• 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.17 no.4
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• pp.981-988
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• 2013

In this paper, we propose a method to generate defocusing images at the focusing plane using the 3D spatial information of object through pickup process of integral imaging technique. In the proposed method, the focusing and defocusing images are generated by the convolution operation between elemental images and ${\delta}$ function array. We observed the image difference by defocusing degree according to the distance of focusing plane. To show the feasibility of the proposed method, some preliminary experiments are carried out and the results are presented.

• Journal of the Optical Society of Korea
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• v.13 no.1
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• pp.139-145
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• 2009

Computational integral imaging (CII) is a new method for 3D imaging and visualization. However, it suffers from seriously poor image quality of the reconstructed image as the reconstructed image plane increases. In this paper, to overcome this problem, we propose a CII method based on a smart pixel mapping (SPM) technique for partially occluded 3D object recognition, in which the object to be recognized is located at far distance from the lenslet array. In the SPM-based CII, the use of SPM moves a far 3D object toward the near lenslet array and then improves the image quality of the reconstructed image. To show the usefulness of the proposed method, we carry out some experiments for occluded objects and present the experimental results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.9
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• pp.1767-1778
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• 2009

A robust and secure InIm(Integral imaging)-based 3D watermarking scheme using cellular automata transform (CAI) is proposed. In the InIm-based 3D watermarking scheme, the elemental image array (EIA) watermark for the target watermark which has to be detected, is synthesized from the computational pickup process of InIm and embedded in a cover image. The EIA watermark can provide a robust reconstruction of the target watermark However, the 3D property of the EIA watermark causes a weakening of the security. To overcome this problem, the proposed method uses the CAT domain to embed and extract the EIA watermark in the cover image. The use of CAT significantly improves the security for our watermarking algorithm using a single secure key only. Experiments are presented to show that the proposed scheme shows robust and secure performances against various attacks.

• Journal of information and communication convergence engineering
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• v.19 no.2
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• pp.102-107
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• 2021

In this paper, we propose a new three-dimensional (3D) photon-counting integral imaging reconstruction method using a merging reconstruction process and maximum likelihood estimation (MLE). The conventional 3D photon-counting reconstruction method extracts photons from elemental images using a Poisson random process and estimates the scene using statistical methods such as MLE. However, it can reduce the photon levels because of an average overlapping calculation. Thus, it may not visualize 3D objects in severely low light environments. In addition, it may not generate high-quality reconstructed 3D images when the number of elemental images is insufficient. To solve these problems, we propose a new 3D photon-counting merging reconstruction method using MLE. It can visualize 3D objects without photon-level loss through a proposed overlapping calculation during the reconstruction process. We confirmed the image quality of our proposed method by performing optical experiments.

• The Journal of Korea Robotics Society
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• v.14 no.1
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• pp.8-13
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• 2019

Visual place recognition is widely researched area in robotics, as it is one of the elemental requirements for autonomous navigation, simultaneous localization and mapping for mobile robots. However, place recognition in changing environment is a challenging problem since a same place look different according to the time, weather, and seasons. This paper presents a feature extraction method using a deep convolutional auto-encoder to recognize places under severe appearance changes. Given database and query image sequences from different environments, the convolutional auto-encoder is trained to predict the images of the desired environment. The training process is performed by minimizing the loss function between the predicted image and the desired image. After finishing the training process, the encoding part of the structure transforms an input image to a low dimensional latent representation, and it can be used as a condition-invariant feature for recognizing places in changing environment. Experiments were conducted to prove the effective of the proposed method, and the results showed that our method outperformed than existing methods.

• The Journal of the Korea Contents Association
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• v.10 no.6
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• pp.1-9
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• 2010

Recently, 3D stereoscopic display such as 3D stereoscopic cinemas and 3D stereoscopic TV is getting a lot of interest. In general, a stereo image can be used in 3D stereoscopic display. In other hands, for 3D auto stereoscopic display, the elemental images should be generated through visualization from every camera in a lens array. Since a lens array consists of several cameras, it takes a lot of time to generate the elemental images with respect to 3D virtual space, specially, if a large bio-medical volume data is in the 3D virtual space, it will take more time. In order to improve the problem, in this paper, we construct an octree for a given bio-medical volume data and then propose a method to generate the elemental images through efficient rendering of the Octree data using GPU. Experimental results show that the proposed method can obtain more improvement comparable than conventional one, but the development of more efficient method is required.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.12
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• pp.2762-2770
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• 2012

In this paper, we propose a novel approach to effectively compress the sub-images transformed from the picked-up elemental images in integral imaging, in which motion vectors of the object in each sub-image are fast and accurately estimated and compensated by combined use of MSE(mean square error)-based TSS(tree-step search) and FS(full search) schemes. This is, the possible object areas in each sub-image are searched by using the fast TSS algorithm in advance, then the these selected object areas are fully searched with the accurate FS algorithm. Furthermore, the sub-images in which all object's motion vectors are compensated, are transformed into the residual images by using the difference image method and finally compressed with the MPEG-4 algorithm. Experimental results reveal that the proposed method shows 214% improvement in the compression time per each image frame compared to that of the conventional method while keeping the same compression ratio with the conventional method. These successful results confirm the feasibility of the proposed method in the practical application.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.8
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• pp.1659-1665
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• 2009

In this paper, we propose an implementation method of 3D image correlator using integral imaging technique. In the proposed method, elemental images of the reference and signal 3D objects are recorded by lenslet arrays and then reference and signal output plane images with high resolution are optically reconstructed on the output plane by displaying these elemental images into a display panel. Through cross-correlations between the reconstructed reference and the single plane images, 3D object recognition is performed. The proposed method can provide a precise 3D object recognition by using the high-resolution output plane images compared with the previous methods and implement all-optical structure for real-time 3D object recognition system. To show the feasibility of the proposed method, optical experiments are carried out and the results are presented.

• Journal of Information Display
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• v.10 no.1
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• pp.1-5
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• 2009

A novel computational integral imaging technique with enhanced depth sensitivity is proposed. For each lateral position at a given depth plane, the dissimilarity between corresponding pixels of the elemental images is measured and used as a suppressing factor for that position. The intensity values are aggregated to determine the correct depth plane of each plane object. The experimental and simulation results show that the reconstructed depth image on the incorrect depth plane is effectively suppressed, and that the depth image on the correct depth plane is reconstructed clearly without any noise. The correct depth plane is also exactly determined.