• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.772-774
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• 2005

간판인식은 관광지의 간판을 자동 인식하여 외국인 또는 외지인에게 편리한 관광 정보제공을 목적으로 연구되고 있다. 이러한 연구의 효율적인 진행을 위해 객관적인 간판영상 데이터베이스가 필수적이다. 이 논문은 전주 한옥마을을 대상으로 수집한 간판영상 데이터베이스에 대해 기술한다. 총 45개의 서로 다른 간판에 대해 각각 50개씩 영상을 다양한 조건에서 획득하였다.

• The Journal of the Korea Contents Association
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• v.6 no.11
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• pp.243-248
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• 2006

Recognition of sign has been studied to provide convenience tour information for foreigners and strangers through automatic recognition of sign. The sign image database is essential to training the classifier and to intuitive measurement of performance. In this paper, we described the sign image database collected at Jeonju Hanok Village. As to 45 each other sign image, corresponding 50 images are collected under several condition. This database could be important content to study for the field of pattern recognition.

• The Journal of the Korea Contents Association
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• v.8 no.11
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• pp.10-17
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• 2008

The sign recognition and translation offer information and support decision making for foreigners or city tourist. Collecting sign images and building words in signs are essential to train machine recognizers and to evaluate systems. In this paper, we analyze the characteristics of sign images. The collected sign images are about 1000 captured from difference conditions and locations. We also build a dictionary of words in 100,000 sign names.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.1
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• pp.69-83
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• 2020

Signboards are standardized according to national legislation for the safety of pedestrians and disaster prevention in urban areas. Signboards should be installed according to the standard. However, it is not easy to manage the signboards systematically due to the number of signboards that have been installed for a long time and frequently changing stores. In this study, we proposed a methodology for distinguishing signboards that deviated from the standard. To this end, the signboard was photographed using a stereo camera, and then the three-dimensional coordinates of the signboard were determined from the signboard image to calculate the signboard's horizontal and vertical dimensions to determine the signboard's specifications. In order to determine the interior and relative orientation parameters of the stereo camera, an outdoor three-dimensional building was used as the test field. Then, the image coordinates of four vertices of the signboard were extracted from the signboard image taken from about 15m ~ 22m distance using deep learning. After determining the signboard's three-dimensional coordinates by using the interior and relative orientation parameters of the stereo camera and the image coordinates of the four vertices of the signboard, the horizontal and vertical sizes of the signboard were calculated, resulting in an error of about 2.7cm on average. The specifications for the ten flat-type signboards showed that all of the horizontal sizes were compliant with the specifications, but the vertical sizes exceeded about 36.5cm on average. Through this, it was found that maintenance of flat-type signboards is needed overall.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.4
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• pp.219-231
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• 2019

The specifications of signboards are set for each type of signboards, but the shape and size of the signboard actually installed are not uniform. In addition, because the colors of the signboard are not defined, so various colors are applied to the signboard. Methods for recognizing signboards can be thought of as similar methods of recognizing road signs and license plates, but due to the nature of the signboards, there are limitations in that the signboards can not be recognized in a way similar to road signs and license plates. In this study, we proposed a methodology for recognizing plate-type signboards, which are the main targets of illegal and old signboards, and automatically extracting areas of signboards, using the deep learning-based Faster R-CNN algorithm. The process of recognizing flat type signboards through signboard images captured by using smartphone cameras is divided into two sequences. First, the type of signboard was recognized using deep learning to recognize flat type signboards in various types of signboard images, and the result showed an accuracy of about 71%. Next, when the boundary recognition algorithm for the signboards was applied to recognize the boundary area of the flat type signboard, the boundary of flat type signboard was recognized with an accuracy of 85%.

• Journal of Broadcast Engineering
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• v.22 no.3
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• pp.339-349
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• 2017

In this paper, we propose a technique for smoothly replacing a signboard image in a video clip with another signboard image and an application for performing it. In order to replace the signboard area in the image, it must be tracked and the shape of the signboard changed. Also, when an obstruction occurs due to a front object, the obscured area is extracted so that only the signboard area can be replaced, and color correction is performed for natural synthesis. The proposed application receives the information required by the algorithm from the user and explains the required functions, thereby disclosing the entire process in which the algorithm is actually implemented and tested. This can be used for a variety of media, and can be used as a means to remove the indirect advertisement that is often seen, and it can also be used as a new advertisement model in which another advertisement image is inserted into the image to be advertised.

