• Journal of Electrical Engineering and Technology
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• v.9 no.5
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• pp.1762-1773
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• 2014

In this paper, a distributed image coding scheme for multi-view video through an efficient generation of side information is proposed. A distributed video coding technique corrects the errors in the side information, which is generated with the original image, by using the channel coding technique at the decoder. Therefore, the more correct the generated side information is, the better the performance of distributed video coding. The proposed technique is to apply the distributed video coding schemes to the image coding for multi-view video. It generates side information by selectively and efficiently using both 3-dimensional warping based on the depth map with spatially adjacent frames and motion-compensated temporal interpolation with temporally adjacent frames. In this scheme the difference between the adjacent frames, the sizes of the motion vectors for the adjacent blocks, and the edge information are used as the selection criteria. From the experiments, it was observed that the quality of the side information generated by the proposed technique was improved by the average peak signal-to-noise ratio of 0.97dB than the one by motion-compensated temporal interpolation or 3-dimensional warping. The result from analyzing the rate-distortion curves revealed that the proposed scheme could reduce the bit-rate by 8.01% on average at the same peak signal-to-noise ratio value, compared to previous work.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06b
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• pp.367-369
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• 2006

사람은 일반적으로 깊이를 지각하는데 두 눈으로 들어오는 영상의 시차(binocular disparity)를 이용하며 6-15m 정도의 범위 내에서는 매우 뛰어난 깊이 판별 능력을 보인다. 그러나 사람은 하나의 눈만으로도 깊이를 지각하는데 별 어려움을 느끼지 못한다. 이것은 공간의 깊이 지각 단서로 양안단서안이 아니라 다양한 단안단서(monocular Cue)들이 함께 사용되기 때문이다. 본 논문에서는 사람이 공간 깊이정보 파악에 사용하는 것으로 알려진 여러 단안 단서들 중 영상의 채도(saturation) 정보와 디포커스(defocus) 정보, 기하학적 깊이(geometric depth) 정보에 기반을 둔 단안 영상에서의 상대적 깊이지도의 생성방법을 제안한다.

• ETRI Journal
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• v.35 no.6
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• pp.1011-1020
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• 2013

In this paper, we present a method for obtaining a high-quality 3D depth. The advantages of active pattern projection and passive stereo matching are combined and a system is established. A diffractive optical element (DOE) is developed to project the active pattern. Cross guidance (CG) and auto guidance (AG) are proposed to perform the passive stereo matching in a stereo image in which a DOE pattern is projected. When obtaining the image, the CG emits a DOE pattern periodically and consecutively receives the original and pattern images. In addition, stereo matching is performed using these images. The AG projects the DOE pattern continuously. It conducts cost aggregation, and the image is restored through the process of removing the pattern from the pattern image. The ground truth is generated to estimate the optimal parameter among various stereo matching algorithms. Using the ground truth, the optimal parameter is estimated and the cost computation and aggregation algorithm are selected. The depth is calculated and bad-pixel errors make up 4.45% of the non-occlusion area.

• Journal of information and communication convergence engineering
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• v.11 no.4
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• pp.282-287
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• 2013

A real-time digital holographic display is the core technology for the next-generation 3DTV. Holographic display requires a considerably large amount of calculation. If generating a large number of digital holograms is intended, the amount of calculation and the time required increase exponentially. This is a significant obstacle in a real-time hologram service. This paper proposes an algorithm that increases the speed of generating a Fresnel hologram by using a recursive addition operation covering the entire coordinate array of a digital hologram. The 3D object designed to calculate the digital hologram uses a depth-map image produced by computer graphics. The proposed algorithm is a technique that performs the computer-generated holography (CGH) operation with only recursive addition of all of the hologram's coordinates by analyzing the regularity between the 3D object and the digital hologram coordinates. The experimental results show that the proposed algorithm increases the operation speed by 70% over the technique using the conventional CGH equation and by more than 30% over the previously proposed recursive technique.

• Journal of the Korea Computer Graphics Society
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• v.20 no.2
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• pp.1-11
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• 2014

The quality of 2D-to-3D conversion depends on the accuracy of the assigned depth to scene objects. Manual depth painting for given objects is labor intensive as each frame is painted. Specifically, a human is one of the most challenging objects for a high-quality conversion, as a human body is an articulated figure and has many degrees of freedom (DOF). In addition, various styles of clothes, accessories, and hair create a very complex silhouette around the 2D human object. We propose an efficient method to estimate visually pleasing depths of a human at every frame in a monocular video. First, a 3D template model is matched to a person in a monocular video with a small number of specified user correspondences. Our pose estimation with sequential joint angular constraints reproduces a various range of human motions (i.e., spine bending) by allowing the utilization of a fully skinned 3D model with a large number of joints and DOFs. The initial depth of the 2D object in the video is assigned from the matched results, and then propagated toward areas where the depth is missing to produce a complete depth map. For the effective handling of the complex silhouettes and appearances, we introduce a partial depth propagation method based on color segmentation to ensure the detail of the results. We compared the result and depth maps painted by experienced artists. The comparison shows that our method produces viable depth maps of humans in monocular videos efficiently.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.12a
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• pp.13-16
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• 2000

