• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2003.11a
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• pp.61-64
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• 2003

무손실 이미지 압축은 (Lossless Image Compression)은 손실이미지 압축(Lossy Image Compression)에 비해, 압축률(compression ratio)은 떨어지지만, 반면 원이미지와 복원이미지가 완전히 일치하므로, 원인이미지의 품질을 그대로 유지학 수 있다. 따라서, 이미지의 품질(Quality)과 압축효율(compression ratio)은 서로 상반된 관계에 있으며, 지금도 좀 더 놀은 압축효과를 얻으려는 여러 무손실 압축 방법이 발표되고 있다. 무손실 이미지 압축은 이미지의 정확성과 정밀성이 요구되는, 의료영양분야에서 가장 널리 쓰이고 있으며, 그밖에, 원본이미지를 기본으로 다른 이미지프로세싱이 필요한 경우, 압축 복원을 반복적으로 수행할 필요가 있을 때, 기타 사진 예술분야, 원격 영상 등 정밀성이 요구되는 분양에서 쓰이고 있다. [7]. 무손실 이미지 압축의 가장 대표적인 CALIC[3]과 JPEG_LS[2]를 들 수 있다. CALIC은 비교적 높은 압축률을 나타내지만, 3-PASS의 과정을 거치는 복잡도가 지적되고 있다. 반면 JPEG-LS는 압축률은 CALIC에 미치지 못하지만 빠른 코딩/디코딩 속도를 보인다. 본 논문에서는 여거 가지의 예측 모드를 두어, 블록단위별로 주변 CONTEXT에 따라, 최상의 예측 모드를 판단하여, 이를 적용, 픽셀의 여러 값을 최소화하였다. 그 후 적응산술 부호기(Adaptive arithmetc coder)를 이용하여, 인코딩을 하였다. 이때 최대 에러값은 64를 넘지 않게 했으며, 또한 8*8블록별로 에러의 최대값을 측정하여 그 값을 $0\~7$까지의 8개의 대표값으로 양자화하는 방법을 통하여 그에 따라 8개의 보호화 심볼 모델중 알맞은 모델에 적용하였다. 이를 통해, 그 소화값의 확률 구간을 대폭 넓힘으로써, 에러 이미지가 가지고 있는 엔트로피에 좀 근접하게 코딩을 할 수 있게 되었다. 이 방법은 실제로 Arithmetic Coder를 이용하는 다른 압축 방법에 그리고 적용할 수 있다. 실험 결과 압축효율은 JPEG-LS보다 약 $5\%$의 압축 성능 개선이 있었으며, CALIC과는 대등한 압축률을 보이며, 부호화/복호화 속도는 CALIC보다 우수한 것으로 나타났다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.2
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• pp.431-436
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• 2014

Cell segmentation which extracts cell objects from background is one of basic works in bio-imaging which analyze cell images acquired from live cells in cell culture. In the case of clear images, they have a bi-modal histogram distribution and segmentation of them can easily be performed by global threshold algorithm such as Otsu algorithm. But In the case of degraded images, it is difficult to get exact segmentation results. In this paper, we developed a cell segmentation system that it classify input images by the type of their histogram distribution and then apply a proper segmentation algorithm. If it has a bi-modal distribution, a global threshold algorithm is applied for segmentation. Otherwise it has a uni-modal distribution, our algorithm is performed. By experimentation, our system gave exact segmentation results for uni-modal cell images as well as bi-modal cell images.

• Journal of KIISE:Software and Applications
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• v.31 no.2
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• pp.182-188
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• 2004

This paper proposes to synthesize facial images from a few parameters for the pose and the expression of their constituent components. This parameterization makes the representation, storage, and transmission of face images effective. But it is difficult to parameterize facial images because variations of face images show a complicated nonlinear manifold in high-dimensional data space. To tackle this problem, we use an LLE (Locally Linear Embedding) technique for a good representation of face images, where the relationship among face images is preserving well and the projected manifold into the reduced feature space becomes smoother and more continuous. Next, we apply a snake model to estimate face feature values in the reduced feature space that corresponds to a specific pose and/or expression parameter. Finally, a synthetic face image is obtained from an interpolation of several neighboring face images in the vicinity of the estimated feature value. Experimental results show that the proposed method shows a negligible overlapping effect and creates an accurate and consistent synthetic face images with respect to changes of pose and/or expression parameters.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.111-121
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• 2021

As the main carrier of information, digital image is becoming more and more important. However, with the popularity of image acquisition equipment and the rapid development of image editing software, in recent years, digital image counterfeiting incidents have emerged one after another, which not only reduces the credibility of images, but also brings great negative impacts to society and individuals. Image copy-paste tampering is one of the most common types of image tampering, which is easy to operate and effective, and is often used to change the semantic information of digital images. In this paper, a method to protect the authenticity and integrity of image content by studying the tamper detection method of image copy and paste was proposed. In view of the excellent learning and analysis ability of deep learning, two tamper detection methods based on deep learning were proposed, which use the traces left by image processing operations to distinguish the tampered area from the original area in the image. A series of experimental results verified the rationality of the theoretical basis, the accuracy of tampering detection, location and classification.

