• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.822-823
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• 2014

The usual method of generating an image for a multiview display system requires acquiring a color image and depth information of a reference camera. Then, intermediate images, generated using DIBR method, will be captured at a number of different viewpoints and composed to construct an multiview image. When such intermediate views are generated, several holes would be shown because some hidden parts are shown when the screenshot is taken at different angle. Previous research tried to solve this problem by creating a new hole-filling algorithm or enhancing the depth information. This paper describes a new method of enhancing the intermediate view images by applying the Ball Pivoting algorithm, which constructs meshes from a point cloud. When the new method is applied to the Microsoft's "Ballet" and "Break Dancer" data sets, PSNR comparison shows that about 0.18~1.19 increasement. This paper will explaing the new algorithm and the experiment method and results.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.436-438
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• 2019

Air hockey robots are machine vision systems that allow users to play hockey balls through the camera. The position detection of the hockey ball is realized by using the color information of the ball using OpenCV library. It senses the position of the hockey ball, predicts its trajectory, and sends the result to the ARM Cortex-M board. The ARM Cortex-M board controls a 2- Axis Cartesian Coordinate Robot to run an air hockey game. Depending on the strategy of the air hockey robot, it can operate in defensive, offensive, defensive and offensive mode. In this paper, we describe a vision system development and trajectory prediction system and propose a new method to control a biaxial orthogonal robot in an air hockey game.

• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.29-35
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• 2021

License plate recognition plays a key role in intelligent transportation systems. Therefore, it is a very important process to efficiently detect the number and character areas. In this paper, we propose a method to effectively detect license plate number area by applying deep learning and semantic image segmentation algorithm. The proposed method is an algorithm that detects number and text areas directly from the license plate without preprocessing such as pixel projection. The license plate image was acquired from a fixed camera installed on the road, and was used in various real situations taking into account both weather and lighting changes. The input images was normalized to reduce the color change, and the deep learning neural networks used in the experiment were Vgg16, Vgg19, ResNet18, and ResNet50. To examine the performance of the proposed method, we experimented with 500 license plate images. 300 sheets were used for learning and 200 sheets were used for testing. As a result of computer simulation, it was the best when using ResNet50, and 95.77% accuracy was obtained.

• Journal of dental hygiene science
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• v.21 no.4
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• pp.219-226
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• 2021

Background: Proper detection and management of dental plaque are essential for individual oral health. We aimed to evaluate the maturation level of dental plaque using a two-tone disclosing agent and to compare it with the fluorescence of dental plaque on the quantitative light-induced fluorescence (QLF) image to obtain primary data for the development of a new dental plaque scoring system. Methods: Twenty-eight subjects who consented to participate after understanding the purpose of the study were screened. The images of the anterior teeth were obtained using the QLF device. Subsequently, dental plaque was stained with a two-tone disclosing solution and a photograph was obtained with a digital single-lens reflex (DSLR) camera. The staining scores were assigned as follows: 0 for no staining, 1 for pink staining, and 2 for blue staining. The marked points on the DSLR images were selected for RGB color analysis. The relationship between dental plaque maturation and the red/green (R/G) ratio was evaluated using Spearman's rank correlation. Additionally, different red fluorescence values according to dental plaque accumulation were assessed using one-way analysis of variance followed by Scheffe's post-hoc test to identify statistically significant differences between the groups. Results: A comparison of the intensity of red fluorescence according to the maturation of the two-tone stained dental plaque confirmed that R/G ratio was higher in the QLF images with dental plaque maturation (p<0.001). Correlation analysis between the stained dental plaque and the red fluorescence intensity in the QLF image confirmed an excellent positive correlation (p<0.001). Conclusion: A new plaque scoring system can be developed based on the results of the present study. In addition, these study results may also help in dental plaque management in the clinical setting.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.1-8
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• 2022

Recently, Google MedeiaPipe presents a novel approach for neural network-based hair segmentation from a single camera input specifically designed for real-time, mobile application. Though neural network related to hair segmentation is relatively small size, it produces a high-quality hair segmentation mask that is well suited for AR effects such as a realistic hair recoloring. However, it has undesirable segmentation effects according to hair styles or in case of containing noises and holes. In this study, the energy function of the test image is constructed according to the estimated prior distributions of hair location and hair color likelihood function. It is further optimized according to graph cuts algorithm and initial hair region is obtained. Finally, clustering algorithm and image post-processing techniques are applied to the initial hair region so that the final hair region can be segmented precisely. The proposed method is applied to MediaPipe hair segmentation pipeline.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.87-93
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• 2023

