In this paper, we develop and study a new algorithm to recognize and precisely measure keys for the ultimate purpose of physically duplicating them. The main challenge comes from the fact that the proposed algorithm must use a single picture of the key obtained from a regular desktop scanner without any special preparation. It does not use the special lasers, lighting systems, or camera setups commonly used for the purpose of key measuring, nor does it require that the key be placed in a precise position and orientation. Instead, we propose an algorithm that uses a wide range of image processing methods to discover all the information needed to identify the correct key blank and to find precise measures of the notches of the key shank from the single scanned image alone. Our results show that our algorithm can correctly differentiate between different key models and can measure the dents of the key with a precision of a few tenths of a millimeter.

Clustering algorithms are used in many Natural Language Processing (NLP) tasks. They have proven to be popular and effective tools to use to discover groups of similar linguistic items. In this exploratory paper, we propose a new clustering algorithm to automatically cluster together similar sentences based on the sentences’ part-of-speech syntax. The algorithm generates and merges together the clusters using a syntactic similarity metric based on a hierarchical organization of the parts-of-speech. We demonstrate the features of this algorithm by implementing it in a question type classification system, in order to determine the positive or negative impact of different changes to the algorithm.