• Journal of the KSME
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• v.53 no.5
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• pp.51-55
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• 2013

이 글에서는 단분자해석방법을 이용하는 3세대 DNA 시퀀싱에 나타나는 다중물리-다중스케일 유동에 대한 해석방법들을 소개하고, 관련된 예제로 탄소나노튜브을 통한 DNA를 포함한 전해질 유동의 최신결과들을 소개하고자 한다.

• 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.

• The Journal of the Korea Contents Association
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• v.7 no.1
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• pp.48-57
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• 2007

Eye localization is necessary for face recognition and related application areas. Most of eye localization algorithms reported thus far still need to be improved about precision and computational time for successful applications. In this paper, we propose an improved eye localization method based on multi-scale Gator feature vector models. The proposed method first tries to locate eyes in the downscaled face image by utilizing Gabor Jet similarity between Gabor feature vector at an initial eye coordinates and the eye model bunch of the corresponding scale. The proposed method finally locates eyes in the original input face image after it processes in the same way recursively in each scaled face image by using the eye coordinates localized in the downscaled image as initial eye coordinates. Experiments verify that our proposed method improves the precision rate without causing much computational overhead compared with other eye localization methods reported in the previous researches.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.1
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• pp.1-6
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• 2018

In this paper, we propose the method to eliminate repetitive processes for key-point detection on multi-scale image space. The proposed method detects key-points from the original image, and select a good key-points using the cluster filters, and create the key-point clusters. And it select reference objects by using direction angles of the key-point clusters, predict the scale of the original image by using the distributed distance ratio. It transform the scale of the reference image, and apply the detection of key-points to the transformed reference image. In the results of the experiment, the proposed method can be found to improve the key-points detection time by 75 % and 71 % compared to SIFT method and scaled ORB method using the multi-scale images.

• Prospectives of Industrial Chemistry
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• v.24 no.3
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• pp.28-41
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• 2021

고분자 시스템의 경우 매우 상이한 시간 및 길이 스케일(time and length scale)에 연관된 복잡한 내부 구조(internal structure)를 가지고 있기 때문에 전통적인 실험 방법만으로는 체계적이고 종합적인 연구가 쉽지 않다. 최근 다양한 시간 및 길이 스케일에 연관된 연구를 진행할 수 있는 다중 스케일 전산 모사(multiscale computer simulation) 방법은 이러한 고분자 시스템 연구에 있어서 새로운 대안으로 각광받고 있다. 본 논문에서는 최근 급격한 발전을 이룬 고분자 용액(polymeric liquid) 시스템에 대한 평형(equilibrium) 및 비평형(nonequilibrium) 전산 모사(computer simulation) 방법들에 관해 소개하고 이를 통합적으로 해석할 수 있는 다중 스케일 전산 모사 방법에 대해 여러 가지 사례를 들어 살펴보았다.

• The Journal of the Korea Contents Association
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• v.14 no.3
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• pp.11-21
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• 2014

Multi-scale image segmentation is important in many applications such as image stylization and medical diagnosis. This paper proposes a novel segmentation algorithm based on MSER(maximally stable extremal region) which captures multi-scale structure and is stable and efficient. The algorithm collects MSERs and then partitions the image plane by redrawing MSERs in specific order. To denoise and smooth the region boundaries, hierarchical morphological operations are developed. To illustrate effectiveness of the algorithm's multi-scale structure, effects of various types of LOD control are shown for image stylization. The proposed technique achieves this without time-consuming multi-level Gaussian smoothing. The comparisons of segmentation quality and timing efficiency with mean shift-based Edison system are presented.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.301-307
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• 2024

This study introduces a novel cascade fusion architecture aimed at enhancing thermal images across various scale conditions. The processing of thermal images at multiple scales has been challenging due to the limitations of existing methods that are designed for specific scales. To overcome these limitations, this paper proposes a unified framework that utilizes cascade feature fusion to effectively learn multi-scale representations. Confidence maps from different image scales are fused in a cascaded manner, enabling scale-invariant learning. The architecture comprises end-to-end trained convolutional neural networks to enhance image quality by reinforcing mutual scale dependencies. Experimental results indicate that the proposed technique outperforms existing methods in multi-scale thermal image enhancement. Performance evaluation results are provided, demonstrating consistent improvements in image quality metrics. The cascade fusion design facilitates robust generalization across scales and efficient learning of cross-scale representations.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.06a
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• pp.1-4
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• 2019

최근에 딥러닝 기술을 적용한 보행자 검출 연구가 활발히 진행되고 있다. 연구자들은 딥러닝 네트워크를 이용하여 보행자 오검출율을 낮추는 방법에 대해 지속적으로 연구하여 성능을 꾸준히 상승시켰다. 그러나 대부분의 연구는 다중 스케일 보행자가 분포되는 저해상도 영상에서 보행자를 제대로 검출하지 못하는 어려움이 존재한다. 따라서 본 연구에서는 기존의 Faster R-CNN구조를 기반으로 하여 새로운 다중 특징 융합 레이어와 다중 스케일 앵커 박스를 적용하여 보행자 오검출율을 줄이는 MS-FRCNN(Multi-scaleFaster R-CNN)구조를 제안한다. 제안된 방식의 성능 검증을 위해 Caltech 데이터세트를 이용하여 실험한 결과, 제안된 MS-FRCNN방식이 기존의 다른 보행자 검출 방식보다 다중 스케일 보행자 검출에서 medium 조건하에 5%, all 조건하에 3.9% 나아짐을 알 수 있었다.

• Journal of Korea Multimedia Society
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• v.7 no.8
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• pp.1046-1057
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• 2004

An image enhancement method using both homomorphic transformation and multiscale decomposition is proposed. The original image is first transformed to homomorphic domain by taking the logarithm, is then separated to multiscales. These multiscales are combined with weighting. The combined signal is exponentially transformed back into intensity domain. In homomorphic domain, the magnitude control of low frequency component make change the dynamic range, and the magnitude control of the other frequency components contribute to enhancement of the contrast. The "${\AA}$ trous" algorithm, which has a simple and efficient scheme, is used for multiscale decomposition. The performance of proposed method is verified by simulation.

• 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.