• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -1
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• pp.172-175
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• 2002

This paper presents perceptual model with a stochastic rnultiresolution characteristic that can be applied with watermark embedding in the biorthogonal wavelet domain. The perceptual model with adaptive watermarking algorithm embed at the texture and edge region for more strongly embedded watermark by the SSQ(successive subband quantization). The watermark embedding is based on the computation of a NVF(noise visibility function) that have local image properties. This method uses non-stationary Gaussian model stationary Generalized Gaussian model because watermark has noise properties. In order to determine the optimal NVF, we consider the watermark as noise. The particularities of embedding in the stationary GG model use shape parameter and variance of each subband regions in multiresolution. To estimate the shape parameter, we use a moment matching method. Non-stationary Gaussian model use the local mean and variance of each subband. The experiment results of simulation were found to be excellent invisibility and robustness. Experiments of such distortion are executed by Stirmark benchmark test.

• Journal of the Optical Society of Korea
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• 제17권2호
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• pp.213-218
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• 2013

Although infrared focal plane array (IRFPA) detectors have been commonly used, non-uniformity correction (NUC) remains an important problem in the infrared imaging realm. Non-uniformity severely degrades image quality and affects radiometric accuracy in infrared imaging applications. Residual non-uniformity (RNU) significantly affects the detection range of infrared surveillance and reconnaissance systems. More effort should be exerted to improve IRFPA uniformity. A novel NUC method that considers the surrounding temperature variation compensation is proposed based on the binary nonlinear non-uniformity theory model. The implementing procedure is described in detail. This approach simultaneously corrects response nonlinearity and compensates for the influence of surrounding temperature shift. Both qualitative evaluation and quantitative test comparison are performed among several correction technologies. The experimental result shows that the residual non-uniformity, which is corrected by the proposed method, is steady at approximately 0.02 percentage points within the target temperature range of 283 K to 373 K. Real-time imaging shows that the proposed method improves image quality better than traditional techniques.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.586-588
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• 2005

The MSC (Multi Spectral Camera) system is a remote sensing payload to obtain high resolution ground image. This system uses lossy image compression method for &Direct mission& that transmit whole image during one contact. But some image degradation occurred especially at high compression ratio. To reduce this degradation, the MSC uses NUC (Non-uniformity Correction) Unit. This unit correct CCD (Charge Coupled Device)'s high-frequency non-uniformity. So high frequency contents of image can be minimized and whole system SNR can be maximized. But NUC has some disadvantage either. It decreases entire system reliability by adding one electronic system. Adding NUC also led to difficulty of electronic design, assembly and testability. In this paper, the comparison is performed between on-orbit non-uniform correction and on ground correction. by evaluating NUC advantage for the point of view of image quality. Using real MSC parameter and proper model, considerable reference point for the system design came to possible.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2007년도 하계종합학술대회 논문집
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• pp.249-250
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• 2007

Background subtraction is a method typically used to segment moving regions in image sequences taken from a static camera by comparing each new frame to a model of the scene background. We present a improved non-parametric background model by null hypothesis. Evaluation shows that this approach achieves very sensitive detection with very low false alarm rates.

• 한국시뮬레이션학회논문지
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• 제29권4호
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• pp.21-31
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• 2020

비협조적 표적식별(NCTR, Non-Cooperative Target Recognition)은 전자정보 등 다른 체계의 지원 없이 레이다 자체적으로 표적을 식별하는 기능을 말한다. 이를 구현하기 위한 대표적인 방법 중 하나인 역합성개구레이다(ISAR) 영상은 표적의 기동 및 위치에 따라 크게 변하기 때문에 기종을 판단할 수 있는 데이터베이스 없이 이를 자동으로 식별하기란 매우 어렵다. 본 연구에서는 실측 영상이 부족한 상황에서도 ISAR 영상 시뮬레이션 및 딥러닝 기법을 활용한 식별 데이터베이스 구축방안에 대해 논한다. 다양한 레이다 운용 환경에 따라 변화하는 ISAR 영상을 모사하기 위해 '완전 산란체', '결손 산란체', 'JEM 잡음'으로 명명한 영상 형성 과정을 거쳐 이를 학습하는 모델을 제안한다. 이 모델의 학습 결과를 통해 유사한 형상의 시뮬레이션 영상은 물론 처음 입력된 실측 ISAR 영상도 식별할 수 있음을 확인하였다.

