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Detail
Journal Article
Real-Time Hair Segmentation Using Mobile-Unet
- Authors
- Ho-Sub Yoon, Seong-Woo Park, Jang-Hee Yoo
- Issue Date
- 2021-01
- Citation
- Electronics, v.10, no.2, pp.1-12
- ISSN
- 2079-9292
- Publisher
- MDPI
- Language
- English
- Type
- Journal Article
- Abstract
- We described a real-time hair segmentation method based on a fully convolutional network with the basic structure of an encoder?밺ecoder. In one of the traditional computer vision techniques for hair segmentation, the mean shift and watershed methodologies suffer from inaccuracy and slow execution due to multi-step, complex image processing. It is also difficult to execute the process in real-time unless an optimization technique is applied to the partition. To solve this problem, we exploited Mobile-Unet using the U-Net segmentation model, which incorporates the optimization techniques of MobileNetV2. In experiments, hair segmentation accuracy was evaluated by different genders and races, and the average accuracy was 89.9%. By comparing the accuracy and execution speed of our model with those of other models in related studies, we confirmed that the proposed model achieved the same or better performance. As such, the results of hair segmentation can obtain hair information (style, color, length), which has a significant impact on human-robot interaction with people.
- KSP Keywords
- Basic structure, Complex image, Computer Vision(CV), Execution speed, Fully Convolutional Networks(FCN), Hair segmentation, Human robot interaction(HRI), Image processing(IP), Mean-shift(MS), Multi-step, Optimization techniques
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
