Local Region-Based Hand Segmentation Using Deep Learning 


Vol. 48,  No. 9, pp. 1135-1143, Sep.  2023
10.7840/kics.2023.48.9.1135


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  Abstract

Segmenting objects through the Digital Image Processing makes it difficult to draw high accuracy in a complex background. In this paper, we propose a method of using deep-learning segmentation to segment objects in complex environments, providing ROI(Region of Interest) of images as a pre-processing, and Thresholding in the post-processing. The proposed method is to detect the position of the hand in a complex image using a deep learning-based object recognition algorithm employing the YOLOv4 model; to expand the ROI so that the deep learning-based segmentation techniques using the U-Net model can be applied locally, not on the entire image; and to process by Thresholding through Otsu's Binarization method. We applied the proposed algorithm to hand images with complex backgrounds and verified the effectiveness of the algorithm by measuring the IoU values of the masks of correct answers and results.

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[IEEE Style]

J. Jeon, T. Kim, Y. Jeong, K. Park, "Local Region-Based Hand Segmentation Using Deep Learning," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 9, pp. 1135-1143, 2023. DOI: 10.7840/kics.2023.48.9.1135.

[ACM Style]

Junhyun Jeon, Tae-Hun Kim, Yoosoo Jeong, and Kil-Houm Park. 2023. Local Region-Based Hand Segmentation Using Deep Learning. The Journal of Korean Institute of Communications and Information Sciences, 48, 9, (2023), 1135-1143. DOI: 10.7840/kics.2023.48.9.1135.

[KICS Style]

Junhyun Jeon, Tae-Hun Kim, Yoosoo Jeong, Kil-Houm Park, "Local Region-Based Hand Segmentation Using Deep Learning," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 9, pp. 1135-1143, 9. 2023. (https://doi.org/10.7840/kics.2023.48.9.1135)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880