Improving arm segmentation in sign language recognition systems using image processing
| Autor: | Qiaoli Zhuang, Chen Yingrou, Bao Jiaxin, Qiuhong Tian, Yang Huimin |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Support Vector Machine Computer science Biomedical Engineering Biophysics Health Informatics Bioengineering Image processing 02 engineering and technology Sign language Image (mathematics) Biomaterials Sign Language 020901 industrial engineering & automation Image Processing Computer-Assisted 0202 electrical engineering electronic engineering information engineering Humans Segmentation Artificial neural network business.industry Pattern recognition Image segmentation Hand Support vector machine Euclidean distance 020201 artificial intelligence & image processing Neural Networks Computer Artificial intelligence business Algorithms Information Systems |
| Zdroj: | Technology and Health Care. 29:527-540 |
| ISSN: | 1878-7401 0928-7329 |
| DOI: | 10.3233/thc-192000 |
| Popis: | BACKGROUND: For a traditional vision-based static sign language recognition (SLR) system, arm segmentation is a major factor restricting the accuracy of SLR. OBJECTIVE: To achieve accurate arm segmentation for different bent arm shapes, we designed a segmentation method for a static SLR system based on image processing and combined it with morphological reconstruction. METHODS: First, skin segmentation was performed using YCbCr color space to extract the skin-like region from a complex background. Then, the area operator and the location of the mass center were used to remove skin-like regions and obtain the valid hand-arm region. Subsequently, the transverse distance was calculated to distinguish different bent arm shapes. The proposed segmentation method then extracted the hand region from different types of hand-arm images. Finally, the geometric features of the spatial domain were extracted and the sign language image was identified using a support vector machine (SVM) model. Experiments were conducted to determine the feasibility of the method and compare its performance with that of neural network and Euclidean distance matching methods. RESULTS: The results demonstrate that the proposed method can effectively segment skin-like regions from complex backgrounds as well as different bent arm shapes, thereby improving the recognition rate of the SLR system. |
| Databáze: | OpenAIRE |
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