• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.279-282
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• 2017

Kinect depth sensor, a depth camera developed by Microsoft as a natural user interface for game appeared as a very useful tool in computer vision field. In this paper, due to kinect's depth sensor and its high frame rate, we developed a distance measurement system using Kinect camera to test it for unmanned vehicles which need vision systems to perceive the surrounding environment like human do in order to detect objects in their path. Therefore, kinect depth sensor is used to detect objects in its field of view and enhance the distance measurement system from objects to the vision sensor. Detected object is identified in accuracy way to determine if it is a real object or a pixel nose to reduce the processing time by ignoring pixels which are not a part of a real object. Using depth segmentation techniques along with Open CV library for image processing, we can identify present objects within Kinect camera's field of view and measure the distance from them to the sensor. Tests show promising results that this system can be used as well for autonomous vehicles equipped with low-cost range sensor, Kinect camera, for further processing depending on the application type when they reach a certain distance far from detected objects.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.3188-3207
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• 2017

In this paper, we propose an image enhancement algorithm to improve license plate extraction rate in various environments (Day Street, Night Street, Underground parking lot, etc.). The proposed algorithm is composed of image enhancement algorithm and license plate extraction algorithm. The image enhancement method can improve an image quality of the degraded image, which utilizes a histogram information and overall gray level distribution of an image. The proposed algorithm employs an interpolated probability distribution value (PDV) in order to control a sudden change in image brightness. Probability distribution value can be calculated using cumulative distribution function (CDF) and probability density function (PDF) of the captured image, whose values are achieved by brightness distribution of the captured image. Also, by adjusting the image enhancement factor of each part region based on image pixel information, it provides a function that can adjust the gradation of the image in more details. This processed gray image is converted into a binary image, which fuses narrow breaks and long thin gulfs, eliminates small holes, and fills gaps in the contour by using morphology operations. Then license plate region is detected based on aspect ratio and license plate size of the bound box drawn on connected license plate areas. The images have been captured by using a video camera or a personal image recorder installed in front of the cars. The captured images have included several license plates on multilane roads. Simulation has been executed using OpenCV and MATLAB. The results show that the extraction success rate is more improved than the conventional algorithms.

• Annual Conference of KIPS
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• 2019.05a
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• pp.152-155
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• 2019

클라우드가 대중화되어 많은 모바일 유저들이 자동 백업 기능을 사용하면서 민감한 개인정보가 포함된 사진들이 무분별하게 클라우드에 업로드 되고 있다. 개인정보를 포함한 클라우드가 악의적으로 해킹 될 시, 사진에 포함된 지문, 자동차 번호판, 카드 번호 등이 유출됨에 따라 대량의 개인정보가 유출될 가능성이 크다. 이에 따라 적절한 기준에 맞게 사진 속 개인 정보 유출을 막을 수 있는 기술의 필요성이 대두되고 있다. 현재의 클라우드 시스템의 문제를 해결하고자 본 연구는 모바일 기기에서 클라우드 서버로 사진을 백업하는 과정에서 영역 검출과 블러링의 과정을 제안하고 있다. 클라우드 업로드 과정에서 사진 속의 개인 정보를 검출한 뒤 이를 블러링하여 클라우드에 저장함으로써 악의적인 접근이 행해지더라도 개인정보의 유출을 방지할 수 있다. 머신러닝과 computer vision library등을 이용하여 이미지 내에 민감한 정보를 포함하고 있는 영역을 학습된 모델을 통해 검출한 뒤, OpenCV를 이용하여 블러링처리를 진행한다 사진 속에 포함될 수 있는 생체정보인 지문은 손 영역을 검출한 뒤, 해당 영역을 블러링을 하여 업로드하고 카드번호나 자동차 번호판이 포함된 사진은 영역을 블러링한 뒤, 암호화하여 업로드 된다. 후에 필요에 따라 본인인증을 거친 후 일정기간 열람을 허용하지만 사용되지 않을 경우 삭제되도록 한다. 개인정보 유출로 인한 피해가 꾸준히 증가하고 있는 지금, 사진 속의 개인 정보를 보호하는 기술은 안전한 통신과 더불어 클라우드의 사용을 더 편리하게 할 수 있을 것으로 기대된다.

