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Abstract
In this paper we have investigated the capabilities of a quadrant photodiode based gesture sensor in the recognition of digits drawn in the air. The sensor consisting of 4 active elements, 4 LEDs and a pinhole was considered as input interface for both discrete and continuous gestures. Index finger and a round pointer were used as navigating mediums for the sensor. Experiments performed with 5 volunteers allowed to record 300 examples of each digit from 0 to 9, which were drawn in the air. Digits were converted from a list of recorded coordinates into images processed as in the MNIST database. Three approaches for recognition of digits recorded by quadrant photodiode were considered: convolutional neural network trained only on examples from the MNIST database, network trained on mixed data of MNIST with examples recorded using quadrant photodiode (4/1 proportions) and trained on the MNIST with examples recorded using the elaborated sensor but after the arbitral rejection of 20% of worst quality data (4/1 proportions preserved). The application of the third approach in comparison to the first one allowed to increase the overall accuracy of digits classification from 34.4% to 86% for testing data recorded with the use of the pointer and from 32% to 81.2% for data recorded with the use of a finger (for 50Hz sampling frequency).
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Authors (3)
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Janczyk Kamil
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Details
- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Title of issue:
- 2018 11th International Conference on Human System Interaction (HSI) strony 111 - 117
- Language:
- English
- Publication year:
- 2018
- Bibliographic description:
- Kamil J., Czuszyński K., Rumiński J.: Digits Recognition with Quadrant Photodiode and Convolutional Neural Network// 2018 11th International Conference on Human System Interaction (HSI)/ : , 2018, s.111-117
- DOI:
- Digital Object Identifier (open in new tab) 10.1109/hsi.2018.8431246
- Verified by:
- Gdańsk University of Technology
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