Abstract-A novel metric for time series, called MSM (move-split-merge), is proposed. This metric uses as building blocks three fundamental operations: Move, Split, and Merge, which can be applied in sequence to transform any time series into any other time series. A Move operation changes the value of a single element, a Split operation converts a single element into two consecutive elements, and a Merge operation merges two consecutive elements into one. Each operation has an associated cost, and the MSM distance between two time series is defined to be the cost of the cheapest sequence of operations that transforms the first time series into the second one. An efficient, quadratic-time algorithm is provided for computing the MSM distance. MSM has the desirable properties of being metric, in contrast to the dynamic time warping (DTW) distance, and invariant to the choice of origin, in contrast to the Edit Distance with Real Penalty (ERP) metric. At the same time, experiments with public time series datasets demonstrate that MSM is a meaningful distance measure, that oftentimes leads to lower nearest neighbor classification error rate compared to DTW and ERP.

The lack of a written representation for American Sign Language (ASL) makes it difficult to do something as commonplace as looking up an unknown word in a dictionary. The majority of printed dictionaries organize ASL signs (represented in drawings or pictures) based on their nearest English translation; so unless one already knows the meaning of a sign, dictionary look-up is not a simple proposition. In this paper we introduce the ASL Lexicon Video Dataset, a large and expanding public dataset containing video sequences of thousands of distinct ASL signs, as well as annotations of those sequences, including start/end frames and class label of every sign. This dataset is being created as part of a project to develop a computer vision system that allows users to look up the meaning of an ASL sign. At the same time, the dataset can be useful for benchmarking a variety of computer vision and machine learning methods designed for learning and/or indexing a large number of visual classes, and especially approaches for analyzing gestures and human communication.

In human-computer interaction applications, gesture recognition has the potential to provide a natural way of communication between humans and machines. The technology is becoming mature enough to be widely available to the public and real-world computer vision applications start to emerge. A typical example of this trend is the gaming industry and the launch of Microsoft's new camera: the Kinect. Other domains, where gesture recognition is needed, include but are not limited to: sign language recognition, virtual reality environments and smart homes. A key challenge for such real-world applications is that they need to operate in complex scenes with cluttered backgrounds, various moving objects and possibly challenging illumination conditions. In this paper we propose a method that accommodates such challenging conditions by detecting the hands using scene depth information from the Kinect. On top of our detector we employ a dynamic programming method for recognizing gestures, namely Dynamic Time Warping (DTW). Our method is translation and scale invariant which is a desirable property for many HCI systems. We have tested the performance of our approach on a digits recognition system. All experimental datasets include hand signed digits gestures but our framework can be generalized to recognize a wider range of gestures.

Abstract. A method is presented to help users look up the meaning of an unknown sign from American Sign Language (ASL). The user submits a video of the unknown sign as a query, and the system retrieves the most similar signs from a database of sign videos. The user then reviews the retrieved videos to identify the video displaying the sign of interest. Hands are detected in a semi-automatic way: the system performs some hand detection and tracking, and the user has the option to verify and correct the detected hand locations. Features are extracted based on hand motion and hand appearance. Similarity between signs is measured by combining dynamic time warping (DTW) scores, which are based on hand motion, with a simple similarity measure based on hand appearance. In user-independent experiments, with a system vocabulary of 1,113 signs, the correct sign was included in the top 10 matches for 78% of the test queries.

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