A Novel String Grammar Unsupervised Possibilistic C-Medians Algorithm for Sign Language Translation Systems

Abstract

Abstract:Sign language is a basic method for solving communication problems between deaf and hearing people. In order to communicate, deaf and hearing people normally use hand gestures, which include a combination of hand positioning, hand shapes, and hand movements. Thai Sign Language is the communication method for Thai hearing-impaired people. Our objective is to improve the dynamic Thai Sign Language translation method with a video captioning technique that does not require prior hand region detection and segmentation through using the Scale Invariant Feature Transform (SIFT) method and the String Grammar Unsupervised Possibilistic C-Medians (sgUPCMed) algorithm. This work is the first to propose the sgUPCMed algorithm to cope with the unsupervised generation of multiple prototypes in the possibilistic sense for string data. In our experiments, the Thai Sign Language data set (10 isolated sign language words) was collected from 25 subjects. The best average result within the constrained environment of the blind test data sets of signer-dependent cases was 89-91%, and the successful rate of signer semi-independent cases was 81-85%, on average. For the blind test data sets of signer-independent cases, the best average classification rate was 77-80%. The average result of the system without a constrained environment was around 62-80% for the signer-independent experiments. To show that the proposed algorithm can be implemented in other sign languages, the American sign language (RWTH-BOSTON-50) data set, which consists of 31 isolated American Sign Language words, is also used in the experiment. The system provides 88.56% and 91.35% results on the validation set alone, and for both the training and validation sets, respectively.

Real-Time Musical Conducting Gesture Recognition Based on a Dynamic Time Warping Classifier Using a Single-Depth Camera

“…We compared our method with the methods reported by three other studies that are closely related to ours. The first one is by H. Je, J. Kim, and D. Kim, who used a depth camera to recognize some musical gestures through feature points [5]. The second one is by N. Kawarazaki, Y. Kaneishi, N. Saito, and T. Asakawa [35], who used a depth camera and an inertia sensor to detect the conductor's movements and where the conductor was facing.…”

“…Humans often express their actions, intents, and emotions through hand gestures, and gesture recognition is useful for processing some tasks automatically. For example, American Sign Language helps those with speaking or hearing disabilities to communicate through just hand gestures [3][4][5][6]. Another example is traffic police officers who direct traffic during a traffic jam or car accident.…”

String Grammar Unsupervised Possibilistic Fuzzy C-Medians for Gait Pattern Classification in Patients with Neurodegenerative Diseases

“…U is a membership matrix [ u ik ] C × N and T is a possibilistic matrix [ t ik ] C × N . The objective function of the sgUPFCMed is where u ik is the membership value of string s k in the cluster i , t ik is the possibilistic value of string s k in the cluster i , m is the fuzzifier (normally m > 1), η > 1, β > 0, a > 0, b > 0, ∑ c =1 C u ik = 1 for k = 1,…, N , and 0 ≤ u ik , t ik ≤ 1. β is defined as the sample covariance [ 23 ] based on the Euclidean distance. Since our data set is a string data set, the calculation of β will be where Med is the median string of the data set; i.e., The theorem for the sgUPFCMed and its corresponding proof are shown in Theorem 1 .…”

“…The sgUPFCMed is utilized to find a string prototype(s) for each disease. Then the fuzzy k-nearest neighbor [ 23 ] is utilized to find the best match for a test data sample. The paper is structured as follows.…”

Emerging Approaches and Advances in Big Data

“…This special issue of Symmetry entitled "Emerging Approaches and Advances in Big Data" consists of 17 papers [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] that all present research in the emerging area of Big Data.…”