In this paper, we propose a system that accurately and efficiently determines forward head posture based on network learning by analyzing the user's skeletal posture. Forward head posture syndrome is a condition in which the forward head posture is changed by keeping the neck in a bent forward position for a long time, causing pain in the back, shoulders, and lower back, and it is known that daily posture habits are more effective than surgery or drug treatment. Existing methods use convolutional neural networks using webcams, and these approaches are affected by the brightness, lighting, skin color, etc. of the image, so there is a problem that they are only performed for a specific person. To alleviate this problem, this paper extracts the skeleton from the image and learns the data corresponding to the side rather than the frontal view to find the forward head posture more efficiently and accurately than the previous method. The results show that the accuracy is improved in various experimental scenes compared to the previous method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.463-465
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• 2021

Currently, the inspection method printed on food packages and boxes is to sample only a few products and inspect them with human eyes. Such a sampling inspection has the limitation that only a small number of products can be inspected. Therefore, accurate inspection using a camera is required. This paper proposes a deep learning object recognition technology model, which is an artificial intelligence technology, as a method for detecting the defects of expiration date printed on the product packaging. Using the Faster R-CNN (region convolution neural network) model, the color images, converted gray images, and converted binary images of the printed expiration date are trained and then tested, and each detection rates are compared. The detection performance of expiration date printed on the package by the proposed method showed the same detection performance as that of conventional vision-based inspection system.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.253-258
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• 2023

In comics, panels serve a variety of dramatic purposes, including controlling the flow of the story through the size, placement, and order of panels; directing the eye to where characters or specific actions take place; expressing the passage or sequence of time; and using backgrounds, lighting, and color to emphasize emotion and convey mood. When applied to VR animation, these comic panels can give users a more immersive experience of the cartoon world in the VR space. In the VR animation "Mescaformhill: Missing Five," produced in 2022, the comic panels directing is very important to guide the viewer's attention, and in the VR space, the camera angle can be freely changed to realize various visual effects, and the physical interaction brings the comic panels directing to life. This can open up new ways of storytelling in VR animation and contribute to its successful expansion into new mediums.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.869-876
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• 2006

In the case of earthenware cultural heritages that are found in the form of fragments, the major task is quick and precise restoration. The existing method, which follows the rule of trial and error, is not only greatly time consuming but also lacked precision. If this job could be done by three dimensional scanning, matching up pieces could be done with remarkable efficiency. In this study, the original earthenware was modeled through three-dimensional pattern scanning and photogrammetry, and each of the fragments were scanned and modeled. In order to obtain images from the photogrammetry, we calibrated and used a Canon EOS 1DS real size camera. We analyzed the relationship among the sections of the formed model, efficiently compounded them, and analyzed the errors through residual and color error map. Also, we built a web-based three-dimensional simulation environment centering around the users, for the virtual museum.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.75-75
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• 2022

Traditional agriculture mostly focused on activity in the field, but current agriculture faces problems such as reduction of agricultural inputs, labor shortage and so on. Accordingly, traditional agricultural experiments generally considered the simple treatment effects, but current agricultural experiments need to consider the several and complicate treatment effects. To analyze such several and complicate treatment effects, data collection has the first priority. Remote sensing is a quite effective tool to collect information in agriculture, and recent easier availability of UAVs (Unmanned Aerial Vehicles) enhances the effectiveness. LAI (Leaf Area Index) is one of the most important information for evaluating the condition of crop growth. In this study, we utilized UAV with multispectral camera to evaluate plant-based LAI of sweetcorn in a small-scale field experiment and discussed the feasibility of a new experimental design to analyze the several and complicate treatment effects. The plant-based SR measured by UAV showed the highest correlation coefficient with LAI measured by a canopy analyzer in 2018 and 2019. Application of linear mix model showed that plant-based SR data had higher detection power due to its huge number of data although SR was inferior to evaluate LAI than the canopy analyzer. The distribution of plant-based data also statistically revealed the border effect in treatment plots in the traditional experimental design. These results suggest that remote sensing with UAVs has the advantage even in a small-scale experimental plot and has a possibility to provide a new experimental design if combined with various analytical applications such as plant size, shape, and color.