• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2366-2395
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• 2018

Multi-view super-resolution (MVSR) refers to the process of reconstructing a high-resolution (HR) image from a set of low-resolution (LR) images captured from different viewpoints typically by different cameras. These multi-view images are usually obtained by a camera array. In our previous work [1], we super-resolved multi-view LR images via image fusion (IF) and blind deblurring (BD). In this paper, we present a new MVSR method that jointly realizes IF and BD based on an integrated energy function optimization. First, we reformulate the MVSR problem into a multi-channel blind deblurring (MCBD) problem which is easier to be solved than the former. Then the depth map of the desired HR image is calculated. Finally, we solve the MCBD problem, in which the optimization problems with respect to the desired HR image and with respect to the unknown blur are efficiently addressed by the alternating direction method of multipliers (ADMM). Experiments on the Multi-view Image Database of the University of Tsukuba and images captured by our own camera array system demonstrate the effectiveness of the proposed method.

• Journal of Broadcast Engineering
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• v.9 no.3
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• pp.185-195
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• 2004

This paper presents a multi-depth map fusion method for the 3D scene reconstruction. It fuses depth maps obtained from the stereo matching technique and the depth camera. Traditional stereo matching techniques that estimate disparities between two images often produce inaccurate depth map because of occlusion and homogeneous area. Depth map obtained from the depth camera is globally accurate but noisy and provide a limited depth range. In order to get better depth estimates than these two conventional techniques, we propose a depth map fusion method that fuses the multi-depth maps from stereo matching and the depth camera. We first obtain two depth maps generated from the stereo matching of 3-view images. Moreover, a depth map is obtained from the depth camera for the center-view image. After preprocessing each depth map, we select a depth value for each pixel among them. Simulation results showed a few improvements in some background legions by proposed fusion technique.

• Review of Korea Contents Association
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• v.8 no.1
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• pp.12-17
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• 2010

• Journal of Advanced Navigation Technology
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• v.17 no.1
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• pp.98-106
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• 2013

In this paper, we propose a moving object detection and tracking method for multi-view camera environment. Based on the similarity and characteristics of motion vectors and coding block modes extracted from compressed bitstreams, validation of moving blocks, labeling of the validated blocks, and merging of neighboring blobs are performed. To continuously track objects for temporary stop, crossing, and overlapping events, a window based object updating algorithm is proposed for single- and multi-view environments. Object detection and tracking could be performed with an acceptable level of performance without decoding of video bitstreams for normal, temporary stop, crossing, and overlapping cases. The rates of detection and tracking are over 89% and 84% in multi-view environment, respectively. The rates for multi-view environment are improved by 6% and 7% compared to those of single-view environment.

• Journal of Broadcast Engineering
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• v.11 no.4 s.33
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• pp.407-419
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• 2006

In this paper, an adaptive illumination change compensation method is proposed for multi-view video coding. In multi-view video, an illumination change can occur due to physically imperfect camera calibration, each different camera position and direction, and so on. These characteristics can cause a performance decrease in the multi-view video coding that uses an inter-view prediction by referring to the pictures obtained from the neighboring views. By using the proposed method, a compression ratio of the proposed method in the multi-view video coding is increased, and finally $0.1{\sim}0.6dB$ PSNR(Peak Signal-to-Noise Ratio) improvement was obtained compared with the case of not using the proposed method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.7
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• pp.1477-1486
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• 2012

In this paper, we propose Multi-view CGH Making System using method of generation of virtual view-point depth image. We acquire reliable depth image using TOF depth camera. We extract parameters of reference-view cameras. Once the position of camera of virtual view-point is defined, select optimal reference-view cameras considering position of it and distance between it and virtual view-point camera. Setting a reference-view camera whose position is reverse of primary reference-view camera as sub reference-view, we generate depth image of virtual view-point. And we compensate occlusion boundaries of virtual view-point depth image using depth image of sub reference-view. In this step, remaining hole boundaries are compensated with minimum values of neighborhood. And then, we generate final depth image of virtual view-point. Finally, using result of depth image from these steps, we generate CGH. The experimental results show that the proposed algorithm performs much better than conventional algorithms.

• Journal of Broadcast Engineering
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• v.11 no.4 s.33
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• pp.507-520
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• 2006

In this paper, we propose an application of OpenGL functions for novel view synthesis from multi-view images and depth maps. While image based rendering has been meant to generate synthetic images by processing the camera view with a graphic engine, little has been known about how to apply the given images and depth information to the graphic engine and render the scene. This paper presents an efficient way of constructing a 3D space with camera parameters, reconstructing the 3D scene with color and depth images, and synthesizing virtual views in real-time as well as their depth images.

• Journal of Broadcast Engineering
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• v.15 no.6
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• pp.768-782
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• 2010

Because of the different shooting position between multi-view cameras and the imperfect camera calibration, Illumination mismatches of multi-view video can happen. This variation can bring about the performance decrease of multi-view video coding(MVC) algorithm. A histogram matching algorithm can be applied to recompensate these inconsistencies in a prefiltering step. Once all camera frames of a multi-view sequence are adjusted to a predefined reference through the histogram matching, the coding efficiency of MVC is improved. However the histogram distribution can be different not only between neighboring views but also between sequential views on account of movements of camera angle and some objects, especially human. Therefore the histogram matching algorithm which references all frames in chose view is not appropriate for compensating the illumination differences of these sequence. Thus we propose new algorithms both the image classification algorithm which is applied two criteria to improve the correlation between inter-view frames and the histogram matching which references and matches with a group of pictures(GOP) as a unit to advance the correlation between successive frames. Experimental results show that the compression ratio for the proposed algorithm is improved comparing with the conventional algorithms.

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.115-118
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• 2003

This paper describes a system of detecting multi-views faces and estimating their face poses in an omni-directional camera environment for non-intrusive iris recognition. The paper is divided into two parts; First, moving region is identified by using difference-image information. Then this region is analyzed with face-color information to find the face candidate region. Second part is applying PCA (Principal Component Analysis) to detect multi-view faces, to estimate face pose.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.29-37
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• 2014

Since multi-view video exists a number of camera color image and depth image, it has a huge of data. Thus, a new compression technique is indispensable for reducing this data. Recently, the effective compression encoding technique for multi-view video that used in layered depth image concepts is a remarkable. This method uses several view point of depth information and warping function, synthesizes multi-view color and depth image, becomes one data structure. In this paper we use actual distance for solving overlap in layered depth image that reduce required data for reconstructing in color-based transform. In experimental results, we confirmed high compression performance and good quality of reconstructed image.