• Journal of the Korean Institute of Intelligent Systems
• /
• v.23 no.1
• /
• pp.57-64
• /
• 2013

In this paper, we propose the classification method of medicine bottle images using the features with color and size information. It is difficult to classify with size feature only, because there are many similar sizes of bottles. Therefore, we suggest a classification method based on color information, which robust to illumination variations. First, we extract MBR(Minimum Boundary Rectangle) of medicine bottle area using Binary Threshold of Red, Green, and Blue in image and classify images with size. Then, hue information and RGB color average rate are used to classify image, which features are robust to lighting variations. Finally, using SURF(Speed Up Robust Features) algorithm, corresponding image can be found from candidates with previous extracted features. The proposed method makes to reduce execution time and minimize the error rate and is confirmed to be reliable and efficient from experiment.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.23 no.3
• /
• pp.226-231
• /
• 2013

In The matching algorithm for automatic packaging of drugs is essential to determine whether the canister can exactly refill the suitable medicine. In this paper, we propose a hierarchical neural network with the upper and lower layers which can perform real-time processing and classification of many types of medicine bottles to prevent accidental medicine disaster. A few number of low-dimensional feature vector are extracted from the label images presenting medicine-bottle information. By using the extracted feature vectors, the lower layer of MLP(Multi-layer Perceptron) neural networks is learned. Then, the output of the learned middle layer of the MLP is used as the input to the upper layer of the MLP learning. The proposed hierarchical neural network shows good classification performance and real- time operation in the test of up to 30 degrees rotated to the left and right images of 100 different medicine bottles.

• Journal of Korea Society of Industrial Information Systems
• /
• v.19 no.1
• /
• pp.77-85
• /
• 2014

Medicine-bottle classification algorithm to avoid medicine accidents must be robust to a geometric change such as rotation, size variation, location movement of the medicine bottles. In this paper, we propose an algorithm to classify the medicine bottles exactly in real-time by using SIFT(Scale Invariant Feature Transform) which is robust to the geometric change. In first, we classify medicine bottles by size using minimum boundary rectangle(MBR) of medicine bottles as a striking feature in order to classify the medicine bottles. We extract label region in the MBR and the region of interest(ROI) considering rotation. Then, we classify medicine bottles using SIFT for the extracted ROI. We also simplify the number of octave of SIFT in order to improve a process speed of SIFT. We confirm to classify all the medicine bottles exactly as a result of performance evaluation of the proposed algorithm about images of 250 medicine bottles. We also confirm to improve the process time more than twice the processing time by simplifying the number of octave of SIFT.

• Child Health Nursing Research
• /
• v.7 no.2
• /
• pp.161-178
• /
• 2001

The purpose of this study was to identify nursing interventions performed by neonatal nursing unit nurses. For data collection this study used the taxonomy of Nursing Intervention Classification(NIC : 486 nursing intervention) which was modified by McCloskey & Bulecheck(2000). The new 58 nursing interventions was translated into Korean, and then modified by pannel group, which consist of clinical experts and nursing scholars and finally the 419 nursing interventions was selected. The data were collected from 112 nurses. 168 nursing interventions were performed at least monthly by 50％ or more of the nurses. The high frequency of performed nursing interventions were Family domain. 37 nursing interventions were performed at least once a day. The nursing interventions receiving the highest item mean score were neonatal care, neonatal monitoring, photo-therapy; neonate, bottle feeding and temperature regulation. 56 nursing interventions were rarely performed by 90％ or more of the nurses. Most of them were in the behavioral domain. The rarely used interventions were urinary bladder training, art therapy, religious addiction prevention, religious ritual enhancement and bladder irrigation. Therefore, neonatal nursing units nurses used interventions in the Physiological: basic domain most often on a daily basis and the interventions in the behavioral domain least often. These findings will help in building of a standardized language for the neonatal nursing units and enhance the quality of nursing care. Further study will be needed to classify each intervention class and nursing activity and validate NIC in pediatric care unit.