• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.10
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• pp.2280-2286
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• 2012

In this paper, we propose a novel nonlinear 3D image correlator using a fast computational integral imaging reconstruction (CIIR) method. In order to implement the fast CIIR method, the magnification process was eliminated. In the proposed correlator, elemental images of the reference and target objects are picked up by lenslet arrays. Using these elemental images, reference and target plane images are reconstructed on the output plane by means of the proposed fast CIIR method. Then, through nonlinear cross-correlations between the reconstructed reference and the target plane images, the pattern recognition can be performed from the correlation outputs. Nonlinear correlation operation can improve the recognition of 3D objects. To show the feasibility of the proposed method, some preliminary experiments are carried out and the results are presented by comparing the conventional method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.5
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• pp.1164-1170
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• 2011

In this paper, we propose accurate lattice extraction of elemental image array and pre-processing methods in computational integral imaging. Pre-processing methods remove distortions and noises of the image. Such distortions occurred in pickup systems are rotational errors. Distortions will degrade the resolution of reconstructed images. To overcome this problem, we propose our methods for extraction of elemental image array and pre-processing methods. Also, we describe that distortions affect the high quality reconstruction. Optical and computational experiments indicate that reconstructed images applied our methods is better than reconstructed images unapplied our methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.155-157
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• 2012

In this paper, we propose a nonlinear 3D image correlator using computational reconstruction of 3D images based on integral imaging. In the proposed method, the elemental images for reference 3D object and target 3D object are recorded through the lens array. The recorded elemental images are reconstructed as reference plane image and target plane images using the computational integral imaging reconstruction algorithm and the nonolinear correlation between them is performed for object recognition. To show the usefulness of the proposed method, the preliminary experiments are carried out and the experimental results are presented compared with the conventional results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.5
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• pp.1171-1176
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• 2011

This paper describes an analysis on the relationship between elemental image size and object locations in the computational integral imaging reconstruction and in the pickup using a periodically-distributed lenslet array. A sparse sampling effect arises from a periodically-distributed lenslet array in the pickup of 3D objects. The relationship between elemental image size and object location is also reported. Based on the analysis, a method to eliminate the sparse sampling is proposed. To show the effectiveness of the proposed method, experimental results are carried out. It turns out that the theory works.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.11
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• pp.2563-2568
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• 2010

SAII (synthetic aperture integral imaging) is a useful technique to record many multi view images of 3D objects by using a moving camera and to reconstruct 3D depth images from the recorded multiviews. This is largely composed of two processes. A pickup process provides elemental images of 3D objects and a reconstruction process generates 3D depth images computationally. In this paper, a signal model for SAII is presented. We defined the granular noise and analyzed its characteristics. Our signal model revealed that we could reduce the noise in the reconstructed images and increase the computational speed by reducing the shifting distance of a single camera.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.1
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• pp.121-126
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• 2009

Plane-based computational integral imaging (CIIR) provides the reconstruction of depth-dependent 3D plane images. However, it has problem degrading the resolution of reconstructed images due to the artifact noise according to the depth. In this paper, to overcome this problem, a signal model for plane-based CIIR is explain. Also the compensation process is introduced to remove the noise caused from CIIR. Computational experiments show that we analyze the characteristics of noise in the reconstructed image of 2D Gaussian image and the high-resolution images can be obtained by using the compensation process.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.10
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• pp.1853-1859
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• 2008

In this paper, we propose a novel method to improve the visual quality of reconstructed images for 3D pattern recognition based on the computational integral imaging reconstruction (CIIR). The proposed CIIR method provides improved 3D reconstructed images by superimposing magnified elemental images by a combination of a round mapping model and image interpolation algorithms. To objectively evaluate the proposed method, we introduce an experimental framework for a computational pickup process and a CIIR process using a Gaussian function and evaluate the proposed method. We also carry out experiments on 3D objects and present their results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.6
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• pp.1363-1368
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• 2011

This paper describes an analysis on the granular noise in pixel-to-pixel mapping-based computational integral imaging. The pixel mapping-based method provides a high-resolution reconstructed images and also its computational cost is very lower than the previous back-projection-based method. In this paper, a signal model for the pixel mapping-based method is introduced, which defines and analyzes the granular noise. Computer experiments provides the granular noise properties based on the proposed signal model. The experimental results indicates that the granular noise pattern differs from that of the back-projection based method. The results is also utilized in the pixel mapping-based method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.85-88
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• 2008

본 논문에서는 집적 영상 (integral imaging) 방식과 픽셀 스크램블링 (pixel scrambling) 기술을 이용한 광 영상 암호화 (optical image encryption) 방법을 제안한다. 제안한 방법의 부호화 과정에서는 먼저 입력영상을 여러 개의 작은 사이즈의 블록으로 나누어 픽셀 스크램블링을 한 다음 집적 영상 기술을 이용하여 요소 영상(elemental image)을 생성하고, 이 영상의 안정성을 위하여 2차 픽셀 스크램블링을 수행하여 최종 암호화된 영상을 얻는다. 그리고 복호화 과정에서는 암호화된 영상에 광학적인 집적 영상 복원 기법과 역 픽셀 스크램블링 방법을 사용하여 원 영상을 복원한다. 제안하는 광 암호화 방법에 대해서 크로핑과 같은 데이터 손실 및 노이즈에 대한 컴퓨터 적으로 모의실험을 수행하여 강인성과 유용성을 보였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.7
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• pp.1598-1603
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• 2011

This paper describes an enhancement method for a computational pickup model. The conventional computational pickup model utilizes the ray-trace model and the pinhole model. The conventional model is very useful, however, it suffers from quality degradation of reconstructed images at long distances. To overcome the problem, we propose an accurate pickup model. The proposed model includes integration of the rays incoming to a sensor that generates a pixel, resulting in robustness on the Aliasing artifact. To show the effectiveness of the proposed method, experimental results are carried out. The results indicated that the proposed method is superior to the conventional method.