• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.286-288
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• 2016

기존에 카메라는 침입자를 탐지할 때 정확성이 부족하고 열화상카메라는 가격이 비싸고 열 측정이 되지 않는 상황일 경우 감시가 힘들다는 단점이 있다. 이러한 단점을 보완하기 위해 빛의 간섭 및 회절에 의한 무아레 현상을 이용하여 이상징후 탐지 및 활용방안을 제시하려한다. 지형의 높낮이 및 형상을 저장하고 침입자가 탐지되었을 경우 무아레 이미지를 기반으로 처음 설정했던 지형 데이터와 비교하여 외부인의 침입을 탐지한다. 미세한 움직임이나 변화에도 크게 이미지가 변하는 무아레 현상의 성질을 이용하여 이상징후를 탐지하는 것이다. 이상징후를 탐지 했을 경우 보안 담당관에게 알림을 전송하거나 경보를 울리는 이상징후 탐지 솔루션 및 활용방안을 제안한다.

• Journal of Korea Multimedia Society
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• v.15 no.11
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• pp.1369-1376
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• 2012

In this paper, we propose 2D industrial image registration method for the detection of defects. Proposed method performs preprocessing to smooth the original image with the preservation of the edge for the robust registration against general noise. Then, x-direction gradient magnitude image and corresponding binary image are generated. Density analysis around neighborhood regions per pixel are performed to generate feature image for preventing mis-registration due to moire-like patterns, which frequently happen in industrial images. Finally, 2D image registration based on phase correlation between feature images is performed to calculate translational parameters to align two images rapidly and optimally. Experimental results showed that the registration accuracy of proposed method for the real industrial images was 100% and our method was about twenty times faster than the previous method. Our fast and accurate method could be used for the real industrial applications.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1990-1993
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• 2003

In this paper, we propose a technique for automatic spinal deformity detection from $moir\acute{e}$ topographic images. Normally the $moir\acute{e}$ stripes show symmetry as a human body is almost symmetric. According to the progress of the deformity of a spine, asymmetry becomes larger. Numerical representation of the degree of asymmetry is therefore useful in evaluating the deformity. First, displacement of local centroids and difference of gray values are evaluated statistically between the left- and the right-hand side regions of the $moir\acute{e}$ images with respect to the extracted middle line. We classify the moire images into two categories i.e., normal and abnormal cases from the features, employing discriminant analysis. An experiment was performed employing 1,200 $moir\acute{e}$ images and 85% of the images were classified correctly.

• The Journal of the Korea Contents Association
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• v.9 no.4
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• pp.189-197
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• 2009

A digital image usually has 8 bits of depth basically representing pixel intensity ranging for [0 255]. These pixel range allow 256 step levels of pixel values in the image. Thus the greyscale value for a given image is an integer. When we carry out interpolation of a given image for resizing we have to round the interpolated value to integer which can result in loss of quality on perceived color values. This paper proposes a new method for recovering this loss of information during interpolation process. By using the proposed method the pixels tend to regain more original values which yields better looking images on resizing.