• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.11a
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• pp.10-13
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• 2022

최근 인공지능 기술을 바탕으로 지능형 분석을 수행하는 기계를 위한 비디오 부호화 기술의 필요성이 요구되면서, MPEG 에서는 VCM(Video Coding for Machines) 표준화를 시작하였다. VCM 에서는 기계를 위한 비디오/이미지 압축 또는 비디오/이미지 특징 압축을 위한 다양한 방법이 제시되고 있다. 본 논문에서는 객체추적(object tracking)을 위한 머신비전(machine vision) 네트워크에서 추출되는 다중스케일(multi-scale) 특징의 효율적인 압축 기법을 제시한다. 제안기법은 다중스케일 특징을 단일스케일(single-scale) 특징으로 차원을 축소하여 형성된 특징 시퀀스를 최신 비디오 코덱 표준인 VVC(Versatile Video Coding)를 사용하여 압축한다. 제안기법은 VCM 에서 제시하는 기준(anchor) 대비 89.65%의 BD-rate 부호화 성능향상을 보인다.

• Journal of the Korean Geographical Society
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• v.49 no.2
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• pp.139-159
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• 2014

This study problematizes the dominance of developmental state theory and its negative influences in the field of Korean studies, in particular, dealing with the industrialization during the developmental era, 1960s~70s. As is generally known, the theory has been in a position of unchallenged authority on the industrialization experience of East Asian countries, including South Korea. However, at the same time, it has also misled us into overlooking strategic relations that had articulated the state forms at multiple scales. This study aims to reconstruct the historical contexts by the theorizing prompted by recent work on state space. I shed light on the multiscalar strategic relations that had shaped the Ulsan refinery plant as a representative state space of the South Korean industrialization during two decades after liberation. Specifically, the study illustrates the features and roles of Cold War networks and multiscalar agnets such as Nam Goong-Yeon. By identifying the plant as a result of sequential articulations between Ulsan and other scales, this study concludes by suggesting to reframing the strategic relational spaces, beyond the view of methodological nationalism, in the perspective of multiscalar approach.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.59-63
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• 2001

기존 스케일러빌러티(scalability) 동영상 부호화 알고리듬 의 문제점을 개선하기 위하여 본 논문에서는 다중 레퍼런스 프레임 방법을 기저 계층(base layer)과 확장 계층(enhancement layer)의 움직임 보상시 사용하여, 부호화 효율 및 드리프트 현상을 현저히 감소시키는 효과를 가져온다. 전산 모의 실험을 통하여 제안 알고리듬은 기존의 H.263+ 알고리듬에 비해 스케일러빌러티 모드를 작동하였을 때와 작동하지 않았을 경우 모두에 대해 더 나은 부호화 효율을 보여 주고, 이전 레퍼런스 확장 계층이 네트웍 사정으로 전송되지 않았을 경우에도 제안하는 알고리듬은 거의 화질의 손실이 없이 복원할 수 있음을 보여준다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.263-265
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• 2020

본 논문에서는 다중 스케일로 구성된 뉴럴 네트워크를 이용하여 영상 내 모아레 무늬을 제거하는 기법을 제안한다. 제안하는 기법은 영상 피라미드를 생성하여 모아레 무늬를 구성하는 다양한 주파수 범위의 정보를 제거한다. 각 branch는 Multi-scale Feature Block (MFB)과 Tone-Mapping Block (TMB)으로 구성하여 효과적으로 모아레 현상을 제거하고 저하된 색상 저하를 복원한다. 컴퓨터 모의실험을 통해 제안하는 기법이 기존 기법에 비해서 높은 모아레 제거 성능을 보이는 것을 확인한다.

• 기계와재료
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• v.16 no.4 s.62
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• pp.110-129
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• 2004

• Journal of the Korean association of regional geographers
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• v.21 no.3
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• pp.618-630
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• 2015

This study is to try to find citizenship needed in the age of glocalization and its implication for geography education. With formation of nation-state after modern, the rights and duties are applied to members of a state in a given territory. But Although states grant de jure citizenship, identity as a citizen is increasingly seen as something that is gained beyond and below the state. Citizenship might be conceived as relational rather than absolute, something that is constituted by its connections or network with different people and places rather than something defined by the borders of the nation-state. New space of citizenship has multiple dimension, and is fluid, mobile, multidimensional, transnational, negotiative. Citizenship operates in an increasingly complex web of overlapping spaces, and is reconceptualized as multiple citizenship based on multiscale. Citizenship should now be thought of as multi-level, reflecting individuals simultaneous membership of political communities at a variety of spatial scales and perhaps of non-territorial social groups. Thus, Citizenship education through geography should focus more on interconnected and layered multiple citizenship than bounded national citizenship.

