• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.302-307
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• 2007

본 논문은 이미지에서 불필요한 영역을 삭제하고, 그 영역을 배경과 어울리게 채워넣는 이미지 인페인팅 방법을 제안한다. 제안하는 인페인팅 방법은 크게 인페인팅 영역을 채우는 밴드 인페인팅(band in-painting)과 seamless cloning으로 나눌 수 있다. 밴드 인페인팅(band in-painting)은 인페인팅 영역의 경계를 따라서 일정한 두께를 가지는 타겟 밴드(target band)를 정의하고, 인페인팅 영역 밖의 모든 픽셀을 중심으로 하는, 타겟 밴드와 같은 모양과 크기를 가지는 소스 밴드(source band)와 타겟 밴드 차이를 계산하여, 그 값의 차이가 가장 작은 소스밴드 영역의 값을 인페인팅 영역에 복사하는 것이다. Seamless cloning은 인페인팅 영역과 입력 이미지의 경계를 없애는 것이다.

• Journal of Korea Multimedia Society
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• v.11 no.2
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• pp.153-162
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• 2008

We propose a novel image inpainting method composed of two parts: band matching and seamless cloning. In band matching, a band enclosing the boundary of a missing region is compared to those from the other parts of the image. The inner area of the minimum difference band is then copied to the missing region. Even though this band matching results in successful inpainting in many practical applications, brightness discontinuity (a seam) may appear between the filled missing region and its neighborhood. We apply seamless cloning to remove such discontinuity between the two regions. However, since this basic method using one patch may not deal with cases where there are abrupt changes of color or brightness along the boundary, we furthermore devise one more step: target sub-division. The target area is subdivided into small sub-areas, and the band matching and seamless cloning are applied to each of them. The multiple results from the sub-division are then ordered according to inpainting quality, which is measured based on the edge map or discontinuity map along the boundary band.

• Journal of Aerospace System Engineering
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• v.16 no.5
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• pp.70-77
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• 2022

This paper proposes database generation and management system for small-pixelized airborne target recognition. The proposed system has five main features: 1) image extraction from in-flight test video frames, 2) automatic image archiving, 3) image data labeling and Meta data annotation, 4) virtual image data generation based on color channel convert conversion and seamless cloning and 5) HOG/LBP-based tiny-pixelized target augmented image data. The proposed framework is Python-based PyQt5 and has an interface that includes OpenCV. Using video files collected from flight tests, an image dataset for airborne target recognition on generates by using the proposed system and system input.