Topic
Dynamic time warping
About: Dynamic time warping is a research topic. Over the lifetime, 6013 publications have been published within this topic receiving 133130 citations.
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16 Nov 2011
TL;DR: A dynamic time warping (DTW) based pre-clustering technique to significantly improve hand gesture recognition accuracy of various graphical models used in the human computer interaction (HCI) literature is proposed.
Abstract: Vision based hand gesture recognition systems track the hands and extract their spatial trajectory and shape information, which are then classified with machine learning methods. In this work, we propose a dynamic time warping (DTW) based pre-clustering technique to significantly improve hand gesture recognition accuracy of various graphical models used in the human computer interaction (HCI) literature. A dataset of 1200 samples consisting of the ten digits written in the air by 12 people is used to show the efficiency of the method. Hidden Markov model (HMM), input-output HMM (IOHMM), hidden conditional random field (HCRF) and explicit duration model (EDM), which is a type of hidden semi Markov model (HSMM) are trained on the raw dataset and the clustered dataset. Optimal model complexities and recognition accuracies of each model for both cases are compared. Experiments show that the recognition rates undergo substantial improvement, reaching perfect accuracy for most of the models, and the optimal model complexities are significantly reduced.
31 citations
22 Aug 2011
TL;DR: The key characteristic of the method is to use DTW algorithm to match corresponding pairs of histograms at every projecting angle, which allows to exploit the Radon property to include both boundary as internal structure of shapes, while avoiding compressing pattern representation into a single vector and thus miss information, thanks to the DTW.
Abstract: In this paper, we present a method for pattern such as graphical symbol and shape recognition and retrieval. It is basically based on dynamic programming for matching the Radon features. The key characteristic of the method is to use DTW algorithm to match corresponding pairs of histograms at every projecting angle. This allows to exploit the Radon property to include both boundary as internal structure of shapes, while avoiding compressing pattern representation into a single vector and thus miss information, thanks to the DTW. Experimental results show that the method is robust to distortion and degradation including affine transformations.
31 citations
TL;DR: Time warping and motion vector blending at the juncture of two divisemes and the algorithm to search the optimal concatenated visible speech are developed to provide the final concatenative motion sequence.
Abstract: We present a technique for accurate automatic visible speech synthesis from textual input. When provided with a speech waveform and the text of a spoken sentence, the system produces accurate visible speech synchronized with the audio signal. To develop the system, we collected motion capture data from a speaker's face during production of a set of words containing all diviseme sequences in English. The motion capture points from the speaker's face are retargeted to the vertices of the polygons of a 3D face model. When synthesizing a new utterance, the system locates the required sequence of divisemes, shrinks or expands each diviseme based on the desired phoneme segment durations in the target utterance, then moves the polygons in the regions of the lips and lower face to correspond to the spatial coordinates of the motion capture data. The motion mapping is realized by a key-shape mapping function learned by a set of viseme examples in the source and target faces. A well-posed numerical algorithm estimates the shape blending coefficients. Time warping and motion vector blending at the juncture of two divisemes and the algorithm to search the optimal concatenated visible speech are also developed to provide the final concatenative motion sequence. Copyright © 2004 John Wiley & Sons, Ltd.
31 citations
TL;DR: The clustering method of DTW and HMM can effectively classify driver behavior, and can be applied by automobile insurance companies, and for the development of specific training courses for drivers to optimize their driving behavior.
31 citations
TL;DR: Experimental results have successfully validated the effectiveness of the DTW-based recognition algorithm for online handwriting and gesture recognition using the inertial pen.
Abstract: This paper presents an inertial-sensorbased digital pen (inertial pen) and its associated dynamic time warping (DTW)-based recognition algorithm for handwriting and gesturer recognition. Users hold the inertial pen to write numerals or English lowercase letters and make hand gestures with their preferred handheld style and speed. The inertial signals generated by hand motions are wirelessly transmitted to a computer for online recognition. The proposed DTW-based recognition algorithm includes the procedures of inertial signal acquisition; signal preprocessing, motion detection, template selection, and recognition. We integrate signals collected from an accelerometer, a gyroscope, and a magnetometer into a quaternionbased complementary filter for reducing the integral errors caused by the signal drift or intrinsic noise of the gyroscope, which might reduce the accuracy of the orientation estimation. Furthermore, we have developed minimal intra-class to maximal inter-class based template selection method (min-max template selection method) for a DTW recognizer to obtain a superior class separation for improved recognition. Experimental results have successfully validated theeffectiveness of the DTW-based recognition algorithm for online handwriting and gesture recognition using the inertial pen. KeyWords—Inertial pen, dynamic time warping, quaternion-based complementary filter, handwriting recognition,Gesture recognition.
31 citations