IoT-Based Image Recognition System for Smart Home-Delivered Meal Services

被引:5
作者
Tseng, Hsiao-Ting [1 ]
Hwang, Hsin-Ginn [1 ]
Hsu, Wei-Yen [2 ]
Chou, Pei-Chin [2 ]
Chang, I-Chiu [2 ]
机构
[1] Natl Chiao Tung Univ, Inst Informat Management, Hsinchu 300, Taiwan
[2] Natl Chung Cheng Univ, Inst Informat Management, Chiayi 621, Taiwan
来源
SYMMETRY-BASEL | 2017年 / 9卷 / 07期
关键词
Internet of Things; long-term care 2.0; image segmentation; k-means clustering; histogram; LONG-TERM-CARE; SEGMENTATION; HISTOGRAM; TAIWAN; DISABILITIES; ALGORITHM; PEOPLE;
D O I
10.3390/sym9070125
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
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页数:12
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