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: A method for isolated handwritten or hand-printed character recognition using dynamic programming for matching the non-linear multi-projection profiles that are produced from the Radon transform using dynamic time warping (DTW).
Abstract: In this paper, we study a method for isolated handwritten or hand-printed character recognition using dynamic programming for matching the non-linear multi-projection profiles that are produced from the Radon transform. The idea is to use dynamic time warping (DTW) algorithm to match corresponding pairs of the Radon features for all possible projections. By using DTW, we can avoid compressing feature matrix into a single vector which may miss information. It can handle character images in different shapes and sizes that are usually happened in natural handwriting in addition to difficulties such as multi-class similarities, deformations and possible defects. Besides, a comprehensive study is made by taking a major set of state-of-the-art shape descriptors over several character and numeral datasets from different scripts such as Roman, Devanagari, Oriya, Bangla and Japanese-Katakana including symbol. For all scripts, the method shows a generic behaviour by providing optimal recognition rates but, with high computational cost.
38 citations
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TL;DR: Five lower limbs’ motions are classified and recognized by using Gaussian kernel-based linear discriminant analysis (LDA) and support vector machine (SVM) respectively and the results prove that the fused feature-based classification outperforms the classification with only sEMG signals or accelerometer signals.
Abstract: Since surface electromyograghic (sEMG) signals are non-invasive and capable of reflecting humans’ motion intention, they have been widely used for the motion recognition of upper limbs. However, limited research has been conducted for lower limbs, because the sEMGs of lower limbs are easily affected by body gravity and muscle jitter. In this paper, sEMG signals and accelerometer signals are acquired and fused to recognize the motion patterns of lower limbs. A curve fitting method based on median filtering is proposed to remove accelerometer noise. As for movement onset detection, an sEMG power spectral correlation coefficient method is used to detect the start and end points of active signals. Then, the time-domain features and wavelet coefficients of sEMG signals are extracted, and a dynamic time warping (DTW) distance is used for feature extraction of acceleration signals. At last, five lower limbs’ motions are classified and recognized by using Gaussian kernel-based linear discriminant analysis (LDA) and support vector machine (SVM) respectively. The results prove that the fused feature-based classification outperforms the classification with only sEMG signals or accelerometer signals, and the fused feature can achieve 95% or higher recognition accuracy, demonstrating the validity of the proposed method.
38 citations
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TL;DR: A new family of 3D skeletal representations for human action recognition, referred to as R3DG features, that explicitly model the 3D geometric relationships between various body parts using rigid body transformations in 3D space and outperforms various state-of-the-art skeleton-based humanaction recognition approaches.
38 citations
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TL;DR: This work proposes a novel variant of Dynamic Time Warping named SC-DTW, which can generate a more feature-to-feature alignment between time series and thus serves as a robust similarity measure.
38 citations
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18 Sep 2001TL;DR: The task of tagging motion data is equal to the task of expressing motion by using motion association rules, which consist of symbols, which uniquely represent basic patterns in motion data.
Abstract: In the past several years, human motion data has been used in some domains such as SFX movies, CGs and so on. Motion data is captured by a motion capture system which captures the position of sensors on body joints, so we can get motion data as 3-D time series data. Since, motion data is multi-stream data of time series for 17 body parts, the amount of data is huge. Furthermore, motion data is expensive. A motion database can help those creators to produce motion data with less cost. The database, however, requires a content-based retrieval method because it is difficult to identify the motion by using a keyword approach. Consequently we introduce association rules which represent dependency between body parts. Association rules represent the motion of body parts and can be used as visual tags. We introduce a method to discover dependency between body parts as association rules. Association rules consist of symbols uniquely representing basic patterns. We call basic patterns primitive motions. Primitive motions are extracted from motion data by using segmentation and clustering processes. Finally, we discuss some experiments to discover association rules from multi-streams of motion data.
38 citations