• 대한전자공학회논문지SP
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• 제41권6호
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• pp.1-7
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• 2004

디지털 미디어와 통신 네트워크의 급속한 발전으로 적절한 지적소유권(IPR) 보호 기술인 데이터 인증방법에 대한 필요성이 절실히 요구되고 있다. 본 논문에서는 정보량이 많은 워터마크를 삽입하여도 워터마크가 삽입된 영상의 화질을 열화시키지 않고, 특히 영상의 일부가 제거되는 공격에서 강인성을 갖기 위해, 제안한 선형비트확장(LBX) 인터리빙을 이용하여 마킹 공간인 이산 웨이브렛 변환(DWT)영역의 자주파수 계수에 그레이스케일 워터마크 로고를 삽입하는 새로운 영상 워터마킹 기술을 제안하였다. 실험결과 영상절단과 영상회전 등과 같은 영상의 일부가 제거되는 공격에 대해서 특히 높은 강인성을 가짐을 검증하였다.

• 대한한의진단학회지
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• 제17권3호
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• pp.233-240
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• 2013

Objectives The objective and universal grading system for the facial nerve palsy is needed to the objectification of treatment in Oriental medicine. In this study, the facial nerve palsy grading was developed with combination of image processing technique and Nottingham scale. Methods The developed system is composed of measurement part, image processing part, facial nerve palsy evaluation part, and display part. With the video data recorded by webcam at measurement part, the positions of marker were measured at image processing part. In evaluation part, Nottingham scales were calculated in four different facial expressions with measured marker position. The video of facial movement, time history of marker position, and Nottingham scale were displayed in display part. Results & Conclusion The developed system was applied to a normal subject and a abnormal subject with facial nerve palsy. The left-right difference of Nottingham scores was large in the abnormal compared with the normal. In normal case, the change of the length between supraorbital point and infraorbital point was larger than that of the length between lateral canthus and angle of mouth. The abnormal case showed an opposite result. The developed system showed the possibilities of the objective and universal grading system for the facial nerve palsy.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.344-346
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• 2004

IKONOS 1m satellite imagery is particularly well suited for 3-D feature extraction and 1 :5,000 scale topographic mapping. Because the image line and sample calculated by given RPCs have the error of more than 11m, in order to be able to perform feature extraction and topographic mapping, rational polynomial coefficients(RPCs) camera model that are derived from the very complex IKONOS sensor model to describe the object-image geometry must be refined by several Ground Control Points(GCPs). This paper presents a quantitative evaluation of the geometric accuracy that can be achieved with IKONOS imagery by refining the offset and scaling factors of RPCs using several GCPs. If only two GCPs are available, the offsets and scale factors of image line and sample are updated. If we have more than three GCPs, four parameters of the offsets and scale factors of image line and sample are refined first, and then six parameters of the offsets and scale factors of latitude, longitude and height are updated. The stereo images acquired by IKONOS satellite are tested using six ground points. First, the RPCs model was refined using 2 GCPs and 4 check points acquired by GPS. The results from IKONOS stereo images are reported and these show that the RMSE of check point acquired from left images and right are 1.021m and 1.447m. And then we update the RPCs model using 4 GCPs and 2 check points. The RMSE of geometric accuracy is 0.621 m in left image and 0.816m in right image.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권12호
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• pp.4439-4455
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• 2021

Compared with two-stage object detection algorithms, one-stage algorithms provide a better trade-off between real-time performance and accuracy. However, these methods treat the intermediate features equally, which lacks the flexibility to emphasize meaningful information for classification and location. Besides, they ignore the interaction of contextual information from different scales, which is important for medium and small objects detection. To tackle these problems, we propose an image pyramid network based on dual attention mechanism (DAIPNet), which builds an image pyramid to enrich the spatial information while emphasizing multi-scale informative features based on dual attention mechanisms for one-stage object detection. Our framework utilizes a pre-trained backbone as standard detection network, where the designed image pyramid network (IPN) is used as auxiliary network to provide complementary information. Here, the dual attention mechanism is composed of the adaptive feature fusion module (AFFM) and the progressive attention fusion module (PAFM). AFFM is designed to automatically pay attention to the feature maps with different importance from the backbone and auxiliary network, while PAFM is utilized to adaptively learn the channel attentive information in the context transfer process. Furthermore, in the IPN, we build an image pyramid to extract scale-wise features from downsampled images of different scales, where the features are further fused at different states to enrich scale-wise information and learn more comprehensive feature representations. Experimental results are shown on MS COCO dataset. Our proposed detector with a 300 × 300 input achieves superior performance of 32.6% mAP on the MS COCO test-dev compared with state-of-the-art methods.

