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Enhanced dynamic programming approach for subunit modelling to handle segmentation and recognition ambiguities in sign language

机译:用于子单元建模的增强型动态编程方法,可处理手语中的分段和识别歧义

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Sign language serves as a primary means of communication among the deaf impaired community. The major challenges faced by the Sign Language Recognition (SLR) system are recognizing signs from large vocabularies in continuous video sequences. In this research paper, a novel subunit sign modelling framework is proposed for vision-based SLR which aims in solving the major issues in SLR systems. The problem of hand segmentation ambiguities and segregating epentheses movements between two adjacent signs in continuous video sequences are addressed. A novel subunit sign modelling framework is presented and illustrated to embark upon these problems while considering large-vocabularies. This framework is developed using a novel methodology of Enhanced Dynamic Programming (EDP) approach in subunit sign modelling. This EDP framework works with a combination of dynamic time warping and spatiotemporal clustering techniques. Since, sign language consists of both spatial and temporal feature vectors, dynamic time warping is used as a distance measure to compute the distance between two adjacent signs in sign trajectories. This distance is used as a temporal feature vector during the clustering of spatial feature vectors using Minimum Entropy Clustering (MEC). This process is done recursively to cluster all the epentheses movements dynamically without using any explicit or implicit modelling. Experimental results have confirmed that the computation cost of the proposed system is less because the epenthesis movements are eliminated before classification and the gesture base space utilized by the sign gestures is very low because the proposed system does not require any modelling to handle epenthesis movements. The results obtained from the proposed subunit sign modelling framework is compared with other existing models in order to prove that the proposed system is best among the existing systems.
机译:手语是聋哑社区之间进行交流的主要手段。手语识别(SLR)系统面临的主要挑战是从连续视频序列中的大量词汇中识别符号。在这篇研究论文中,针对基于视觉的SLR提出了一个新颖的亚单元标志建模框架,该框架旨在解决SLR系统中的主要问题。解决了连续视频序列中两个相邻符号之间的手段歧义模糊和隔离遮蔽移动的问题。提出并说明了一个新颖的亚单元标志建模框架,以在考虑大型词汇的同时着手解决这些问题。该框架是在子单元符号建模中使用增强型动态编程(EDP)方法的新颖方法开发的。此EDP框架结合了动态时间规整和时空聚类技术。由于手语由空间和时间特征向量组成,因此动态时间扭曲被用作距离度量,以计算手语轨迹中两个相邻手语之间的距离。在使用最小熵聚类(MEC)对空间特征向量进行聚类期间,此距离用作时间特征向量。该过程以递归方式完成,以动态聚类所有猪笼草运动,而无需使用任何显式或隐式建模。实验结果证实,所提出的系统的计算成本较低,因为在分类之前消除了上肢运动,并且由于所提议的系统不需要任何模型来处理上肢运动,因此手势所使用的手势基础空间非常低。从拟议的亚单位标志建模框架获得的结果与其他现有模型进行了比较,以证明拟议的系统在现有系统中是最好的。

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