Title: IoT-Based Image Recognition System for Smart Home-Delivered Meal Services
Authors: Tseng, Hsiao-Ting
Hwang, Hsin-Ginn
Hsu, Wei-Yen
Chou, Pei-Chin
Chang, I-Chiu
資訊管理與財務金融系 註:原資管所+財金所
Department of Information Management and Finance
Keywords: Internet of Things;long-term care 2.0;image segmentation;k-means clustering;histogram
Issue Date: 1-Jul-2017
Abstract: Population ageing is an important global issue. The Taiwanese government has used various Internet of Things (IoT) applications in the 10-year long-term care program 2.0. It is expected that the efficiency and effectiveness of long-term care services will be improved through IoT support. Home-delivered meal services for the elderly are important for home-based long-term care services. To ensure that the right meals are delivered to the right recipient at the right time, the runners need to take a picture of the meal recipient when the meal is delivered. This study uses the IoT-based image recognition system to design an integrated service to improve the management of image recognition. The core technology of this IoT-based image recognition system is statistical histogram-based k-means clustering for image segmentation. However, this method is time-consuming. Therefore, we proposed using the statistical histogram to obtain a probability density function of pixels of a figure and segmenting these with weighting for the same intensity. This aims to increase the computational performance and achieve the same results as k-means clustering. We combined histogram and k-means clustering in order to overcome the high computational cost for k-means clustering. The results indicate that the proposed method is significantly faster than k-means clustering by more than 10 times.
URI: http://dx.doi.org/10.3390/sym9070125
http://hdl.handle.net/11536/145909
ISSN: 2073-8994
DOI: 10.3390/sym9070125
Journal: SYMMETRY-BASEL
Volume: 9
Issue: 7
Begin Page: 0
End Page: 0
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