• 전기전자학회논문지
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• 제28권3호
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• pp.271-278
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• 2024

본 논문은 위상천이 간섭계 원리를 기반으로 계조도 영상의 위상 변환 매핑을 이용한 암호화와 복호화 방법을 제안한다. 제안한 암호화 시스템에서 대칭형 보안키로 암호화한 암호 영상은 복소 디지털 홀로그램 함수의 형태로 구성된다. 한편 암호화하는 계조도영상은 위상 매핑 변환되고 무작위로 발생한 이진 보안 암호화키의 위상과 합쳐져 입력 위상 합수로 사용된다. 위상 정보의 복호화는 복소 디지털 홀로그램 함수와 보안 암호화키를 사용하여 수행되고 위상 역변환을 통하여 원래의 계조도 영상을 복원한다. 제안한 방법은 암호화 과정에서 CCD의 양자화 수준을 8-bits(2⁸=256 levels)로 하였을 때 복원된 영상의 상관 계수가 0.995로 원래의 256-계조도 영상이 잘 복원되었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권12호
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• pp.4420-4438
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• 2021

Image dehazing is an ill-posed problem which is far from being solved. Traditional image dehazing methods often yield mediocre effects and possess substandard processing speed, while modern deep learning methods perform best only in certain datasets. The haze removal effect when processed by said methods is unsatisfactory, meaning the generalization performance fails to meet the requirements. Concurrently, due to the limited processing speed, most dehazing algorithms cannot be employed in the industry. To alleviate said problems, a lightweight fast dehazing network based on a multiple scale-patch framework (MSP) is proposed in the present paper. Firstly, the multi-scale structure is employed as the backbone network and the multi-patch structure as the supplementary network. Dehazing through a single network causes problems, such as loss of object details and color in some image areas, the multi-patch structure was employed for MSP as an information supplement. In the algorithm image processing module, the image is segmented up and down for processed separately. Secondly, MSP generates a clear dehazing effect and significant robustness when targeting real-world homogeneous and nonhomogeneous hazy maps and different datasets. Compared with existing dehazing methods, MSP demonstrated a fast inference speed and the feasibility of real-time processing. The overall size and model parameters of the entire dehazing model are 20.75M and 6.8M, and the processing time for the single image is 0.026s. Experiments on NTIRE 2018 and NTIRE 2020 demonstrate that MSP can achieve superior performance among the state-of-the-art methods, such as PSNR, SSIM, LPIPS, and individual subjective evaluation.

• 반도체디스플레이기술학회지
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• 제10권3호
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• pp.89-94
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• 2011

The image identification methodology associates an image with a unique identifiable representation. Whenever the methodology regenerates an identifier for the same image, moreover, the newly created identifier needs to be consistent in terms of representation value. In this paper, we discuss a methodology for image identifier generation utilizing luminance correlation. We furthermore propose a method for performance enhancement of the image identifier generation. We also demonstrate the experimental evaluations for uniqueness and similarity analysis and performance improvement that have shown favorable results.

• 종양간호연구
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• 제8권2호
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• pp.86-92
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• 2008

Purpose: This study was designed to investigate the body-image and quality of life in breast cancer patients with mastectomy. Methods: Data were obtained by self-reported questionaries from 110 patients undergone mastectomy from August 1 to 31, 2007. And data were analyzed using SPSS/PC WIN 12.0 program and frequency, percentage, mean, minimum, maximum, t-test, ANOVA, Scheffe test, Pearson's Correlation Coefficient were used. The results of the study were as follows; 1) Mean of Body image of the subjects was $52.54{\pm}6.67$ (range of scale; 17 to 85). Mean of quality of life was $118.01{\pm}34.37$ (range of scale; 0 to 10). 2) There was no significant difference with the score of body image by demographic and disease-related characteristics of subjects. 3) There was significant difference with the score of quality of life by the work type and economic status of subjects. 4) Relationship between body image and quality of life showed moderately strong positive correlation (r=0.408, p<0.0001). Conclusion: Mean of body image and of quality of life in mastectomy patients were moderate. Relationship between body image and quality of life showed moderately strong positive correlation. Therefore, for improving the quality of life, it is needed to improve body image in breast cancer patients with mastectomy.

• 한국디자인학회:학술대회논문집
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• 한국디자인학회 1998년도 제6회 1998 봄 학술연구발표대회 개요 집 디자인 학 연구
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• pp.11-12
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• 1998

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.736-739
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• 2006

While hyperspectral data are very rich in information, their processing poses several challenges such as computational requirements, noise removal and relevant information extraction. In this paper, the application of advanced scale-space filtering to selected hyperspectral bands was investigated. In particular, a pre-processing tool, consisting of anisotropic diffusion and morphological leveling filtering, has been developed, aiming to an edge-preserving smoothing and simplification of hyperspectral data, procedures which are of fundamental importance during feature extraction and object detection. Two scale space parameters define the extent of image smoothing (anisotropic diffusion iterations) and image simplification (scale of morphological levelings). Experimental results demonstrated the effectiveness of the developed scale space filtering for the enhancement and smoothing of hyperspectral remote sensing data and their advantage against watershed over-segmentation problems and edge detection.