In human interactions, hands are a powerful way of expressing information that, in some cases, can be used as a valid substitute for voice, as it happens in Sign Language. Hand gesture recognition has always been an interesting topic in the areas of computer vision and multimedia. These gestures can be represented as sets of feature vectors that change over time. Recurrent Neural Networks (RNNs) are suited to analyse this type of sets thanks to their ability to model the long term contextual information of temporal sequences. In this paper, a RNN is trained by using as features the angles formed by the finger bones of human hands. The selected features, acquired by a Leap Motion Controller (LMC) sensor, have been chosen because the majority of human gestures produce joint movements that generate truly characteristic corners. A challenging subset composed by a large number of gestures defined by the American Sign Language (ASL) is used to test the proposed solution and the effectiveness of the selected angles. Moreover, the proposed method has been compared to other state of the art works on the SHREC dataset, thus demonstrating its superiority in hand gesture recognition accuracy.• the search of a robust solution able to recognize also gestures that are similar to each other; • the achievement of the highest accuracy level compared with works of the current literature.

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The internal inspection of large pipeline infrastructures, such as sewers and waterworks, is a fundamental task for the prevention of possible failures. In particular, visual inspection is typically performed by human operators on the basis of video sequences either acquired on-line or recorded for further offline analysis. In this work, we propose a vision-based software approach to assist the human operator by conveniently showing the acquired data and by automatically detecting and highlighting the pipeline sections where relevant anomalies could occur.