used to characterise the content of an image and to detect objects. Unlike edges or segmented blobs, lines are not only an accumulation of certain feature pixels but can also be described in an easy and exact mathematical way. Besides a lot of different detection methods, the Hough transform has gained much attention in recent years. With increasing processing power and continuous development, computer vision algorithms get more powerful with respect to speed, robustness and accuracy. But there still arise problems when searching for the best parameters for an algorithm or when characterising and evaluating the results of feature detection tasks. It is often difficult to estimate the accuracy of an algorithm and the influences of the parameter selection. Highly interdependent parameters and preprocessing steps continually lead to only hardly comprehensible results. Therefore, instead of pure trial and error and subjective ratings, a systematic assessment with a hard, numerical evaluation criterion is suggested. The paper at hand deals with the latter ones by using a human-generated ground truth to approach the problem. Thereby, the accuracy of the surveyed Kernel-based Hough transform algorithm was improved by a factor of three. These results are used for the tracking of cylindrical markers and to reconstruct their spatial arrangement for a biomedical research application.
This article discusses the relevance of the motion behavior and adaptation of a collaborative robot for human-robot cooperation. Two experiments on cooperative assembly are shown. First, a human-human experiment with defined test conditions evaluates the aspects of distance, nearest body part, and predictability as significant. Second, a human-robot experiment shows that fixed trajectories and conservative dynamic parameters lead to a quick gain of confidence of the participants. Besides, the data shows that a realistic use case with complex tasks is key to evaluate the impact of motion parameters.