• Journal of the Optical Society of Korea
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• v.18 no.6
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• pp.714-719
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• 2014

A pico-projector is a compact and mobile projector that has an infinite focus. We apply the pico-projector to a projection-type integral imaging system, which can expand the image depth to form multiple central depth planes. In a projection-type integral imaging system, the image flipping problem arises because the expanded elemental images pass through a lens array. To solve this problem, we propose the ray tracing of a pico-projector at a central depth plane and compensate the elemental image using a pixel-mapping process. Experiments to verify the proposed method are performed, and the results are presented.

• Journal of Broadcast Engineering
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• v.23 no.3
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• pp.403-408
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• 2018

Generally, a large data set is required for learning of deep learning. However, since it is not easy to create large data sets, there are a lot of techniques that make small data sets larger through data expansion such as rotation, flipping, and filtering. However, these simple techniques have limitation on extendibility because they are difficult to escape from the features already possessed. In order to solve this problem, we propose a method to acquire new image data by using existing data. This is done by retrieving and acquiring similar images using existing image data as a query of the content-based image retrieval (CBIR). Finally, we compare the performance of the base model with the model using CBIR.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.85-92
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• 2023

Deep learning applications are increasingly being leveraged for disease detection tasks in medical imaging modalities such as X-ray, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI). Most data-centric deep learning challenges necessitate the use of supervised learning methodologies to attain high accuracy and to facilitate performance evaluation through comparison with the ground truth. Supervised learning mandates a substantial amount of image and label sets, however, procuring an adequate volume of medical imaging data for training is a formidable task. Various data augmentation strategies can mitigate the underfitting issue inherent in supervised learning-based models that are trained on limited medical image and label sets. This research investigates the enhancement of a deep learning-based rib fracture segmentation model and the efficacy of data augmentation techniques such as left-right flipping, rotation, and scaling. Augmented dataset with L/R flipping and rotations(30°, 60°) increased model performance, however, dataset with rotation(90°) and ⨯0.5 rescaling decreased model performance. This indicates the usage of appropriate data augmentation methods depending on datasets and tasks.

• Journal of Astronomy and Space Sciences
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• v.20 no.2
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• pp.153-162
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• 2003

Recently, we have reconstructed LX-200 12inch telescope system for tracking and observing man-made space object. Motor, motor driver and motion controller were up-graded for getting faster respond characteristic and active control available. Also, command signal was offered to the PID controller into motor driver as computed real orbiting information of objects using PC, and it's control period of command signal was reduced to the 20ms. As the result, slew speed of the mount system was in proved up to $18^{\circ}/sec$ and respond speed of the system was faster than that of commercial system. Also, flipping state of image observed could be minimized by the up-graded system.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.604-606
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• 2002

본 논문에서는 3차원 영상의 생성 원리와 3차원 영상 모드 그리고 기존의 3차원 영상 재생 장치를 분석하였다. 3차원 영상 모드에는 interlace page-flipping, sync-doubling 모드가 있으며, LCD(Liquid Crystal Display) shutter glasses를 이용한 3차원 영상 재생 장치의 동작 원리 및 파형을 분석하였고 현 재생 장치의 장. 단점을 논하였다 이 장비는 심한 flicker를 유발하였고, 완전한 3차원 입체 영상의 효과를 얻을 수 없었다. 마지막으로 LCD 모니터용 3차원 영상 재생 장치의 필요성과 향후 연구 방향을 제시하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.6017-6037
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• 2017

In a multi-resolution image encoding system, the image is encoded into a single file as a layer of bit streams, and then it is transmitted layer by layer progressively to reduce the transmission time across a low bandwidth connection. This encoding scheme is also suitable for multiple decoders, each with different capabilities ranging from a handheld device to a PC. In our previous work, we proposed an edge adaptive hierarchical interpolation algorithm for multi-resolution image coding system. In this paper, we enhanced its compression efficiency by adding three major components. First, its prediction accuracy is improved using context adaptive error modeling as a feedback. Second, the conditional probability of prediction errors is sharpened by removing the sign redundancy among local prediction errors by applying sign flipping. Third, the conditional probability is sharpened further by reducing the number of distinct error symbols using error remapping function. Experimental results on benchmark data sets reveal that the enhanced algorithm achieves a better compression bit rate than our previous algorithm and other algorithms. It is shown that compression bit rate is much better for images that are rich in directional edges and textures. The enhanced algorithm also shows better rate-distortion performance and visual quality at the intermediate stages of progressive image transmission.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.167-174
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• 2023

This paper attempted to improve the accuracy of the colorectal cancer diagnosis model using image data augmentation in convolutional neural network. Image data augmentation was performed by flipping, rotation, translation, shearing and zooming with basic image manipulation method. This study split 4000 training data and 1000 test data for 5000 image data held, the model is learned by adding 4000 and 8000 images by image data augmentation technique to 4000 training data. The evaluation results showed that the clasification accuracy for 4000, 8000, and 12,000 training data were 85.1%, 87.0%, and 90.2%, respectively, and the improvement effect depending on the increase of image data was confirmed.

• Tunnel and Underground Space
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• v.29 no.3
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• pp.184-196
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• 2019

This study aims to extract a 3D image of micro-cracks generated by hydraulic fracturing tests, using the deep learning method and X-ray computed tomography images. The pixel-level cracks are difficult to be detected via conventional image processing methods, such as global thresholding, canny edge detection, and the region growing method. Thus, the convolutional neural network-based encoder-decoder network is adapted to extract and analyze the micro-crack quantitatively. The number of training data can be acquired by dividing, rotating, and flipping images and the optimum combination for the image augmentation method is verified. Application of the optimal image augmentation method shows enhanced performance for not only the validation dataset but also the test dataset. In addition, the influence of the original number of training data to the performance of the deep learning-based neural network is confirmed, and it leads to succeed the pixel-level crack detection.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.605-608
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• 1996

The aim of this thesis is to develop a SMT-components placement inspection system equipped with a visual sensor. The visual sensor, which consists of a camera and 2-layer LED illuminator, developed to inspect the component placement state such as missing, shift, flipping, polarity and tomb-stone. on PCB in the reflow-process. In practical applications, however, it is too hard to classify component from images mixed pad on PCB, cream solder paste and component. To overcome the problem, this thesis proposes the 2-layer illumination method and the heuristic image processing algorithms according to inspection type. To show the effectiveness of the proposed approach, a series of experiments on the inspection were conducted. The results show that the proposed method is robust to visual noise and variations in component conditions.

• ETRI Journal
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• v.46 no.3
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• pp.526-537
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• 2024

Monitoring fetal growth in utero is crucial to anomaly diagnosis. However, current computer-vision models struggle to accurately assess the key metrics (i.e., head circumference and occipitofrontal and biparietal diameters) from ultrasound images, largely owing to a lack of training data. Mitigation usually entails image augmentation (e.g., flipping, rotating, scaling, and translating). Nevertheless, the accuracy of our task remains insufficient. Hence, we offer a U-Net fetal head measurement tool that leverages a hybrid Dice and binary cross-entropy loss to compute the similarity between actual and predicted segmented regions. Ellipse-fitted two-dimensional ultrasound images acquired from the HC18 dataset are input, and their lower feature layers are reused for efficiency. During regression, a novel region of interest pooling layer extracts elliptical feature maps, and during segmentation, feature pyramids fuse field-layer data with a new scale attention method to reduce noise. Performance is measured by Dice similarity, mean pixel accuracy, and mean intersection-over-union, giving 97.90%, 99.18%, and 97.81% scores, respectively, which match or outperform the best U-Net models.