• 한국전자파학회논문지
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• 제27권8호
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• pp.758-765
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• 2016

본 논문에서는 편대비행 중인 다수의 표적을 식별하기 위하여 기존의 표적들을 분리시키는 기법을 이용하는 대신 PSO(Particle Swarm Optimization) 알고리즘을 이용하여 미리 학습되어 있던 각 표적의 역합성 개구면 레이다(Inverse Synthetic Aperture Radar: ISAR) 영상들을 합성하는 방법을 제안한다. 제안된 기법에서 ISAR 영상의 합성은 표적의 수와 관측 각도 및 표적의 위치를 변수로 하는 비선형문제를 최적화함으로써 수행된다. 추적 레이다를 통하여 관측 각도가 추정 됨을 가정한 후, 표적의 수와 위치는 PSO로 템플릿 매칭(template matching)을 최적화 하여 추정된다. 축소된 크기의 F-16을 사용한 시뮬레이션 결과, 편대비행 중인 표적들의 ISAR 영상과 동일한 ISAR 영상이 합성됨으로써 제안된 기법의 효용성이 검증되었다.

• 한국인쇄학회지
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• 제25권1호
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• pp.65-80
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• 2007

The input and output device requires precise color representation and CMS (Color Management System) because of the increasing number of ways to apply the digital image into electronic publishing. However, there are slight differences in the device dependent color signal among the input and output devices. Also, because of the non-linear conversion of the input signal value to the output signal value, there are color differences between the original copy and the output copy. It seems necessary for device-dependent color information values to change into device-independent color information values. When creating an original copy through electronic publishing, there should be color management with the input and output devices. From the devices' three phases of calibration, characterization and color conversion, the device-dependent color should undergo a color transformation into a device-independent color. In this paper, an experiment was done where the input device used the linear multiple regression and the sRGB color space to perform a color transformation. The output device used the GOG, GOGO and sRGB for the color transformation. After undergoing a color transformation in the input and output devices, the best results were created when the original target underwent a color transformation by the scanner and digital camera input device by the linear multiple regression, and the LCD output device underwent a color transformation by the GOG model.

• 한국전자통신학회논문지
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• 제8권8호
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• pp.1129-1135
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• 2013

본 논문에서는 최적 특징점 선택기법를 적용한 다중 최적 Adaboost 분류기를 기반으로 새로운 차량 검출 알고리즘을 제안한다. 제안하는 알고리즘은 2 가지 주요 모듈로 구성된다. 첫 번째는 설치된 카메라의 사이트 모델링을 이용한 영상 스케일링을 기반으로 하는 이론적 DDISF(Distance Dependent Image Scaling Factor) 모듈이며, 두 번째는 차량과 카메라의 거리에 대응하는 최적 Haar-like 특징을 활용하는 것이다. 실험 결과 제안하는 알고리즘은 기존의 방법에 비하여 인식 성능이 개선됨을 확인하였다. 제안하는 알고리즘은 96.43% 의 인식률과 약 3.77%의 오검출이 발생하였다. 이러한 성능은 기존의 표준 Adabooost 알고리즘에 비하여 각각 3.69%와 1.28% 의 성능을 개선한 것이다.

• 대한의용생체공학회:의공학회지
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• 제36권5호
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• pp.211-220
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• 2015

Recently, model-based iterative reconstruction (MBIR) has played an important role in transmission tomography by significantly improving the quality of reconstructed images for low-dose scans. MBIR is based on the penalized-likelihood (PL) approach, where the penalty term (also known as the regularizer) stabilizes the unstable likelihood term, thereby suppressing the noise. In this work we further improve MBIR by using a more expressive regularizer which can restore the underlying image more accurately. Here we used a spline regularizer derived from a linear combination of the two-dimensional splines with first- and second-order spatial derivatives and applied it to a non-quadratic convex penalty function. To derive a PL algorithm with the spline regularizer, we used a separable paraboloidal surrogates algorithm for convex optimization. The experimental results demonstrate that our regularization method improves reconstruction accuracy in terms of both regional percentage error and contrast recovery coefficient by restoring smooth edges as well as sharp edges more accurately.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 G
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• pp.3048-3050
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• 1999

This paper suggests a position estimating algorithm using mono vision system with projective geometry method. Generally, 3-D information can not be easily extracted from mono vision system which is taken by a camera at a specific point. But this defect is overcome by adopting model-based image analysis and selecting lines and points on the ground as natural landmarks. And this paper suggests a method that estimates position from many natural landmarks by distance error weight function.