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- W4311591729 abstract "Skin detection is the process of identifying skin pixels in a 2-D image or video. It is a vital part of today’s Computer Vision technology and widely spread among applications including medical diagnosis, cryptographic protection, localization tasks and human-machine interaction. To this end, this work proposes a novel, low power (31–35nW), low voltage (0.6V), area-efficient (2098µm <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> ), GMM-Based analog classifier for skin detection. The architecture consists of bump circuits and a Lazzaro Winner-Take-All circuit. The presented classifier was designed and simulated in a TSMC 90nm CMOS process, using the Cadence IC Suite. To verify the proper operation of the proposed design, a real-world skin segmentation dataset is used. To confirm the circuit’s accuracy, post-layout simulation results were compared to a software-based implementation." @default.
- W4311591729 created "2022-12-27" @default.
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- W4311591729 date "2022-12-02" @default.
- W4311591729 modified "2023-09-24" @default.
- W4311591729 title "A Hardware-Friendly Low-Power Area-Efficient GMM-Based Analog Classifier For Skin Detection" @default.
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- W4311591729 doi "https://doi.org/10.1109/pacet56979.2022.9976323" @default.
- W4311591729 hasPublicationYear "2022" @default.
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