• Proceedings of the Korean Information Science Society Conference
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• 2004.04a
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• pp.337-339
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• 2004

패치 분배는 시스템의 보안과 네트워크를 구성하는 여러 시스템들에 설치된 소프트웨어의 최약성를 보완하기 위한 가장 중요한 요소 중의 하나이다.[4] 최근 다수의 보안패치 분배 시스템이 나타나면서 이들에 대한 선별기준에서 충족시켜야만 하는 필수 조건으로서 이종 컴퓨팅 환경과 다중 플랫폼, 운영체제, 버전의 지원여부가 중요하게 여겨지고 있다.[5,6] 본 논문에서는 이러한 필수조건들을 충족시킬 수 있는 보안패치 분배 시스템을 설계 및 구현하는데 필요한 보안패치 DB 구축 및 검색 방법을 연구, 제시하고자 한다.

• The KIPS Transactions:PartB
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• v.17B no.4
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• pp.295-302
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• 2010

Example-based super resolution(EBSR) is a method to reconstruct high-resolution images by learning patch-wise correspondence between high-resolution and low-resolution images. It can reconstruct a high-resolution from just a single low-resolution image. However, when it is applied to a text image whose font type and size are different from those of training images, it often produces lots of noise. The primary reason is that, in the patch matching step of the reconstruction process, input patches can be inappropriately matched to the high-resolution patches in the patch dictionary. In this paper, we propose a new patch matching method to overcome this problem. Using an image observation model, it preserves the correlation between the input and the output images. Therefore, it effectively suppresses spurious noise caused by inappropriately matched patches. This does not only improve the quality of the output image but also allows the system to use a huge dictionary containing a variety of font types and sizes, which significantly improves the adaptability to variation in font type and size. In experiments, the proposed method outperformed conventional methods in reconstruction of multi-font and multi-size images. Moreover, it improved recognition performance from 88.58% to 93.54%, which confirms the practical effect of the proposed method on recognition performance.