• Journal of Korea Multimedia Society
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• v.6 no.1
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• pp.40-47
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• 2003

In this paper, we propose a new method for reducing the computational overhead of fine-to-coarse multi-resolution motion estimation (MRME) at the finest resolution level by searching for the region to consider motion vectors of the coarsest resolution subband. At this time, if half-pel accuracy motion estimation (HPAME) is used in the baseband where influence a lot of effect to the reconstructed image, we can have the motion vector exactly But, this method causes to higher computational overhead. So we suggest the method to the computational overhead by using selective interpolation. Experimental results show that the proposed algorithm gives better results than the traditional algorithms from image quality.

• Proceedings of the Korea Information Processing Society Conference
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• 2000.10b
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• pp.903-906
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• 2000

웨이브렛 영역에서 벡터 양자화(vector quantization)를 수행하여 생성된 VQ 인덱스들을 엔트로피 부호화(entropy coding)하면 영상의 코딩 효율을 높일 수 있다. 본 논문에서는 벡터 양자화 이전에 VQ 인덱스들의 중복성을 높이기 위해 다중해상도 코드북의 코드 워드들을 에너지 크기 순으로 재정렬하는 기법을 제안한다. 코드 워드들의 평균과 편차를 이용한 재정렬 방법과 제안된 기법을 벡터 양자화 후 생성되는 VQ 인덱스에 DPCM/Huffman 기법을 적용하여 각각에 대한 코딩 효율을 비교한다.

• Korean Journal of Remote Sensing
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• v.27 no.4
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• pp.435-444
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• 2011

The land cover map derived from spectral features of high resolution optical images has low spectral resolution and heterogeneity in the same land cover class. For this reason, despite the same land cover class, the land cover can be classified into various land cover classes especially in vegetation area. In order to overcome these problems, detailed vegetation classification is applied to optical satellite image and SAR(Synthetic Aperture Radar) integrated data in vegetation area which is the result of pre-classification from optical image. The pre-classification and vegetation classification were performed with MLC(Maximum Likelihood Classification) method. The hierarchical land cover classification was proposed from fusion of detailed vegetation classes and non-vegetation classes of pre-classification. We can verify the facts that the proposed method has higher accuracy than not only general SAR data and GLCM(Gray Level Co-occurrence Matrix) texture integrated methods but also hierarchical GLCM integrated method. Especially the proposed method has high accuracy with respect to both vegetation and non-vegetation classification.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1771-1777
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• 2023

With the advancements of deep learning, many semantic segmentation-based methods for land cover classification have been proposed. However, existing deep learning-based models only use image information and cannot guarantee spatiotemporal consistency. In this study, we propose a land cover classification model using geographical coordinates. First, the coordinate features are extracted through the Coordinate Hash Encoder, which is an extension of the Multi-resolution Hash Encoder, an implicit neural representation technique, to the longitude-latitude coordinate system. Next, we propose an architecture that combines the extracted coordinate features with different levels of U-net decoder. Experimental results show that the proposed method improves the mean intersection over union by about 32% and improves the spatiotemporal consistency.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.33-42
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• 2022

In this paper, we proposed a deep learning model based on multi-scale dilated convolution feature fusion for the segmentation of black ice region in road image to send black ice warning to drivers in real time. In the proposed multi-scale dilated convolution feature fusion network, different dilated ratio convolutions are connected in parallel in the encoder blocks, and different dilated ratios are used in different resolution feature maps, and multi-layer feature information are fused together. The multi-scale dilated convolution feature fusion improves the performance by diversifying and expending the receptive field of the network and by preserving detailed space information and enhancing the effectiveness of diated convolutions. The performance of the proposed network model was gradually improved with the increase of the number of dilated convolution branch. The mIoU value of the proposed method is 96.46%, which was higher than the existing networks such as U-Net, FCN, PSPNet, ENet, LinkNet. The parameter was 1,858K, which was 6 times smaller than the existing LinkNet model. From the experimental results of Jetson Nano, the FPS of the proposed method was 3.63, which can realize segmentation of black ice field in real time.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.253-260
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• 2012

Target detection is very difficult with complex clutters in IRST(Infrared Search and Track) system for a long distance target. Especially sea-clutter and ocean-surface with non-uniform temperature distribution make it difficult to detect incoming targets in images obtained in sea environment. In this paper, we propose a novel method based on morphological method for estimation of ocean surface with non-uniform temperature flow. In order to estimate the exact ocean surface temperature flow, we divided it into upper and lower bound flow. And after estimating it, the final ocean surface temperature flow is derived by a mean value of the estimated results. Also, we apply the multi-weighted technique with a variety of sizes of structure elements to overcome sub-sampling effect by using morphology method. Experimental results for ocean surface images acquired from many different environments are compared with results of existing method to verify the performance of the proposed methods.