In the recent mobile communication systems, the performance of Turbo Code using the error correction coding depends on the interleaver influencing the free distance determination and the recursive decoding algorithms that is executed in the turbo decoder. However, performance depends on the interleaver depth that need great many delay over the reception process. Moreover, Turbo Code has been known as the robust coding methods with the confidence over the fading channel. The International Telecommunication Union(ITU) has recently adopted as the standardization of the channel coding over the third generation mobile communications the same as IMT-2000. Therefore, in this paper, we proposed of that has the better performance than existing Turbo Decoder that has the parallel concatenated four-step structure using MAP algorithm. In the real-time voice and video service over the third generation mobile communications, the performance of the proposed method was analyzed by the reduced decoding delay using the variable decoding method by computer simulation over AWGN and lading channels.

• Journal of IKEEE
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• v.28 no.1
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• pp.65-71
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• 2024

This paper proposes a neural network that can reduce the number of parameters while reducing matching errors in occluded regions to increase the accuracy of depth maps in stereo matching. Stereo matching-based object recognition is utilized in many fields to more accurately recognize situations using images. When there are many objects in a complex image, an occluded area is generated due to overlap between objects and occlusion by background, thereby lowering the accuracy of the depth map. To solve this problem, existing research methods that create context information and combine it with the cost volume or RoIselect in the occluded area increase the complexity of neural networks, making it difficult to learn and expensive to implement. In this paper, we create a depthwise seperable neural network that enhances regional feature extraction before cost volume generation, reducing the number of parameters and proposing a neural network that is robust to occlusion errors. Compared to PSMNet, the proposed neural network reduced the number of parameters by 30%, improving 5.3% in color error and 3.6% in test loss.

• ETRI Journal
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• v.29 no.3
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• pp.353-362
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• 2007

In this paper, we propose a novel unsupervised video object extraction algorithm for individual images or image sequences with low depth of field (DOF). Low DOF is a popular photographic technique which enables the representation of the photographer's intention by giving a clear focus only on an object of interest (OOI). We first describe a fast and efficient scheme for extracting OOIs from individual low-DOF images and then extend it to deal with image sequences with low DOF in the next part. The basic algorithm unfolds into three modules. In the first module, a higher-order statistics map, which represents the spatial distribution of the high-frequency components, is obtained from an input low-DOF image. The second module locates the block-based OOI for further processing. Using the block-based OOI, the final OOI is obtained with pixel-level accuracy. We also present an algorithm to extend the extraction scheme to image sequences with low DOF. The proposed system does not require any user assistance to determine the initial OOI. This is possible due to the use of low-DOF images. The experimental results indicate that the proposed algorithm can serve as an effective tool for applications, such as 2D to 3D and photo-realistic video scene generation.

• Genomics & Informatics
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• v.12 no.1
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• pp.2-11
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• 2014

After the initial enthusiasm of the human genome project, it became clear that without additional data pertaining to the epigenome, i.e., how the genome is marked at specific developmental periods, in different tissues, as well as across individuals and species-the promise of the genome sequencing project in understanding biology cannot be fulfilled. This realization prompted several large-scale efforts to map the epigenome, most notably the Encyclopedia of DNA Elements (ENCODE) project. While there is essentially a single genome in an individual, there are hundreds of epigenomes, corresponding to various types of epigenomic marks at different developmental times and in multiple tissue types. Unprecedented advances in next-generation sequencing (NGS) technologies, by virtue of low cost and high speeds that continue to improve at a rate beyond what is anticipated by Moore's law for computer hardware technologies, have revolutionized molecular biology and genetics research, and have in turn prompted innovative ways to reduce the problem of measuring cellular events involving DNA or RNA into a sequencing problem. In this article, we provide a brief overview of the epigenome, the various types of epigenomic data afforded by NGS, and some of the novel discoveries yielded by the epigenomics projects. We also provide ample references for the reader to get in-depth information on these topics.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.4
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• pp.687-694
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• 2022

In this paper, we proposed an autonomous vehicle platform that delivers goods to a designated destination based on the SLAM (Simultaneous Localization and Mapping) map generated indoors by applying the Visual SLAM technology. To generate a SLAM map indoors, a depth camera for SLAM map generation was installed on the top of a small autonomous vehicle platform, and a tracking camera was installed for accurate location estimation in the SLAM map. In addition, a convolutional neural network (CNN) was used to recognize the label of the destination, and the driving algorithm was applied to accurately arrive at the destination. A prototype of an indoor delivery autonomous vehicle was manufactured, and the accuracy of the SLAM map was verified and a destination label recognition experiment was performed through CNN. As a result, the suitability of the autonomous driving vehicle implemented by increasing the label recognition success rate for indoor delivery purposes was verified.