• The Journal of Advanced Prosthodontics
• /
• v.11 no.2
• /
• pp.138-146
• /
• 2019

PURPOSE. Comparing the accuracy of casts produced from digital workflow to that of casts produced from conventional techniques. MATERIALS AND METHODS. Whole arch alginate (ALG) and polyvinyl siloxane (PVS) impressions were taken with stock trays and custom trays, respectively. The ALG impressions were poured with type III dental stone, while the PVS impressions were poured with type IV dental stone. For the digital workflow, IOS impressions were taken and physical casts were produced by 3D printing. In addition, 3D printed casts were produced from images obtained from a laboratory scanner (LS). For each technique, a total of 10 casts were produced. The accuracies of the whole arch and separated teeth were virtually quantified. RESULTS. Whole arch cast accuracy was more superior for PVS followed by LS, ALG, and IOS. The PVS and ALG groups were inferior in the areas more susceptible to impression material distortion, such as fossae and undercut regions. The LS casts appeared to have generalized errors of minor magnitude influencing primarily the posterior teeth. The IOS casts were considerably more affected at the posterior region. On the contrary, the IOS and LS casts were more superior for single tooth accuracy followed by PVS and ALG. CONCLUSION. For whole arch accuracy, casts produced from IOS were inferior to those produced from PVS and ALG. The inferior outcome of IOS appears to be related to the span of scanning. For single tooth accuracy, IOS showed superior accuracy compared to conventional impressions.

• Journal of the Korean Society for Precision Engineering
• /
• v.30 no.9
• /
• pp.991-999
• /
• 2013

Lower limbs deformity is a congenital disease and can also be occurred by an acquired factor. This paper suggests a new technique for surgical planning of Corrective Osteotomy for Lower Limbs (COLL) using 2D-3D medical image registration. Converting to a 3D modeling data of lower limb based on CT (computed tomography) scan, and divide it into femur, tibia and fibula; which composing the lower limb. By rearranging the model based on the biplane 2D images of X-ray data, a 3D upright bone structure was acquired. There are two ways to array the 3D data on the 2D image: Intensity-based registration and feature-based registration. Even though registering Intensity-based method takes more time, this method will provide more precise results, and will improve the accuracy of surgical planning.

• Smart Structures and Systems
• /
• v.32 no.3
• /
• pp.135-151
• /
• 2023

Using 3D asphalt pavement surface data, a deep and multiscale network named CrackNet-M is proposed in this paper for pixel-level crack detection for improvements in both accuracy and robustness. The CrackNet-M consists of four function-specific architectural modules: a central branch net (CBN), a crack map enhancement (CME) module, three pooling feature pyramids (PFP), and an output layer. The CBN maintains crack boundaries using no pooling reductions throughout all convolutional layers. The CME applies a pooling layer to enhance potential thin cracks for better continuity, consuming no data loss and attenuation when working jointly with CBN. The PFP modules implement direct down-sampling and pyramidal up-sampling with multiscale contexts specifically for the detection of thick cracks and exclusion of non-crack patterns. Finally, the output layer is optimized with a skip layer supervision technique proposed to further improve the network performance. Compared with traditional supervisions, the skip layer supervision brings about not only significant performance gains with respect to both accuracy and robustness but a faster convergence rate. CrackNet-M was trained on a total of 2,500 pixel-wise annotated 3D pavement images and finely scaled with another 200 images with full considerations on accuracy and efficiency. CrackNet-M can potentially achieve crack detection in real-time with a processing speed of 40 ms/image. The experimental results on 500 testing images demonstrate that CrackNet-M can effectively detect both thick and thin cracks from various pavement surfaces with a high level of Precision (94.28%), Recall (93.89%), and F-measure (94.04%). In addition, the proposed CrackNet-M compares favorably to other well-developed networks with respect to the detection of thin cracks as well as the removal of shoulder drop-offs.

• Journal of the Semiconductor & Display Technology
• /
• v.21 no.2
• /
• pp.68-73
• /
• 2022

In this study, we produced the surface defect detection model to automatically detect defect bone plates using a deep learning algorithm. Bone plates with a width and a length of 50 mm are most used for fracture treatment. Normal bone plates and defective bone plates were printed on the 3d printer. Normal bone plates and defective bone plates were photographed with 1,080 pixels using the webcam. The total quantity of collected images was 500. 300 images were used to learn the defect detection model. 200 images were used to test the defect detection model. The mAP(Mean Average Precision) method was used to evaluate the performance of the surface defect detection model. As the result of confirming the performance of the surface defect detection model, the detection accuracy was 96.3 %.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.64 no.1
• /
• pp.15-26
• /
• 2022

3D open-field farm model developed from UAV (Unmanned Aerial Vehicle) data could make crop monitoring easier, also could be an important dataset for various fields like remote sensing or precision agriculture. It is essential to separate crops from the non-crop area because labeling in a manual way is extremely laborious and not appropriate for continuous monitoring. We, therefore, made a 3D open-field farm model based on UAV images and developed a crop segmentation model using a supervised machine learning algorithm. We compared performances from various models using different data features like color or geographic coordinates, and two supervised learning algorithms which are SVM (Support Vector Machine) and KNN (K-Nearest Neighbors). The best approach was trained with 2-dimensional data, ExGR (Excess of Green minus Excess of Red) and z coordinate value, using KNN algorithm, whose accuracy, precision, recall, F1 score was 97.85, 96.51, 88.54, 92.35% respectively. Also, we compared our model performance with similar previous work. Our approach showed slightly better accuracy, and it detected the actual crop better than the previous approach, while it also classified actual non-crop points (e.g. weeds) as crops.

