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Open AccessArticle

Graph Cut-Based Human Body Segmentation in Color Images Using Skeleton Information from the Depth Sensor

1
Department of Multimedia Engineering, Dongguk University, Pildong-ro 1gil 30, Jung-gu, Seoul 100-715, Korea
2
Department of Electronics and Electrical Engineering, Dongguk University, Pildong-ro 1gil 30, Jung-gu, Seoul 100-715, Korea
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(2), 393; https://doi.org/10.3390/s19020393
Received: 30 November 2018 / Revised: 13 January 2019 / Accepted: 17 January 2019 / Published: 18 January 2019
(This article belongs to the Special Issue Depth Sensors and 3D Vision)
Segmentation of human bodies in images is useful for a variety of applications, including background substitution, human activity recognition, security, and video surveillance applications. However, human body segmentation has been a challenging problem, due to the complicated shape and motion of a non-rigid human body. Meanwhile, depth sensors with advanced pattern recognition algorithms provide human body skeletons in real time with reasonable accuracy. In this study, we propose an algorithm that projects the human body skeleton from a depth image to a color image, where the human body region is segmented in the color image by using the projected skeleton as a segmentation cue. Experimental results using the Kinect sensor demonstrate that the proposed method provides high quality segmentation results and outperforms the conventional methods. View Full-Text
Keywords: depth image; graph cut; human body segmentation; image segmentation; Kinect sensor; skeleton depth image; graph cut; human body segmentation; image segmentation; Kinect sensor; skeleton
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Lee, J.; Kim, D.-W.; Won, C.S.; Jung, S.-W. Graph Cut-Based Human Body Segmentation in Color Images Using Skeleton Information from the Depth Sensor. Sensors 2019, 19, 393.

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