• Korean Journal of Materials Research
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• v.32 no.8
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• pp.345-353
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• 2022

In this study, using deep learning, super-resolution images of transmission electron microscope (TEM) images were generated for nanomaterial analysis. 1169 paired images with 256 × 256 pixels (high resolution: HR) from TEM measurements and 32 × 32 pixels (low resolution: LR) produced using the python module openCV were trained with deep learning models. The TEM images were related to DyVO₄ nanomaterials synthesized by hydrothermal methods. Mean-absolute-error (MAE), peak-signal-to-noise-ratio (PSNR), and structural similarity (SSIM) were used as metrics to evaluate the performance of the models. First, a super-resolution image (SR) was obtained using the traditional interpolation method used in computer vision. In the SR image at low magnification, the shape of the nanomaterial improved. However, the SR images at medium and high magnification failed to show the characteristics of the lattice of the nanomaterials. Second, to obtain a SR image, the deep learning model includes a residual network which reduces the loss of spatial information in the convolutional process of obtaining a feature map. In the process of optimizing the deep learning model, it was confirmed that the performance of the model improved as the number of data increased. In addition, by optimizing the deep learning model using the loss function, including MAE and SSIM at the same time, improved results of the nanomaterial lattice in SR images were achieved at medium and high magnifications. The final proposed deep learning model used four residual blocks to obtain the characteristic map of the low-resolution image, and the super-resolution image was completed using Upsampling2D and the residual block three times.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.3
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• pp.521-526
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• 2023

Due to the recent pandemic, most educational systems are being conducted through online classes. Unlike face-to-face classes, it is even more difficult for learners to maintain concentration, and evaluating the learners' attitude toward the class is also challenging. In this paper, we proposed a real-time concentration-based review support system for learners in real-time video lectures that can be used in online classes. This system measured the learner's face, pupils, and user activity in real-time using the equipment used in the existing video system, and delivers real-time concentration measurement values to the instructor in various forms. At the same time, if the concentration measurement value falls below a certain level, the system alerted the learner and records the timestamp of the lecture. By using this system, instructors can evaluate the learners' participation in the class in real-time and help to improve their class abilities.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.2
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• pp.291-298
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• 2022

A generative adversarial network (GAN) is a network in which two internal neural networks (generative network and discriminant network) learn while competing with each other. The generator creates an image close to reality, and the delimiter is programmed to better discriminate the image of the constructor. This technology is being used in various ways to create, transform, and restore the entire image X into another image Y. This paper describes a method that can be forged into another object naturally, after extracting only a partial image from the original image. First, a new image is created through the previously trained DCGAN model, after extracting only a partial image from the original image. The original image goes through a process of naturally combining with, after re-styling it to match the texture and size of the original image using the overall style transfer technique. Through this study, the user can naturally add/transform the desired object image to a specific part of the original image, so it can be used as another field of application for creating fake images.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.535-543
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• 2022

A drone is used to diagnose crop growth and to provide information through images in the agriculture field. In the case of using high spatial resolution drone images, growth information for each object can be produced. However, accurate object detection is required and adjacent objects should be efficiently classified. The purpose of this study is to develop a Chinese cabbage object detection algorithm using multispectral reflectance images observed from drone and computer vision techniques. Drone images were captured between 7 and 15 days after planting a Chinese cabbage from 2018 to 2020 years. The thresholds of object detection algorithm were set based on 2019 year, and the algorithm was evaluated based on images in 2018 and 2019 years. The vegetation area was classified using the characteristics of spectral reflectance. Then, morphology techniques such as dilatation, erosion, and image segmentation by considering the size of the object were applied to improve the object detection accuracy in the vegetation area. The precision of the developed object detection algorithm was over 95.19%, and the recall and accuracy were over 95.4% and 93.68%, respectively. The F1-Score of the algorithm was over 0.967 for 2 years. The location information about the center of the Chinese cabbage object extracted using the developed algorithm will be used as data to provide decision-making information during the growing season of crops.