Abstract-A method for object recognition, invariant under translation, rotation, and scaling, is addressed. The first step of the method (preprocessing) takes into account the invariant properties of the normalized moment of inertia and a novel coding that extracts topological object characteristics. The second step (recognition) is achieved by using a holographic nearest-neighbor algorithm (HNN), in which vectors obtained in the preprocessing step are used as inputs to it. The algorithm is tested in character recognition, using the 26 upper case letters of the alphabet. Only four different orientations and one size (for each letter) were used for training. Recognition was tested with 17 different sizes and 14 rotations. The results are encouraging, since we achieved 98% correct recognition. Tolerance to boundary deformations and random noise was tested. Results for character recognition in "real" images of car plates are presented as well.

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Collaborative tasks between human operators and robotic manipulators can improve the performance and flexibility of industrial environments. Nevertheless, the safety of humans should always be guaranteed and the behaviour of the robots should be modified when a risk of collision may happen. This paper presents the research that the authors have performed in recent years in order to develop a human-robot interaction system which guarantees human safety by precisely tracking the complete body of the human and by activating safety strategies when the distance between them is too small. This paper not only summarizes the techniques which have been implemented in order to develop this system, but it also shows its application in three real human-robot interaction tasks.

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