• Journal of the Korea Convergence Society
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• v.10 no.6
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• pp.147-155
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• 2019

In the glocal era, the competition becomes more intense in regional development centered around cities rather than countries. This suggests that increasing the city's unique charms through a positive city image can allow the city to gain a competitive edge in the competition for regional development. In this background, this study would contribute to improving the positive image of Pyeongtaek City, presenting the value and development direction of the image it would pursue, aiming to develop a promotional design for the improvement of the city image. As the details of the research, this study recognized the city image as an efficient means of public relations of the city from its concept and necessity. Thus, this study conducted a survey on the analysis of status and image of Pyeongtaek City. This study would contribute to improving the positive image of Pyeongtaek City, presenting the results of the development of a promotional design of its administration and a plan for utilization in the manual, proceeding by stages.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.6
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• pp.700-705
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• 2020

Generative adversarial networks are methods of generating images by opposing two neural networks. When generating the image, randomly generated noise is rearranged to generate the image. The image generated by this method is not generated well depending on the noise, and it is difficult to generate a proper image when the number of pixels of the image is small In addition, the speed and size of data accumulation in data classification increases, and there are many difficulties in labeling them. In this paper, to solve this problem, we propose a technique to generate noise based on random noise using real data. Since the proposed system generates an image based on the existing image, it is confirmed that it is possible to generate a more natural image, and if it is used for learning, it shows a higher hit rate than the existing method using the hostile neural network respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.6
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• pp.774-784
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• 2021

The object detection and recognition process is a very important task in the field of computer vision, and related research is actively being conducted. However, in the actual object recognition process, the recognition accuracy is often degraded due to the resolution mismatch between the training image data and the test image data. To solve this problem, in this paper, we designed and developed an integrated object recognition and super-resolution framework by proposing an image super-resolution technique to improve object recognition accuracy. In detail, 11,231 license plate training images were built by ourselves through web-crawling and artificial-data-generation, and the image super-resolution artificial neural network was trained by defining an objective function to be robust to the image flip. To verify the performance of the proposed algorithm, we experimented with the trained image super-resolution and recognition on 1,999 test images, and it was confirmed that the proposed super-resolution technique has the effect of improving the accuracy of character recognition.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.129-132
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• 2022

Super-resolution is the process of converting a low-quality image into a high-quality image. This study was conducted using ESPCN. In a super-resolution deep neural network, different quality images can be output even when receiving the same input data according to the activation function that determines the weight when passing through each node. Therefore, the purpose of this study is to find the most suitable activation function for super-resolution by applying the activation functions ReLU, ELU, and Swish and compare the quality of the output image for the same input images. The CelebaA Dataset was used as the dataset. Images were cut into a square during the pre-processing process then the image quality was lowered. The degraded image was used as the input image and the original image was used for evaluation. As a result, ELU and swish took a long time to train compared to ReLU, which is mainly used for machine learning but showed better performance.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.3
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• pp.413-420
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• 2023

For a long time, researchers have presented excellent results in the field of image retrieval due to many studies on CBIR. However, there is still a semantic gap between these search results for images and human perception. It is still a difficult problem to classify images with a level of human perception using a small number of images. Therefore, this paper proposes an image classification model using deep learning-based transfer learning to minimize the semantic gap between images of people and search systems in image retrieval. As a result of the experiment, the loss rate of the learning model was 0.2451% and the accuracy was 0.8922%. The implementation of the proposed image classification method was able to achieve the desired goal. And in deep learning, it was confirmed that the CNN's transfer learning model method was effective in creating an image database by adding new data.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.279-279
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• 2022

최근 이미지분석 기술은 하드웨어 및 소프트웨어 기술의 급격한 발전으로 인해 의학, 생물학, 지리학, 재료공학 등에서 수많은 연구 분야에서 광범위하게 활용되고 있으며, 이미지분석은 다량의 토사에 대하여 입경을 포함한 형상학적 특성을 간편하게 정량화 할 수 있기 때문에 매우 효과적인 분석 방법으로 판단된다. 현재 모래의 입도분석 방법으로는 신뢰성 있는 체가름 시험법(KSF2302) 등이 있으나, 번거로운 처리과정과 많은 시간이 소요된다. 또한 입자형상은 입경이 세립 할수록 직접 측정이 어렵기 때문에, 최근에는 이미지 분석을 이용하는 방법이 시도되고 있다. 본 연구에서는 75㎛ 이상의 조립질 하상 토사 이미지를 취득하여, 입자들의 장·축단 길이, 면적, 둘레, 공칭직경 및 종횡비 등의 형상학적 특성인자를 자동으로 측정하는 프로그램 개발을 수행하였다. 프로그램은 이미지 분석에 특화된 라이브러리인 OpenCV(Open Source Computer Vision)를 적용하였다. 이미지 분석 절차는 크게 이미지 취득, 기하보정, 노이즈제거, 객체추출 및 형상인자 측정 단계로 구성되며, 이미지 취득시 패널의 하단에 Back light를 부착해 시료에 의해 발생되는 음영을 제거하였다. 기하보정은 원근변환(perspective transform)을 적용했으며, 노이즈 제거는 모폴로지 연산과 입자간의 중첩으로 인한 뭉침을 제거하기 위해 watershed 알고리즘을 적용하였다. 최종적으로 객체의 외곽선 추출하여 입자들의 다양한 정보(장축, 단축, 둘레, 면적, 공칭직경, 종횡비)를 산출하고, 분포형으로 제시하였다. 본 연구에서 제안하는 이미지분석을 적용한 토사의 형상학적 특성 측정 방법은 시간과 비용의 측면에서 보다 효율적으로 하상 토사에 대한 다양한 정보를 획득 할 수 있을 것으로 기대한다.