• Journal of Broadcast Engineering
• /
• v.17 no.2
• /
• pp.411-421
• /
• 2012

The stagnant growth in 3D market due to 3D movie contents shortage is encouraging development of techniques for production cost reduction. Elimination of vertical disparity generated during image acquisition requires heaviest time and effort in the whole stereoscopic film-making process. This matter is directly related to competitiveness in the market and is being dealt with as a very important task. The removal of vertical disparity, i.e. image rectification has been treated for a long time in the photogrammetry field. While computer vision methods are focused on fast processing and automation, photogrammetry methods on accuracy and precision. However, photogrammetric approaches have not been tried for the 3D film-making. In this paper, proposed is a photogrammetry-based rectification algorithm that enable to eliminate the vertical disparity precisely by reconstruction of geometric relationship at the time of shooting. Evaluation of proposed algorithm was carried out by comparing the performance with two existing computer vision algorithms. The epipolar constraint satisfaction, epipolar line accuracy and vertical disparity of result images were tested. As a result, the proposed algorithm showed excellent performance than the other algorithms in term of accuracy and precision, and also revealed robustness about position error of tie-points.

• Journal of the Korean Society for Precision Engineering
• /
• v.29 no.1
• /
• pp.33-40
• /
• 2012

The scaffold serves as 3D substrate for the cells adhesion and mechanical support for the newly grown tissue by maintaining the 3D structure for the regeneration of tissue and organ. In this paper, we proposed integrated scaffold fabrication system using multi-axis rapid prototyping (RP) technology. It can fabricate various types of scaffolds: arbitrary sculptured shape, primitive shape, and tube shape scaffolds by layered dispensing biocompatible/ biodegradable polymer strands in designated patterns. In order to fabricate the 3D scaffold, we need to generate the plotting path way for the scaffold fabrication system. We design a data processing program - scaffold plotting software, which can convert the 3D STL file, primitive and tube model images into the NC code for the system. Finally, we fabricated the customized 3D scaffolds with high accuracy using the plotting software and the fabrication system.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.37 no.2
• /
• pp.143-149
• /
• 2024

Segmentation of material phases through image analysis is essential for analyzing the microstructure of materials. Micro-CT images exhibit variations in grayscale values depending on the phases constituting the material. Phase segmentation is generally achieved by comparing the grayscale values in the images. In the case of waste concrete used as a recycled aggregate, it is challenging to distinguish between hydrated cement paste and natural aggregates, as these components exhibit similar grayscale values in micro-CT images. In this study, we propose a method for automatically separating the aggregates in concrete, in micro-CT images. Utilizing the Unet-VGG16 deep-learning network, we introduce a technique for segmenting the 2D aggregate images and stacking them to obtain 3D aggregate images. Image filtering is employed to separate aggregate particles from the selected 3D aggregate images. The performance of aggregate segmentation is validated through accuracy, precision, recall, and F1-score assessments.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2004.11a
• /
• pp.529-534
• /
• 2004

The increasing need and use of 3D GIS particularly in urban areas has produced growing attention on building reconstruction. Nowadays, the use of close-range data for building reconstruction has been intensively emphasized since they can provide higher resolution and more complete coverage than airborne sensory data. We developed a fusion approach for building reconstruction from both points and images. The proposed approach was then applied to reconstructing a building model from real data sets acquired from a large existing building. Based on the experimental results, we assured that the proposed approach cam achieve high resolution and accuracy in building reconstruction. The proposed approach can effectively contribute in developing an operational system producing large urban models for 3D GIS.

• Journal of Urban Science
• /
• v.6 no.2
• /
• pp.17-27
• /
• 2017

Recently, civil complaints have increased due to water pollution and bad smell in rivers. Therefore, attention is focused on improving the river environment. The purpose of this study is to acquire RGB and thermal infrared images using UAV for sewage outlet and to monitor the status of stream pollution and the applicability UAV based images for river embankment maintenance plan was examined. The accuracy of the 3D model was examination by SfM(Structure from Motion) based images analysis on river embankment maintenance area. Especially, The wastewater discharged from the factory near the river was detected as an thermal infrared images and the flow of wastewater was monitored. As a result of the study, we could monitor the cause and flows of wastewater pollution by detecting temperature change caused by wastewater inflow using UAV images. In addition, UAV based a high precision 3D model (DTM, Digital Topographic Map, Orthophoto Mosaic) was produced to obtain precise DSM(Digital Surface Model) and vegetation cover information for river embankment maintenance.