• 한국공간정보시스템학회:학술대회논문집
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• 2004.12a
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• pp.139-143
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• 2004

최근 고 해상도 위성영상정보의 민간 활용에 대한 수요가 증가하면서 기존의 공간 정보를 다루는 여러 응용분야에서 이에 관련된 많은 연구를 하고 있다. 도시교통 환경 분석을 위하여 위성영상정보를 처리하는 과정에 있어서 도로, 건물, 기타 선 구조와 같은 지형지물을 분석하는 과정은 사용자에 따라 주관적일 수 있다. 이러한 배경에서 수학적 그레이 레벨 모폴로지는 하나의 효과적인 접근으로 간주된다. 본 연구에서 지형지물 추출을 위해 윈도우 운영체제에서 실행되는 실질적인 응용 프로그램을 구현하였다. 이 프로그램에서 주요한 지형지물은 그레이 레벨 영상을 이용하여 개방(opening), 폐쇄(closing), 침식(erosion), 팽창(dilation)의 순차적 처리를 통하여 자동적으로 추출된다. 결과적으로, GDPA, 허프 변환 또는 다른 알고리듬들과 비교시 하나의 이점이 된다. 모폴로지 처리와 같이 본 프로그램은 그레이 레벨 값의 범위에 기반하여 지형지물을 추출을 위한 density slicing 기능 또는 주어진 경계치 보다 작은 화소 군집을 제거하는 처리인 'sieve filtering'을 제공한다. 이러한 기능들은 형태학적으로 처리된 결과를 증대하고 지형지물 종류들을 분리하는데 유용하다. 또한 배경의 제거, 잡음 탐지, 도시 환경 원격 탐사에서의 지형지물 특성화에 기여한다. 본 프로그램을 이용하는데 있어서 IKONOS 위성영상을 이용하여 시험 구현하였다. 결과, 다중 경계치 또는 steve filtering을 이용한 그레이 레벨 모폴로지 처리는 복잡한 지형지물과 많은 데이터로 구성된 고해상도 영상 내의 주어진 대상에서 자동적인 처리와 사용자 정의 sieve filtering으로 인한 효과적인 지형지물 추출 방법으로 간주 된다. 시안을 작성 표준화를 위한 첫 단계 시도를 소개하였다.분석 결과는 문장, 그림 및 도표, 장 끝의 질문, 학생의 학습 활동 수 등이 $0.4{\sim}1.5$ 사이의 값으로 학생 참여를 적절히 유도하는 발견 지향적 인 것으로 조사되었다. 그러나 장의 요약은 본문 내용을 반복하는 내용으로 구성되었다. 이와 같이 공통과학 과목은 새로운 현대 사회에 부응하는 교과 목표와 체계를 지향하고 있지만 아직도 통합과학으로서의 내용과 체계를 완전히 갖추고 있지 못할 뿐만 아니라 현재 사용되고 있는 7종의 교과서가 교육 목표를 충분히 반영하지 못하고 있다. 따라서 교사의 역할이 더욱더 중요하게 되었다.괴리가 작아진다. 이 결과에 따르면 위탁증거금의 징수는 그 제도의 취지에 부합되고 있다. 다만 제도운용상의 이유이거나 혹은 우리나라 주식시장의 투자자들이 비합리적인 투자형태를 보임에 따라 그 정책적 효과는 때로 역기능적인 결과로 초래하였다. 그럼에도 불구하고 이 연구결과를 통하여 최소한 주식시장(株式市場)에서 위탁증거금제도는 그 제도적 의의가 여전히 있다는 사실이 확인되었다. 또한 우리나라 주식시장에서 통상 과열투기 행위가 빈번히 일어나 주식시장을 교란시킴으로써 건전한 투자풍토조성에 저해된다는 저간의 우려가 매우 커왔으나 표본 기간동안에 대하여 실증분석을 한 결과 주식시장 전체적으로 볼 때 주가변동율(株價變動率), 특히 초과주가변동율(超過株價變動率)에 미치는 영향이 그다지 심각한 정도는 아니었으며 오히려 우리나라의 주식시장은 미국시장에 비해 주가가 비교적 안정적인 수준을 유지해 왔다고 볼 수 있다.36.4%)와 외식을 선호(29.1%)${\lrcorner}$ 하기 때문에 패스트푸드를 이용하게 된 것으로 응답 하였으며,