• Korean Journal of Remote Sensing
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• v.34 no.6_3
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• pp.1351-1367
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• 2018

Image registration is an essential process for image fusion, change detection and time series analysis using multi-sensor images. For this purpose, we need to detect accurately the difference of scale and rotation between the multi-sensor images with difference spatial resolution. In this paper, we propose a new feature matching method using variable circular template for image registration between multi-resolution images. The proposed method creates a circular template at the center of a feature point in a coarse scale image and also a variable circular template in a fine scale image, respectively. After changing the scale of the variable circular template, we rotate the variable circular template by each predefined angle and compute the mutual information between the two circular templates and then find the scale, the angle of rotation and the center location of the variable circular template, respectively, in fine scale image when the mutual information between the two circular templates is maximum. The proposed method was tested using Kompsat-2, Kompsat-3 and Kompsat-3A images with different spatial resolution. The experimental results showed that the error of scale factor, the error of rotation angle and the localization error of the control point were less than 0.004, $0.3^{\circ}$ and one pixel, respectively.

• Journal of the Economic Geographical Society of Korea
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• v.17 no.1
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• pp.1-27
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• 2014

This paper aims at exploring the multi-scalar processes through which the Gumi Industrial Complex was developed in the late 1960s and the early 1970s. Existing studies, influenced by the "Developmental State Thesis", tend to see the industrialization processes of South Korea either by focusing on the socio-politico-economic processes at the national scale or in terms of the plan rationality of the national bureaucrats. This paper, however, denies this perspective on the basis of the strategic relational approach to the state and the multi-scalar perspective. In particular, it argues that the state actions for national industrialization have been the outcome of complex interactions, conflicts and negotiations among social forces, acting in and through the state, and at diverse geographical scales. This paper attempts to empirically prove this argument on the basis of a case study on the construction processes of Gumi Industrial Complex. The development of Gumi Industrial Complex cannot be solely explained in terms of either the plan rationality of the national bureaucrats or the political motivation related to the fact that Gumi was the hometown of President Park Jung-Hee. This paper argues that the development of Gumi Industrial Complex was heavily influenced by the role of the following actors; place-dependent local actors in Gumi and the multi-scalar agents, such as the Korean-Japanese businessmen and the national parliament members elected in the Gumi electoral district.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.767-776
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• 2020

GcGAN is a deep learning model to translate styles between images under geometric consistency constraint. However, GcGAN has a disadvantage that it does not properly maintain detailed content of an image, since it preserves the content of the image through limited geometric transformation such as rotation or flip. Therefore, in this study, we propose a new image-to-image translation method, MSGcGAN(Multi-Scale GcGAN), which improves this disadvantage. MSGcGAN, an extended model of GcGAN, performs style translation between images in a direction to reduce semantic distortion of images and maintain detailed content by learning multi-scale images simultaneously and extracting scale-invariant features. The experimental results showed that MSGcGAN was better than GcGAN in both quantitative and qualitative aspects, and it translated the style more naturally while maintaining the overall content of the image.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.1
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• pp.40-48
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• 2007

This paper presents a novel texture segmentation method using multilayer perceptron (MLP) networks and Markov random fields in multiscale Bayesian framework. Multiscale wavelet coefficients are used as input for the neural networks. The output of the neural network is modeled as a posterior probability. Texture classification at each scale is performed by the posterior probabilities from MLP networks and MAP (maximum a posterior) classification. Then, in order to obtain the more improved segmentation result at the finest scale, our proposed method fuses the multiscale MAP classifications sequentially from coarse to fine scales. This process is done by computing the MAP classification given the classification at one scale and a priori knowledge regarding contextual information which is extracted from the adjacent coarser scale classification. In this fusion process, the MRF (Markov random field) prior distribution and Gibbs sampler are used, where the MRF model serves as the smoothness constraint and the Gibbs sampler acts as the MAP classifier. The proposed segmentation method shows better performance than texture segmentation using the HMT (Hidden Markov trees) model and HMTseg.