• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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• pp.165-170
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• 1999

Previous technology of remote sensing was focused on analyzing raster image and gaining information through image processing. But now it has extended to diverse fields like automatic map generation, material exploitation or monitoring environmental changes with effort to utilizing practical usage. And with rapid expansion of information exchange on Internet and high-speed network, the demand of public which want to utilize remotely sensed image has been increased. This makes growth of service on acquisition and processing remotely sensed image. GeoNet is a Java-based remotely sensed image processing system. It is based on Java object-oriented paradigm and features cross-platform, web-based execution and extensibility to client/server remotely sensed image processing model. Remotely sensed image processing software made by Java programming language can suggest alternatives to meet readily demand on remotely sensed image processing in proportion to increase of remotely sensed data. In this paper, we introduce GeoNet and explain its architecture.

• 한국의류산업학회지
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• 제5권5호
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• pp.503-510
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• 2003

The purpose this study is to segments apparel market based on difference real-self image and ideal-self image. The objects of the study were to prepare for the establishment of marketing strategy and alternative plan intended to users which are needed in subdivided market, after analyzing according to what the subdivided market is divided into due to the difference real-self image and idea-self image and what difference do they show as a demographic special quality or as a general active special quality in each subdivided market. Factor analysis was performed to determine the leading difference real-self image and ideal-self image, and cluster analysis was employed to identify groups of respondents based on the delineated five image difference factors. Based on the finding, three distinct groups were formed: ideal-self image seeker group, moderators group, real-self image seeker group. And logistic regression was used to assess the relative importance that demographic characteristics play in determining the segmentation. The results of this study show statistically significant differences among the three groups in terms of demographic. Marketing and management implications for effectively targeting the segments are discussed.

• 방송공학회논문지
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• 제21권2호
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• pp.272-275
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• 2016

본 논문은 위성 영상을 위한 안개 제거 및 표면반사율 기반의 특징점 검출 방법을 제안한다. 기존의 안개 제거를 위한 DCP 방법은 패치 기반의 처리 방식으로 인해 전달맵 생성 과정에서 블록현상이 발생하게 되고, 이는 영상을 흐리게 하는 원인이 된다. 따라서 제안한 은닉마코프 기반의 방법은 영상의 블록 현상을 제거하고 선명도를 향상한다. 또한 표면반사율 기반의 견고한 특징점 추출을 통해서 영상 정합의 정확성을 향상하였다. 실험을 통해 제안한 방법이 기존 방법에 비해 안개 제거의 성능에서 우수함을 확인하였으며 이를 통해 특징 검출 및 위성 영상 정합에 적합함을 확인하였다.

• 대한원격탐사학회지
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• 제39권4호
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• pp.395-407
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• 2023

Super-resolution (SR) has great significance in image processing because it enables downstream vision tasks with high spatial resolution. Recently, SR studies have adopted deep learning networks and achieved remarkable SR performance compared to conventional example-based methods. Deep-learning-based SR models generally require low-resolution (LR) images and the corresponding high-resolution (HR) images as training dataset. Due to the difficulties in obtaining real-world LR-HR datasets, most SR models have used only HR images and generated LR images with predefined degradation such as bicubic downsampling. However, SR models trained on simple image degradation do not reflect the properties of the images and often result in deteriorated SR qualities when applied to real-world images. In this study, we propose an image degradation model for HR satellite images based on the modulation transfer function (MTF) of an imaging sensor. Because the proposed method determines the image degradation based on the sensor properties, it is more suitable for training SR models on remote sensing images. Experimental results on HR satellite image datasets demonstrated the effectiveness of applying MTF-based filters to construct a more realistic LR-HR training dataset.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 하계종합학술대회 논문집(4)
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• pp.35-38
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• 2000

The method of Content-based Triangular Mesh Image representation in moving pictures makes better performance in prediction error ratio and visual efficiency than that of classical block matching. Specially if background and objects can be separated from image, the objects are designed by Irregular mesh. In this case this irregular mesh design has an advantage of increasing video coding efficiency. This paper presents the techniques of mesh generation, motion estimation using these mesh, uses image warping transform such as Affine transform for image reconstruction, and evaluates the content based mesh design through computer simulation.

