• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.560-561
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• 2018

The spread of smartphones is increasing the proportion of mobile shopping in the online shopping market. Most mobile shopping services are delivered through applications. However, personalization services are very important for user data collection and analysis. Therefore, in this paper, we implemented the product barcode recognition function and machine learning-based product image recognition function using smartphones camera to collect user data in mobile shopping environment. The implemented function and push notification services enabled the collection and analysis of user data and personalization services for online shopping platform applications.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.1
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• pp.174-183
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• 2011

This paper is about a bar code decoding algorithm developed for smart phone applications. The algorithm consists of bar code data extraction procedure, bar code signal estimation procedure, and bar code decoding procedure. To detect the peak bar code module, a DSTW had been applied because of its outstanding performance in ECG peak detection. In order to minimize errors due to non-uniform light effect, the proposed algorithm was acted as a baseline wandering filter based on module peaks detection. The algorithm had been tested to evaluate the performance under the conditions of blurring, non-uniformed light and white noises. The algorithm had shown excellent performance in reconstruction of bar code decoding, compared to other conventional methods. In order to show the possibility of applying the algorithm to a smart phone, a real UPC-A type 150 bar code pictures obtained from a smart phone camera was applied to the algorithm, achieving the correct recognition rate of 97.33%.

• The Journal of Korea Robotics Society
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• v.19 no.2
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• pp.176-187
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• 2024

Recently, automated solutions using collaborative robots have been emerging in various industries. Their primary functions include Pick & Place, Peg in the Hole, fastening and assembly, welding, and more, which are being utilized and researched in various fields. The application of these robots varies depending on the characteristics of the grippers attached to the end of the collaborative robots. To grasp a variety of objects, a gripper with a high degree of freedom is required. In this paper, we propose a service automation system using a multi-degree-of-freedom gripper, collaborative robots, and vision sensors. Assuming various products are placed at a checkout counter, we use three cameras to recognize the objects, estimate their pose, and create grasping points for grasping. The grasping points are grasped by the multi-degree-of-freedom gripper, and experiments are conducted to recognize barcodes, a key task in service automation. To recognize objects, we used a CNN (Convolutional Neural Network) based algorithm and point cloud to estimate the object's 6D pose. Using the recognized object's 6d pose information, we create grasping points for the multi-degree-of-freedom gripper and perform re-grasping in a direction that facilitates barcode scanning. The experiment was conducted with four selected objects, progressing through identification, 6D pose estimation, and grasping, recording the success and failure of barcode recognition to prove the effectiveness of the proposed system.

• The Journal of Sustainable Design and Educational Environment Research
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• v.9 no.3
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• pp.1-11
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• 2010

In the construction industry, IT based information system has been diversely applied to increase productivity. Although IT device such as PDA, RFID, Barcode, wireless network and web camera has been introduced to gather information in construction site, the effect of the IT device is limited, because of bringing about additional works of engineer. In this paper, we proposed a defect management system which is based on handwritten document digitalization framework for introducing applicability of new IT device, digital pen. By the proposed system, we can effectively gather and input defect information to defect management system by using digital pen and paper like conventional way. Applying the data gathering device, digital pen to defect management, it is able to increase productivity by improving work process, building up and utilizing defect information database of good quality.

• Proceedings of the Korea Contents Association Conference
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• 2011.05a
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• pp.573-574
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• 2011

기존의 4-State 바코드 인식 방법은 정확한 바코드 인식을 위해 모든 윤곽선을 이용하여 외곽선을 추출하고, 학습에 따른 분석 모델을 적용하는 방식이었지만, 본 논문에서는 바코드의 최외각의 윤곽선을 이용하여 불필요한 연산 작업을 최소화하고 인식과정에서 3-line 화소 구분법으로 학습데이터의 사용의존도를 최소화하는 고속 우편물 인식 시스템 개발에 목적을 둔다.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.390-393
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• 2018