• The KIPS Transactions:PartB
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• v.16B no.3
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• pp.239-246
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• 2009

In this paper, we propose a Korean character recognition method from outboard signboard images. We have chosen 808 classes of Korean characters by an analysis of frequencies of appearance in a dictionary of signboard names. The proposed method mainly consists of three steps: feature extraction, rough classification, and coarse classification. The first step is to extract a nonlinear directional segments feature, which is immune to the distortion of character shapes. The second step computes an ordered set of 10 recognition candidates using a minimum distance classifier. The last step reorders the recognition candidates using a Fisher discriminant measure. As experimental results, the recognition accuracy is 80.45% for the first choice, and 93.51% for the top five choices.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.06a
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• pp.391-393
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• 2013

실내 측위 시스템은 실내 환경에서 위치 기반 서비스를 하는데 매우 유용하다. 그러나, Wi-fi 신호 등을 사용하는 일반적인 실내 측위 시스템들은 대부분 시스템의 설치와 유지 보수에 많은 비용이 소요된다. 마커를 기준으로 측위를 하는 영상 기반의 시스템도 있지만, 일반 사용자들에게 미적인 문제를 야기 시킬 수도 있다. 이 논문에서, 우리는 대부분의 공공장소에 이미 설치되어 있는 간판 등을 이용하는 새로운 영상 기반의 실내 측위 시스템을 소개한다. 간판의 위치와 방향은 그것들이 설치 될 때 해당 건물의 기준 좌표계에서 위치와 방향이 결정되기 때문에, 간판에 대한 사용자의 상대적인 위치와 방향을 얻어내면 건물의 기준 좌표계에서 사용자의 위치와 방향을 구할 수 있다. 실제 공공장소에서 수행 된 실험은 우리의 시스템이 실내 측위의 실용적인 방법이 될 수 있음을 보여준다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.8
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• pp.317-323
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• 2017

This study was conducted to investigate the differences in magnetic resonance imaging (MRI) findings after lumbar epidural nerve block using the transforaminal approach and the interlaminar approach in patients with low back pain. This study was an observational analysis study of abnormal findings of MRI after epidural nerve block. This study included 78 patients who underwent MRI at approximately 24 h after lumbar epidural nerve block at a pain clinic of a university hospital between January 2007 and December 2016. Among patients who received epidural nerve block, 36 used the interlaminar approach and 42 used the transforaminal approach. The incidence of patients with abnormal changes in MRI findings was higher among patients using the interlaminar approach (53%) than those using the transforaminal approach (7%). Abnormal MRI findings included epidural air or fluid, needle tracks, and soft tissue changes, with epidural air being the most frequent abnormal finding (72%). We recommend use of the transforaminal approach to reduce the possibility of misreading or difficulty in interpretation of images of patients who underwent MRI at approximately 24 h after lumbar epidural nerve block. Practitioners should consider the possibility of abnormal findings such as epidural air on MRI in cases of epidural nerve block using the interlaminar approach.

• The Journal of the Korea Contents Association
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• v.12 no.2
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• pp.105-115
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• 2012

In this paper, we propose a correction method using phoneme unit segmentation to solve misrecognition of Korean Texts in signboard images using weighted Disassemble Levenshtein Distance. The proposed method calculates distances of recognized texts which are segmented into phoneme units and detects the best matched texts from signboard text database. For verifying the efficiency of the proposed method, a database dictionary is built using 1.3 million words of nationwide signboard through removing duplicated words. We compared the proposed method to Levenshtein Distance and Disassemble Levenshtein Distance which are common representative text string comparison algorithms. As a result, the proposed method based on weighted Disassemble Levenshtein Distance represents an improvement in recognition rates 29.85% and 6% on average compared to that of conventional methods, respectively.