• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.339-340
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• 2021

한 첩씩 복용하는 알약은 복용 여부를 정확히 판별하기 어렵다. 그래서 이러한 상황에서 벗어나고자 만든 스마트 약통을 제안하고 있다. 이 스마트 약통은 약의 오남용을 방지하고, 날짜별 복용 여부를 휴대폰 어플로 알려주는 장점을 가지고 있다. 장기간 복용하는 약은 한 번의 내원으로 많은 양의 약을 처방받아 오기 때문에 기억력이 좋지 않은 어른이 사용하기에 유용하다. 처방 받은 약통에 있는 QR코드를 최초 입력함으써 약 3일 정도의 데이터 수집기간을 통해 평균 복용시간을 인식하고, 평균 복용시간을 인식한 후에 약 먹을 시간을 알려주는 기능을 탑재하였다. 평상시에는 잠금장치를 통해 걸어 열 수 없게 프로그램을 설정하고, 복용시간에만 잠금장치를 해제하여 환자의 약물 오남용을 막고 안전하게 복용할 수 있을 것이다.

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

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.13-20
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• 2024

This paper introduces a method for generating model images that can identify specific cylindrical medicine containers in videos and investigates data collection techniques. Previous research had separated object detection from specific object recognition, making it challenging to apply automated image stitching. A significant issue was that the coordinate-based object detection method included extraneous information from outside the object area during the image stitching process. To overcome these challenges, this study applies the newly released YOLOv8 (You Only Look Once) segmentation technique to vertically rotating pill bottles video and employs the ORB (Oriented FAST and Rotated BRIEF) feature matching algorithm to automate model image generation. The research findings demonstrate that applying segmentation techniques improves recognition accuracy when identifying specific pill bottles. The model images created with the feature matching algorithm could accurately identify the specific pill bottles.