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Large vocabulary gesture recognition using a training set of limited size is a challenging problem in computer vision. With few examples per gesture class, researchers often employ exemplar-based methods such as Dynamic Time Warping (DTW). This paper makes two contributions in the area of exemplar-based gesture recognition: 1) it introduces Multiple-Pass DTW (MP-DTW), a method in which scores from...
Sign Language Recognition (SLR) aims at translating the Sign Language (SL) into speech or text, so as to facilitate the communication between hearing-impaired people and the normal people. This problem has broad social impact, however it is challenging due to the variation for different people and the complexity in sign words. Traditional methods for SLR generally use handcrafted feature and Hidden...
Sign Language Recognition (SLR) aims at translating the sign language into text or speech, so as to realize the communication between deaf-mute people and ordinary people. This paper proposes a framework based on the Hidden Markov Models (HMMs) benefited from the utilization of the trajectories and hand-shape features of the original sign videos, respectively. First, we propose a new trajectory feature...
This study proposes a vision based Persian Sign Language (PSL) recognition system. Continuous Hidden Markov Model (HMM) with Gaussian mixture state observation densities is used to classify 15 dynamic signs. The proposed feature extraction approach is based on the spline interpolation of the sign trajectories. The efficiency of the system was assessed with a large set of videos collected by the authors...
Sign language recognition has been the focus of research in recent years because it has enabled the use of sign languages, which are the main medium of communication for the hearing impaired, for human-computer interaction. In this work, we propose a method to recognize signs using Improved Dense Trajectory (IDT) features which were previously used in large-scale action recognition. Fisher Vectors...
In this paper, we propose a new system for isolated sign language recognition (SLR) and continuous SLR. In isolated SLR, Histogram of Oriented Displacement is used to describe the trajectories, and multi-SVM is adopted for classification. In continuous SLR, we propose a Dynamic Programming method with warping templates obtained by Dynamic Time Warping (DTW) algorithm. We evaluate our approach with...
Sign Language Recognition (SLR) targets on interpreting the sign language into text or speech, so as to facilitate the communication between deaf-mute people and ordinary people. This task has broad social impact, but is still very challenging due to the complexity and large variations in hand actions. Existing methods for SLR use hand-crafted features to describe sign language motion and build classification...
In this paper, we present a Sign Language Tutoring Demonstrator, which is capable of teaching the basics of the sign language interactively. Instead of a passive learner, by incorporating a simple sign language recognizer to the system, the learner would be able to practice the signs and have feedbacks according to the similarity of the performed gesture to the actual gesture model.
This paper proposes an automated sign language to speech interpreter that begins by capturing the 3D video stream through Kinect and the joints of interest in the human skeleton are then worked upon. The proposed system deals with the problems faced by mute people in conveying their message through Pakistani sign language. This research makes use of the 3D trajectory algorithm for processing the normalized...
Sign languages (SL) are the most accomplished forms of gestural communication. Therefore, their automatic analysis is a real challenge which is interestingly implied to their lexical and syntactic organization levels. Statements dealing with sign language occupy a significant interest in the Automatic Natural Language Processing (ANLP) domain. In this work, we are dealing with sign language recognition,...
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