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22 December 2022 An automated method of creating lunchbox image datasets using a novel clustering algorithm
Kohki Hayakawa, Takuya Matsumoto, Shenglin Mu, Seiji Nishifuji, Shota Nakashima
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Proceedings Volume 12508, International Symposium on Artificial Intelligence and Robotics 2022; 125080P (2022) https://doi.org/10.1117/12.2662189
Event: Seventh International Symposium on Artificial Intelligence and Robotics 2022, 2022, Shanghai, China
Abstract
Meal assistance robots have been developed because people with upper limb disabilities have difficulty in eating by themselves. We develop a robot to automatically select and assist food by machine learning to operate more easily. This machine learning requires the creation of high-quality datasets for each type of food. In this paper, we propose the automatic improved method by using Density-Based Spatial Clustering of Applications with Noise repetitively to remove noisy images in the dataset. Experimental results show that the percentage of noise images in the dataset was reduced by 20%. In this way, we hope that the accuracy of automatic selection implemented in meal assistance robot is improved.
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Kohki Hayakawa, Takuya Matsumoto, Shenglin Mu, Seiji Nishifuji, and Shota Nakashima "An automated method of creating lunchbox image datasets using a novel clustering algorithm", Proc. SPIE 12508, International Symposium on Artificial Intelligence and Robotics 2022, 125080P (22 December 2022); https://doi.org/10.1117/12.2662189
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KEYWORDS
Image segmentation
Robots
Machine learning
Image processing
Data modeling
Image classification
Target recognition
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