Abstract
Gesture image segmentation is a challenge task due to the high degree of freedom of human gestures, large differences in shape and high flexibility, traditional pattern recognition and image processing methods are not effective in gesture detection. The traditional image segmentation based on the detection of skin color and the image of the depth image are limited by the effects of ambient light, skin color difference and image depth variation, resulting in unsatisfactory results. Therefore, we propose a hand gesture depth image segmentation method based on random forest. The method learns the gesture image feature representation of the depth image by supervising learning. Experiments show that the proposed method segments the gesture s’ pixels from the backgrounds area of the depth image. The proposed method potential has widely usages in gesture tracking, gesture recognition and human computer interaction.
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Acknowledgments
This research work is supported by the grant of Guangxi science and technology development project (No: AC16380124), the grant of Guangxi Science Foundation (No: 2017GXNSFAA198226), the grant of Guangxi Key Laboratory of Trusted Software of Guilin University of Electronic Technology (No: KX201513), the grant of Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics of Guilin University of Electronic Technology (No: GIIP201602), and the grant of Innovation Project of GUET Graduate Education (2018YJCX43).
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Tang, R., Pan, H., Chen, X., Chen, J. (2018). Random Forest Based Gesture Segmentation from Depth Image. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10942. Springer, Cham. https://doi.org/10.1007/978-3-319-93818-9_48
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DOI: https://doi.org/10.1007/978-3-319-93818-9_48
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