현재 사용되고 있는 카트의 경우 물품들을 모아서 계산대에서 한번에 계산을 처리하기 위해 물품 계산 이전에 물품을 모아 놓는 역할을 한다. 이는 시간과 비효율성의 문제를 낳는다. 이를 극복하기 위해 카트에서 실시간으로 고객이 직접 바코드를 입력하는 방법을 연구하였으며, 영상처리 기법을 사용하여 좀 더 인간친화적인 방법으로 카트를 이용하는 방법을 고안하였다. 또한 무게 센서감지를 통해 상품목록의 보안성을 높이려고 하였다.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.7
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• pp.92-100
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• 2004

A service robot is expected to be useful in indoor environment such as a hotel, a hospital and so on. However, many service robots are driven by wheels so that they cannot climb stairs to move to other floors. If the robot cannot use elevators. In this paper, the mobile manipulator system was developed, which can operate numeral buttons on the operating panel in the elevator. To perform this task, the robot is composed of an image recognition module, an ultrasonic sensor module and a manipulator. The robot can recognize numeral buttons and an end-effector in manipulator by the vision system. The Learning vector quantization (LVQ) algorithm is used to recognize the number on the button. The barcode mark on the end-effector is used to recognize the end-effector. The manipulator can push numeral buttons using informations captured by the vision system. The proposed method is evaluated by experiments.

• ALGAE
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• v.33 no.3
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• pp.215-229
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• 2018

In this study, the phylogenetic relationships among the genera Alsidium C. Agardh and Bryothamnion $K{\ddot{u}}tzing$ were investigated. Phylogenetic analyses using the plastid-encoded markers rbcL, psbA, and the mitochondrial barcode region (COI-5P) resolved a well-supported clade that included the species Alsidium corallinum, Bryothamnion seaforthii, and B. triquetrum. Our results indicated that taxonomic recognition of the genus Bryothamnion is not supported and two species of Bryothamnion are reallocated to Alsidium. A reexamination of the morphological definition of Alsidium is provided with an updated diagnosis of the genus and a morphology-based comparison of species that are currently circumscribed under this generic name. Furthermore, we reviewed morphological differences and similarities between Alsidium and the genus Digenea, both belonging to the tribe Alsidieae, discussing the most relevant morphological characters.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.584-587
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• 2010

이차원 바코드는 인식이 편리하고 영상의 변이나 손실에 강인하기 때문에 널리 사용되고 있다. 본 논문에서는 모바일 장치에서 카메라로 촬영한 영상으로부터 이차원 바코드의 한 종류인 데이터매트릭스를 검출하고 인식하는 방법을 소개한다. 모바일 안드로이드 환경에서 빠른 속도로 검출 및 인식 과정을 수행하기 위해 JNI를 이용하여 C로 기능을 구현하였다. 먼저, 이차원 영상을 이진화한 후 연결 성분 탐색을 통해 바코드 후보영역을 추출한다. 그리고 직선을 검출하여 데이터매트릭스의 주요소인 L 인식 패턴과 타이밍 패턴을 찾는다. 각 패턴을 이용하여 바코드 영역의 꼭지점을 찾아낸 후, 회전이나 기울어짐을 정규화하여 정사각형 형태로 교정한다. 그 후, 타이밍 패턴을 기준으로 데이터 영역을 인식한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.04a
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• pp.459-462
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• 2010

반도체 공정에서 Track In/Out 판단을 위해서 바코드와 RFID(Radio-Frequency IDentification)가 주로 사용되고 있다. 현재 알려진 바코드 인식률은 약 80%로써 재고 유실이 여전히 존재하며 정확성과 신뢰성을 담보하지 못하고 있는 현실이다. 이 연구에서는 20% 정도의 인식되지 않은 부분을 보완하기 위해 바코드를 영상 처리하고 그 내부에 있는 문자들을 인식하는 프로그램을 개발한다. 해당 공정에서 바코드가 제대로 인식이 안 된 경우에는 인식된 문자들을 참고함으로써 공정의 전체 과정을 파악하고 트랙킹할 수 있다. 바코드의 문자 인식을 생산 공정에 맞추어 실시간에 수향하여 필요시 활용함으로써 공정의 정확성과 생산성을 향상에 기여하고자 한다.