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Improving arm segmentation in sign language recognition systems using image processing.

Qiuhong Tian, Jiaxin Bao, Huimin Yang

    Technology and Health Care : Official Journal of the European Society for Engineering and Medicine
    |October 19, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an improved method for arm segmentation in static sign language recognition (SLR), enhancing accuracy by effectively processing complex backgrounds and various arm shapes. The new approach boosts the overall performance of SLR systems.

    Keywords:
    Static sign language recognitionbent arm shapegeometric featureimage segmentationtransverse distance

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    Area of Science:

    • Computer Vision
    • Human-Computer Interaction
    • Biomedical Engineering

    Background:

    • Accurate arm segmentation is crucial for improving static sign language recognition (SLR) systems.
    • Traditional vision-based SLR methods face challenges with arm segmentation accuracy, especially with varying arm poses.

    Purpose of the Study:

    • To develop an accurate arm segmentation method for static SLR systems.
    • To address the challenge of segmenting different bent arm shapes effectively.

    Main Methods:

    • Utilized YCbCr color space for skin segmentation to isolate skin regions from complex backgrounds.
    • Employed area operators and mass center location to refine the hand-arm region, removing extraneous skin-like areas.
    • Calculated transverse distance to differentiate various bent arm shapes and extracted geometric features for recognition using a Support Vector Machine (SVM) model.

    Main Results:

    • The proposed method successfully segmented skin regions from complex backgrounds and accurately identified different bent arm shapes.
    • Demonstrated improved recognition rates for the static sign language recognition system.

    Conclusions:

    • The developed image processing and morphological reconstruction-based method enhances arm segmentation accuracy in static SLR.
    • This approach offers a viable solution for improving the performance of vision-based sign language recognition systems.