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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TL;DR: In this article, the authors describe modifications of the Dynamic Time Warping (DTW) and the Parametric Time Warming (PTW) algorithms that improve the alignment quality for complex, highly variable LC-MS data sets.
Abstract: Time alignment of complex LC-MS data remains a challenge in proteomics and metabolomics studies. This work describes modifications of the Dynamic Time Warping (DTW) and the Parametric Time Warping (PTW) algorithms that improve the alignment quality for complex, highly variable LC-MS data sets. Regular DTW or PTW use one-dimensional profiles such as the Total Ion Chromatogram (TIC) or Base Peak Chromatogram (BPC) resulting in correct alignment if the signals have a relatively simple structure. However, when aligning the TICs of chromatograms from complex mixtures with large concentration variability such as serum or urine, both algorithms often lead to misalignment of peaks and thus incorrect comparisons in the subsequent statistical analysis. This is mainly due to the fact that compounds with different m/z values but similar retention times are not considered separately but confounded in the benefit function of the algorithms using only one-dimensional information. Thus, it is necessary to treat the infor...
53 citations
TL;DR: A full-body layered deformable model (LDM) inspired by manually labeled silhouettes for automatic model-based gait recognition from part-level gait dynamics in monocular video sequences and can serve as an analysis tool for studying factors affecting the gait under various conditions.
Abstract: This paper proposes a full-body layered deformable model (LDM) inspired by manually labeled silhouettes for automatic model-based gait recognition from part-level gait dynamics in monocular video sequences. The LDM is defined for the fronto-parallel gait with 22 parameters describing the human body part shapes (widths and lengths) and dynamics (positions and orientations). There are four layers in the LDM and the limbs are deformable. Algorithms for LDM-based human body pose recovery are then developed to estimate the LDM parameters from both manually labeled and automatically extracted silhouettes, where the automatic silhouette extraction is through a coarse-to-fine localization and extraction procedure. The estimated LDM parameters are used for model-based gait recognition by employing the dynamic time warping for matching and adopting the combination scheme in AdaBoost.M2. While the existing model-based gait recognition approaches focus primarily on the lower limbs, the estimated LDM parameters enable us to study full-body model-based gait recognition by utilizing the dynamics of the upper limbs, the shoulders and the head as well. In the experiments, the LDM-based gait recognition is tested on gait sequences with differences in shoe-type, surface, carrying condition and time. The results demonstrate that the recognition performance benefits from not only the lower limb dynamics, but also the dynamics of the upper limbs, the shoulders and the head. In addition, the LDM can serve as an analysis tool for studying factors affecting the gait under various conditions.
53 citations
25 Mar 2012
TL;DR: A fast unsupervised spoken term detection system based on lower-bound Dynamic Time Warping (DTW) search on Graphical Processing Units (GPUs) and the K nearest neighbor DTW search are presented.
Abstract: In this paper we present a fast unsupervised spoken term detection system based on lower-bound Dynamic Time Warping (DTW) search on Graphical Processing Units (GPUs). The lower-bound estimate and the K nearest neighbor DTW search are carefully designed to fit the GPU parallel computing architecture. In a spoken term detection task on the TIMIT corpus, a 55x speed-up is achieved compared to our previous implementation on a CPU without affecting detection performance. On large, artificially created corpora, measurements show that the total computation time of the entire spoken term detection system grows linearly with corpus size. On average, searching a keyword on a single desktop computer with modern GPUs requires 2.4 seconds/corpus hour.
53 citations
TL;DR: A method for the automatic handwritten signature verification (AHSV) that relies on global features that summarize different aspects of signature shape and dynamics of signature production and shows that the correctness of the algorithm detecting the signature is more acceptable.
53 citations
TL;DR: This paper proposes the use of a set of features derived from a Gaussian mixture model (GMM) for the alignment of the signatures using DTW, the first of its kind that uses the features of the GMM, a model-based classifier into the framework of the DTW technique for online signature verification.
Abstract: This paper presents a novel online signature verification system based on the extension of the traditional dynamic time warping (DTW) matching scheme. We propose the use of a set of features derived from a Gaussian mixture model (GMM) for the alignment of the signatures using DTW. These features aid in capturing signature-dependent characteristics of a user in the feature space with a probabilistic framework. In addition, we explore the characteristics of the warping path of DTW, by employing the proposed GMM features. We derive a score for the warping path, and fuse it to that of the DTW score for verifying the authenticity of a test signature. To the best of our knowledge, this paper is the first of its kind that uses the features of the GMM, a model-based classifier into the framework of the DTW technique for online signature verification. The experiments are conducted on the publicly available MCYT database for both common and user thresholds. The results obtained are promising over prior works for this database.
53 citations