• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.500-503
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• 2012

스마트폰은 폭발적인 성장세와 더불어 어플리케이션의 시장규모가 크게 증대될 것으로 예상된다. 본 논문에서는 직접 사진을 캡처 시킨 문자들을 텍스트로 변환시켜주고 데이터베이스를 이용해서 그 문자들의 의미를 표현하는 어플리케이션을 구현한다. 어플리케이션의 기능으로는 텍스트로 변환하고자 하는 사진을 찍거나 혹은 앨범에 있는 사진을 불러내어 텍스트로 변환을 시켜서 상대방에게 메시지나 블루투스 등을 이용하여 전송을 가능하게 해줄 수 있으며, 글꼴을 바꾼다거나 크기 등 편집 기능까지 추가적으로 가능하도록 할 수 있다. 이 어플리케이션으로 인한 효과로 인터넷이나 거리를 돌아다니다가 원하는 문구가 있으면 바로 원하는 상대방에게 쉽고 간편하고 빠르게 전달할 수 있는 편리함 등이 있다.

• Journal of Digital Contents Society
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• v.15 no.6
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• pp.737-743
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• 2014

For single image super resolution (SR), interpolation based and example based algorithms are extensively used. The interpolation algorithms have the strength of theoretical simplicity. However, those algorithms are tending to produce high resolution images with jagged edges, because they are not able to use more priori information. Example based algorithms have been studied in the past few years. For example based SR, the nearest neighbor based algorithms are extensively considered. Among them, neighbor embedding (NE) has been inspired by manifold learning method, particularly locally linear embedding. However, the sizes of local training sets are always too small. So, NE algorithm is weak in the performance of the visuality and quantitative measure by the poor generalization of nearest neighbor estimation. An improved NE algorithm with Support Vector Regression (SVR) was proposed to solve this problem. Given a low resolution image, the pixel values in its high resolution version are estimated by the improved NE. Comparing with bicubic and NE, the improvements of 1.25 dB and 2.33 dB are achieved in PSNR. Experimental results show that proposed method is quantitatively and visually more effective than prior works using bicubic interpolation and NE.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.1
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• pp.152-159
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• 2010

Existing character recognition algorithm recognize characters in simple conditional. It has the disadvantage that recognition rates often drop drastically when input document image has low quality, rotated text, various font or size text because of external noise or data loss. In this paper, proposes the optical character recognition algorithm which using bicubic interpolation method for the catalog retrieval when the input image has rotated text, blurred, various font and size. In this paper, applied optical character recognition algorithm consist of detection and recognition part. Detection part applied roberts and hausdorff distance algorithm for correct detection the catalog of book. Recognition part applied bicubic interpolation to interpolate data loss due to low quality, various font and size text. By the next time, applied rotation for the bicubic interpolation result image to slant proofreading. Experimental results show that proposal method can effectively improve recognition rate 6% and search-time 1.077s process result.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.425-433
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• 2018

In this paper, we propose a super-resolution reconstruction algorithm for plenoptic images based on the reliability of disparity. The subperture image generated by the Flanoptic camera image is used for disparity estimation and reconstruction of super-resolution image based on TV_L1 algorithm. In particular, the proposed image reconstruction method is effective in the boundary region where disparity may be relatively inaccurate. The determination of reliability of disparity vector is based on the upper, lower, left and right positional relationship of the sub-aperture image. In our method, the unreliable vectors are excluded in reconstruction. The performance of the proposed method was evaluated by comparing to a bicubic interpolation method, a conventional disparity based method and dictionary based method. The experimental results show that the proposed method provides the best performance in terms of PSNR(Peak Signal to noise ratio), SSIM(Structural Similarity).