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- W2892871840 abstract "Although many studies suggest high performance hand detection methods, those methods are likely to be overfitting. Fortunately, the Convolution Neural Network (CNN) based approach provides a better way that is less sensitive to translation and hand poses. However the CNN approach is complex and can increase computational time, which at the end reduce its effectiveness on a system where the speed is essential.In this study we propose a shallow CNN network which is fast, and insensitive to translation and hand poses. It is tested on two different domains of hand datasets, and performs in relatively comparable performance and faster than the other state-of-theart hand CNN-based hand detection method. Our evaluation shows that the proposed shallow CNN network performs at 93.9% accuracy and reaches much faster speed than its competitors." @default.
- W2892871840 created "2018-10-05" @default.
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- W2892871840 date "2018-07-01" @default.
- W2892871840 modified "2023-09-26" @default.
- W2892871840 title "CNN Based Posture-Free Hand Detection" @default.
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- W2892871840 doi "https://doi.org/10.1109/iciteed.2018.8534743" @default.
- W2892871840 hasPublicationYear "2018" @default.
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