• Geophysics and Geophysical Exploration
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• v.11 no.1
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• pp.68-77
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• 2008

We have developed an inversion algorithm for loop-loop electromagnetic (EM) data, based on the localised non-linear or extended Born approximation to the solution of the 2.5D integral equation describing an EM scattering problem. Source and receiver configuration may be horizontal co-planar (HCP) or vertical co-planar (VCP). Both multi-frequency and multi-separation data can be incorporated. Our inversion code runs on a PC platform without heavy computational load. For the sake of stable and high-resolution performance of the inversion, we implemented an algorithm determining an optimum spatially varying Lagrangian multiplier as a function of sensitivity distribution, through parameter resolution matrix and Backus-Gilbert spread function analysis. Considering that the different source-receiver orientation characteristics cause inconsistent sensitivities to the resistivity structure in simultaneous inversion of HCP and VCP data, which affects the stability and resolution of the inversion result, we adapted a weighting scheme based on the variances of misfits between the measured and calculated datasets. The accuracy of the modelling code that we have developed has been proven over the frequency, conductivity, and geometric ranges typically used in a loop-loop EM system through comparison with 2.5D finite-element modelling results. We first applied the inversion to synthetic data, from a model with resistive as well as conductive inhomogeneities embedded in a homogeneous half-space, to validate its performance. Applying the inversion to field data and comparing the result with that of dc resistivity data, we conclude that the newly developed algorithm provides a reasonable image of the subsurface.

• Korean Journal of Remote Sensing
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• v.38 no.3
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• pp.237-250
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• 2022

In this study, we propose a method to estimate the biomass of invertebrate grazers from the videos with underwater drones by using a multi-object tracking model based on deep learning. In order to detect invertebrate grazers by classes, we used YOLOv5 (You Only Look Once version 5). For biomass estimation we used DeepSORT (Deep Simple Online and real-time tracking). The performance of each model was evaluated on a workstation with a GPU accelerator. YOLOv5 averaged 0.9 or more mean Average Precision (mAP), and we confirmed it shows about 59 fps at 4 k resolution when using YOLOv5s model and DeepSORT algorithm. Applying the proposed method in the field, there was a tendency to be overestimated by about 28%, but it was confirmed that the level of error was low compared to the biomass estimation using object detection model only. A follow-up study is needed to improve the accuracy for the cases where frame images go out of focus continuously or underwater drones turn rapidly. However,should these issues be improved, it can be utilized in the production of decision support data in the field of invertebrate grazers control and monitoring in the future.

• Journal of Broadcast Engineering
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• v.28 no.1
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• pp.31-41
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• 2023

Directly converting the focal depth and image size of computer-generated-hologram (CGH), which is obtained by calculating the interference pattern of light from the 3D image, is known to be quite difficult because of the less similarity between the CGH and the original image. This paper proposes a method for separately converting the each of focal length of the given CGH, which is composed of multi-depth images. Firstly, the proposed technique converts the 3D image reproduced from the CGH into a Light-Field (LF) image composed of a set of 2D images observed from various angles, and the positions of the moving objects for each observed views are checked using an object detection algorithm YOLOv5 (You-Only-Look-Once-version-5). After that, by adjusting the positions of objects, the depth-transformed LF image and CGH are generated. Numerical simulations and experimental results show that the proposed technique can change the focal length within a range of about 3 cm without significant loss of the image quality when applied to the image which have original depth of 10 cm, with a spatial light modulator which has a pixel size of 3.6 ㎛ and a resolution of 3840⨯2160.