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드라마 <오징어게임> 속 비속어 번역 양상과 격식성 변화
이가현 ( Gahyun Lee ) 이화여자대학교 통역번역연구소 2022 T&I review Vol.12 No.1
Korean cultural contents, collectively referred to as “K-Contents” in Korea, have been drawing growing interest since a Korean drama titled “Squid Game” gained immense popularity across the world after its release by the OTT (over-the-top) media service provider Netflix in September 2021. Subsequently, the violent and sexually suggestive content of “Squid Game” became a subject of controversy. While the degree of acceptance toward its violent and sexually suggestive content differed from country to country, the level of verbal violence and sexual suggestiveness expressed in the characters’ lines also differed by language, which is an important factor to consider in the context of translation. This research paper compares the Korean script of “Squid Game” with its Japanese subtitles with a focus on the use of swearwords, as well as English subtitles to further identify significant differences, and analyzes how formality changes in line with the level of profanity. The outcomes of the comparative analysis indicate that both the Japanese and English translations showed a higher degree of formality than the original Korean script. However, the analysis of the actual translation of swearwords in each language revealed that differences in translation stemmed from the patterns of how such swearwords are used in practice in the culture associated with each language. (Hankuk University of Foreign Studies, Korea)
에지 보정을 이용한 ORB SLAM2의 특징점 정합률 개선
이가현(Gahyun Lee),이수원(Suwon Lee),서영건(Yeong-Geon Seo) 한국디지털콘텐츠학회 2021 한국디지털콘텐츠학회논문지 Vol.22 No.1
As a field of research on image recognition, a method of extracting key feature points and matching between images is used when recognizing or analyzing objects. Feature point extraction algorithms used here include SIFT, SURF, and ORB. ORB is used in autonomous driving technology along with visual SLAM technology that generates a map and estimates the location by recognizing and analyzing the surrounding space. In the case of autonomous driving, since the drivers safety is closely related to the occurrence of accidents between vehicles, image recognition technology is very important, so position estimation should be made based on faster analysis and accurate processing. In our study, to solve the problem, we propose a method to extract many feature points from the input image through image correction and to generate a new key by modifying the key frame generation condition, thereby reducing the position estimation and matching failure rate. As a result of the experiment, the matching rate improved by 43% and the number of new key frames increased.