• International Journal of Advanced Culture Technology
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• 제11권1호
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• pp.283-289
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• 2023

AI is bringing about drastic changes not only in the aspect of technologies but also in society and culture. Medical AI based on deep learning have developed rapidly. Especially, the field of medical image analysis has been proven that AI can identify the characteristics of medical images more accurately and quickly than clinicians. Evaluating the latest results of the AI-based medical image processing is important for the implication for the development direction of medical AI. In this paper, we analyze and evaluate the latest trends in AI-based medical image analysis, which is showing great achievements in the field of medical AI in the healthcare industry. We analyze deep learning models for medical image analysis and AI-based medical image segmentation for quantitative analysis. Also, we evaluate the future development direction in terms of marketability as well as the size and characteristics of the medical AI market and the restrictions to market growth. For evaluating the latest trend in the deep learning-based medical image processing, we analyze the latest research results on the deep learning-based medical image processing and data of medical AI market. The analyzed trends provide the overall views and implication for the developing deep learning in the medical fields.

• 대한전자공학회논문지SP
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• 제48권4호
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• pp.38-48
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• 2011

Backlight Profile과 Image Pixel의 Homogeneity 분석을 통한 Block 단위 LCD Backlight Nonideality 및 Crosstalk Compensation 방안이 제안되었다. Image에 따라 Block Size 및 연산에서 제외되는 Block 범위 그리고 연산에 고려되는 Backlight 범위를 최적화하여, 화질을 유지하면서 연산량을 최소화시켰다. 실제 영상을 바탕으로 하는 Simulation을 통해 제안된 Compensation 연산량과 화질이 평가되었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권10호
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• pp.5197-5218
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• 2019

Large-scale retrieval algorithm is problem for visual analyses applications, along its research track. In this paper, we propose a high-efficiency region division-based image retrieve approaches, which fuse low-level local color histogram feature and texture feature. A novel image region division is proposed to roughly mimic the location distribution of image color and deal with the color histogram failing to describe spatial information. Furthermore, for optimizing our region division retrieval method, an image descriptor combining local color histogram and Gabor texture features with reduced feature dimensions are developed. Moreover, we propose an extended Canberra distance method for images similarity measure to increase the fault-tolerant ability of the whole large-scale image retrieval. Extensive experimental results on several benchmark image retrieval databases validate the superiority of the proposed approaches over many recently proposed color-histogram-based and texture-feature-based algorithms.

• 반도체디스플레이기술학회지
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• 제19권1호
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• pp.6-10
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• 2020

Unlike modern photography technology, in the early days, efforts to physically compose an image with a concept similar to the current photograph have not been popular or commercially successful. The limitation of the use of images as artistic media or recordings has reached the stage of introducing the technology of image analysis to automate the function that humans recognize and judge through vision. In addition, the accuracy of the image has exceeded the human visual ability, enabling the technology that enables the step of recognizing and informing the fact that the human is not aware of it. Based on such a base, the range that can be applied through the image data in the future era can be said to be unpredictable, and the technology that targets large scale image database instead of an image is also expanding the possibilities as a new application technology. In order to identify a particular image from a massive database, different methodologies have been introduced. In this paper, we discuss image identifier production methods based on multi-resolution pyramid.

• 대한임베디드공학회논문지
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• 제6권1호
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• pp.48-54
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• 2011

As personal or compact devices with image acquisition functionality are becoming easily available for common users, the voluminous images that need to be managed by image related services or systems demand efficient and effective methods in the perspective of image identification. The objective of image identification is to associate an image with a unique identifier. Moreover, whenever an image identifier needs to be regenerated, the newly generated identifier should be consistent. In this paper, we propose three image identifier generation methods utilizing image features: linear component, luminance area, and combination of both features. The linear component based method exploits the information of distribution of partial lines over an image, while the luminance area based method utilizes the partition of an image into a number of small areas according to the same luminance degree. The third method is proposed in order to take advantage of both former methods. In this paper, we also demonstrate the experimental evaluations for uniqueness and similarity analysis that have shown favorable results.