• Journal of Broadcast Engineering
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• v.26 no.2
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• pp.175-183
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• 2021

The study of the fine grained classification of images continues to develop, but the study of object recognition for animals with polymorphic properties is proceeding slowly. Using only pet images corresponding to dogs and cats, this paper aims to compare methods using image processing and methods using deep learning among methods of classifying species of animals, which are fine grained classifications. In this paper, Grab-cut algorithm is used for object segmentation by method using image processing, and method using Fisher Vector for image encoding is proposed. Other methods used deep learning, which has achieved good results in various fields through machine learning, and among them, Convolutional Neural Network (CNN), which showed outstanding performance in image recognition, and Tensorflow, an open-source-based deep learning framework provided by Google. For each method proposed, 37 kinds of pet images, a total of 7,390 pages, were tested to verify and compare their effects.

• Journal of IKEEE
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• v.27 no.4
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• pp.494-500
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• 2023

Image processing is currently used in various fields. Among them, autonomous vehicles, medical image processing, and robot control require fast image processing response speeds. To fulfill this requirement, hardware design for real-time processing is being actively researched. In addition to the size of the input image, the hardware processing speed is affected by the size of the inactive video periods that separate lines and frames in the image. In this paper, we design three different scaler structures based on the type of line memories, which is closely related to the inactive video periods. The structures are designed in hardware using the Verilog standard language, and synthesized into logic circuits in a field programmable gate array environment using Xilinx Vivado 2023.1. The synthesized results are used for frame rate analysis while comparing standard image sizes that can be processed in real time.

• Journal of the Korean Society of Radiology
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• v.8 no.3
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• pp.123-136
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• 2014

In this paper, we study to classify molecular imaging and applications to predict future. Molecular imaging in vivo at the cellular level and the molecular level changes taking place to be imaged, that is molecular cell biology and imaging technology combined with the development of the new field. Molecular imaging is used fluorescence, bioluminescence, SPECT, PET, MRI, Ultrasound and other imaging technologies. That is applied to monitoring of gene therapy, cell tracking and monitoring of cell therapy, antibody imaging, drug development, molecular interaction picture, the near-infrared fluorescence imaging of cancer using fluorescence, bacteria using tumor-targeting imaging, therapeutic early assessment, prediction and therapy. The future of molecular imaging would be developed through fused interdisciplinary research and mutual cooperation, which molecular cell biology, genetics, chemistry, physics, computer science, biomedical engineering, nuclear medicine, radiology, clinical medicine, etc. The advent of molecular imaging will be possible to early diagnosis and personalized treatment of disease in the future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.6
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• pp.372-378
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• 2016

The purpose of this study is to develop a system configuration for gathering data and building a database for agriculture. Some foreign agriculture-related companies have already constructed such a database for scientific agriculture. The hardware of this system is composed of automatic capturing equipment based on aerial photography using a UAV. The software is composed of parts for stitching images, matching GPS data with captured images, and building a database of collected weather information, farm operation data, and aerial images. We suggest a method for building the database, which can include information about the amount of agricultural products, weather, farm operation, and agricultural land images. The images of this system are about 5 times better than satellite images. Factors such as farm working and environmental factors can be basic data for analyzing the full impact of agriculture land. This system is expected to contribute to the scientific analysis of Korea's agriculture.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.1
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• pp.39-46
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• 2019

In this study, before system development for the elimination of foreign fish species, we propose an algorithm to classify fish species by training fish images with CNN. The raw data for CNN learning were directly captured images for each species, Dataset 1 increases the number of images to improve the classification of fish species and Dataset 2 realizes images close to natural environment are constructed and used as training and test data. The classification performance of four CNNs are over 99.97% for dataset 1 and 99.5% for dataset 2, in particular, we confirm that the learned CNN using Data Set 2 has satisfactory performance for fish images similar to the natural environment. And among four CNNs, AlexNet achieves satisfactory performance, and this has also the shortest execution time and training time, we confirm that it is the most suitable structure to develop the system for the elimination of foreign fish species.

• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.873-879
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• 2023

This study explored the use of artificial intelligence(AI) to detect foreign bodies in chest X-ray images. Medical imaging, especially chest X-rays, plays a crucial role in diagnosing diseases such as pneumonia and lung cancer. With the increase in imaging tests, AI has become an important tool for efficient and fast diagnosis. However, images can contain foreign objects, including everyday jewelry like buttons and bra wires, which can interfere with accurate readings. In this study, we developed an AI algorithm that accurately identifies these foreign objects and processed the National Institutes of Health chest X-ray dataset based on the YOLOv8 model. The results showed high detection performance with accuracy, precision, recall, and F1-score all close to 0.91. Despite the excellent performance of AI, the study solved the problem that foreign objects in the image can distort the reading results, emphasizing the innovative role of AI in radiology and its reliability based on accuracy, which is essential for clinical implementation.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04a
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• pp.727-729
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• 2002

본 논문에서 우리는 grey scale 영상을 weighted finite automata(WFA)로써 기술하는 두개의 알고리즘(2, 4)을 분석하였다. 또한 원영상과 WFA를 이용하여 압축된 영상간의 error를 분석하고 그 결과를 제시하였다. 구체적으로, 영상복원 tolerance $\delta$를 이용하여 찾아진 atomatone에 의해 복원된 영상과 원영상의 ι$^2$-norm의 차이가 $\delta$보다 작거나 같음을 증명하였다.

• Proceedings of the Korea Multimedia Society Conference
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• 2001.11a
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• pp.143-147
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• 2001

본 논문은 연속된 이미지로부터 고속 영상 모자익을 구성하는 새로운 접근을 기술한다. 제안하는 알고리듬은 전체 영상이 아닌, 종첩영역에서 특징점 및 대응점을 찾아 초기 변한 행렬을 구함으로 좀더 정확한 변환식을 표현하도록 하고 계산량을 감소시킨다. 또한 고속으로 모자익을 수행하도록 하기 위해 OpenGL 기반 텍스쳐 매핑을 이용하였다. 기존의 방법은 모든 영상의 픽셀에 변환식을 곱함으로 인해 많은 계산시간을 초래했다. 본 논문에서 제안하는 방법은 OpenGL 기반 텍스쳐 매핑을 이용해 영상의 각 버텍스에 변환식을 곱함으로서 계산시간을 단축시켰다. 그 결과, PC에서 카메라로부터 영상을 받아들여 고속으로 모자익을 구성할 수 있다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.11a
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• pp.200-203
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• 2019

영상처리 분야에서 이미지 샤프닝 기법은 주관적 화질 향상에 큰 역할을 하고 있다. 본 논문에서는 모폴로지 기법을 이용한 향상된 이미지 샤프닝 알고리듬을 제안한다. 기존의 Sobel이나 Laplacian 연산자는 에지 검출에 있어서 잡음에 취약하다는 단점이 있다. 이를 해결하기 위해 잡음에 상대적으로 민감하지 않은 모폴로지 기법을 이용했다. 우선, 침식 연산을 수행한 이미지와 원본 이미지와의 차를 통해 에지를 얻는다. 이 에지는 원본 이미지의 히스토그램의 표준 편자 값을 기반으로 원본 이미지와 가중합을 통해 에지를 중점적으로 선명하게 만든다. 실험을 통해 제안하는 알고리듬은 기존의 Sobel이나 Laplacian 연산자 보다 우수한 성능을 보임을 알 수 있었다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.11a
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• pp.102-105
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• 2019

본 논문에서는 심층 신경망 검색 방법을 사용하여 이미지 고해상도화를 위한 심층 신경망을 설계하는 방법을 구현하였다. 일반적으로 이미지 고해상도화, 잡음 제거 및 번짐 제거를 위한 심층신경망 구조는 사람이 설계하였다. 최근에는 이미지 분류 등 다른 영상처리 기법에서 사용하는 심층 신경망 구조를 검색하기 위한 방법이 연구되었다. 본 논문에서는 강화학습을 사용하여 이미지 고해상도화를 위한 심층 신경망 구조를 검색하는 방법을 제안하였다. 제안된 방법은 policy gradient 방법의 일종인 REINFORCE 알고리즘을 사용하여 심층 신경망 구조를 출력하여 주는 제어용 RNN(recurrent neural network)을 학습하고, 최종적으로 이미지 고해상도화를 잘 실현할 수 있는 심층 신경망 구조를 검색하여 설계하였다. 제안된 심층 신경망 구조를 사용하여 이미지 고해상도화를 구현하였고, 약 36.54dB 의 피크 신호 대비 잡음 비율(PSNR)을 가지는 것을 확인할 수 있었다.