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Depth Extraction of Integral Imaging Using Correlation

상관관계를 활용한 집적 영상의 깊이 추출 방법

  • Kim, Youngjun (Department of Electrical, Electronic, and Control Engineering, IITC, Hankyong National University) ;
  • Cho, Ki-Ok (Department of Electrical, Electronic, and Control Engineering, IITC, Hankyong National University) ;
  • Kim, Cheolsu (Department of Electric Energy and Computer Engineering, Gyeongju University) ;
  • Cho, Myungjin (Department of Electrical, Electronic, and Control Engineering, IITC, Hankyong National University)
  • Received : 2016.03.23
  • Accepted : 2016.04.21
  • Published : 2016.07.31

Abstract

In this paper, we present a depth extraction method of integral imaging using correlation between elemental images with phase only filter. Integral imaging is a passive three-dimensional (3D) imaging system records ray information of 3D objects through lenslet array by 2D image sensor, and displays 3D images by using the similar lenslet array. 2D images by lenslet array have different perspectives. These images are referred to as elemental images. Since the correlation can be calculated between elemental images, the depth information of 3D objects can be extracted. To obtain high correaltion between elemental images effectively, in this paper, we use phase only filter. Using this high correlation, the corresponding pixels between elemental images can be found so that depth information can be extracted by computational reconstruction technique. In this paper, to prove our method, we carry out optical experiment and calculate Peak Sidelobe Ratio (PSR) as a correlation metric.

본 논문에서는 요소 영상간의 상관관계를 위상필터를 이용하여 구하고 집적 영상 기술에서 이를 활용하여 깊이정보를 추출하는 시스템을 제안한다. 집적 영상 기술은 수동 광원을 사용하여 렌즈 배열을 통해 3차원 물체의 광선정보를 2차원 영상 획득 장치로 기록하고 이를 동일한 렌즈 배열을 통해 3차원 영상을 디스플레이 하는 3차원 영상 시스템 기술이다. 렌즈 배열을 통해 획득된 2차원 영상들은 서로 다른 원근감을 가지는 요소 영상이다. 이 영상들과 물체와의 상관관계를 구하여 깊이정보를 추출할 수 있다. 그리고 요소 영상으로 컴퓨터 재생을 하여 다시 필터를 이용한 상관관계를 구하게 되면 더욱 정확한 깊이정보를 얻을 수 있다. 본 논문에서는 제안된 기술을 검증하기 위해 광학적 실험을 수행하고 상관관계의 지표로써 Peak Side lobe Ratio (PSR)을 구하였다.

Keywords

References

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