• 디지털산업정보학회논문지
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• 제14권3호
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• pp.21-28
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• 2018

As computer performance and needs of user convenience increase, computer user interface are also changing. This changes had great effects on software development environment. In past, text editors like vi or emacs on UNIX OS were the main development environment. These editors are very strong to edit source code, but difficult and not intuitive compared to GUI(Graphical User Interface) based environment and were used by only some experts. Moreover, the trends of software development environment was changed from command line to GUI environment and GUI Editor provides usability and efficiency. As a result, the usage of text based editor had decreased. However, because GUI based editor use a lot of computer resources, computer performance and efficiency are decreasing. The more contents are, the more time to verify and display the contents it takes. In this paper, we provide a new parser that provide multi view editing, incremental parsing and multiple entry of abstract syntax tree.

• 융합보안논문지
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• 제15권2호
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• pp.3-9
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• 2015

디지털 압축 기술로 인한 방송 대역폭 증가 효과와 방송수신기의 영상처리기술 발달에 따라 디지털방송에 멀티 뷰 기능을 활용하는 연구 및 응용 개발이 진행되고 있다. 디지털방송에서의 멀티 뷰 기능은 시청자에게 서너 개의 방송프로그램 영상을 동시에 시청할 수 있게 하거나 스포츠 방송의 경우 멀티 앵글 영상을 제공하여 원하는 시점에서 경기를 감상할 수 있도록 하는데 주로 응용되고 있는데 백 수십 개의 방송프로그램을 제공하고 있는 근래의 디지털방송 양상을 반영하고 데이터서비스의 다양한 멀티 뷰 활용을 지원하는 통합적인 멀티 뷰 영상 지원 방법이 존재하지 않고 있다. 이 에, 본 논문은 디지털방송 사업자가 운영하는 모든 방송프로그램 영상들을 포함하는 멀티 뷰 영상을 구성하고 이를 데이터서비스가 활용할 수 있도록 DVB 표준에 정합되는 멀티 뷰 영상 활용 정보를 구성하고 전송하는 방식, 그리고 데이터서비스가 멀티 뷰 영상을 자신의 기능으로 수월하게 활용할 수 있도록 방송수신기의 미들웨어를 확장하는 방법을 제안한다.

• Journal of Information Processing Systems
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• 제13권5호
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• pp.1126-1134
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• 2017

A variety of medical service applications in the field of the Internet of Things (IoT) are being studied. Segmentation is important to identify meaningful regions in images and is also required in 3D images. Previous methods have been based on gray value and shape. The Visible Korean dataset consists of serially sectioned high-resolution color images. Unlike computed tomography or magnetic resonance images, automatic segmentation of color images is difficult because detecting an object's boundaries in colored images is very difficult compared to grayscale images. Therefore, skilled anatomists usually segment color images manually or semi-automatically. We present an out-of-core 3D segmentation method for large-scale image datasets. Our method can segment significant regions in the coronal and sagittal planes, as well as the axial plane, to produce a 3D image. Our system verifies the result interactively with a multi-planar reconstruction view and a 3D view. Our system can be used to train unskilled anatomists and medical students. It is also possible for a skilled anatomist to segment an image remotely since it is difficult to transfer such large amounts of data.