A system for sign language recognition using fuzzy object similarity tracking

As a part of natural language understanding, sign language recognition is considered an important area of research. The applications of such a system range from human-computer interaction in virtual reality systems to auxiliary tools for deaf-mute to communicate with ordinary people through computer. A great deal of research is done so far but fewer researchers have extended it to Arabic sign language recognition. In this paper, we have presented a system that performs vision based isolated Arabic sign language recognition using hidden Markov models together with EM algorithm for parameters estimation. An approach to track hands in subsequent frames is proposed using a fuzzy object similarity measure based on a number of geometrical features of hands. Moreover, we have used the centroid of the signer's face to centralize the body coordinates instead of fixing the signer's position or using position tracker device. The overall accuracy of the recognition task is 98% over a dataset of 50 signs including single hand and two-handed signs

A system for sign language recognition using fuzzy object similarity tracking

As a part of natural language understanding, sign language recognition is considered an important area of research. The applications of such a system range from human-computer interaction in virtual reality systems to auxiliary tools for deaf-mute to communicate with ordinary people through computer. A great deal of research is done so far but fewer researchers have extended it to Arabic sign language recognition. In this paper, we have presented a system that performs vision based isolated Arabic sign language recognition using hidden Markov models together with EM algorithm for parameters estimation. An approach to track hands in subsequent frames is proposed using a fuzzy object similarity measure based on a number of geometrical features of hands. Moreover, we have used the centroid of the signer's face to centralize the body coordinates instead of fixing the signer's position or using position tracker device. The overall accuracy of the recognition task is 98% over a dataset of 50 signs including single hand and two-handed signs

An Appearance-Based Framework for 3D Hand Shape Classification and Camera Viewpoint Estimation

An appearance-based framework for 3D hand shape classification and simultaneous camera viewpoint estimation is presented. Given an input image of a segmented hand, the most similar matches from a large database of synthetic hand images are retrieved. The ground truth labels of those matches, containing hand shape and camera viewpoint information, are returned by the system as estimates for the input image. Database retrieval is done hierarchically, by first quickly rejecting the vast majority of all database views, and then ranking the remaining candidates in order of similarity to the input. Four different similarity measures are employed, based on edge location, edge orientation, finger location and geometric moments.National Science Foundation (IIS-9912573, EIA-9809340

A Review of Codebook Models in Patch-Based Visual Object Recognition

The codebook model-based approach, while ignoring any structural aspect in vision, nonetheless provides state-of-the-art performances on current datasets. The key role of a visual codebook is to provide a way to map the low-level features into a fixed-length vector in histogram space to which standard classifiers can be directly applied. The discriminative power of such a visual codebook determines the quality of the codebook model, whereas the size of the codebook controls the complexity of the model. Thus, the construction of a codebook is an important step which is usually done by cluster analysis. However, clustering is a process that retains regions of high density in a distribution and it follows that the resulting codebook need not have discriminant properties. This is also recognised as a computational bottleneck of such systems. In our recent work, we proposed a resource-allocating codebook, to constructing a discriminant codebook in a one-pass design procedure that slightly outperforms more traditional approaches at drastically reduced computing times. In this review we survey several approaches that have been proposed over the last decade with their use of feature detectors, descriptors, codebook construction schemes, choice of classifiers in recognising objects, and datasets that were used in evaluating the proposed methods