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

• Tunnel and Underground Space
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• v.33 no.6
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• pp.508-518
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• 2023

This paper presents a new approach for the automatic mapping of discontinuities in a tunnel face based on its 3D digital model reconstructed by LiDAR scan or photogrammetry techniques. The main idea revolves around the identification of discontinuity areas in the 3D digital model of a tunnel face by segmenting its 2D projected images using a deep-learning semantic segmentation model called U-Net. The proposed deep learning model integrates various features including the projected RGB image, depth map image, and local surface properties-based images i.e., normal vector and curvature images to effectively segment areas of discontinuity in the images. Subsequently, the segmentation results are projected back onto the 3D model using depth maps and projection matrices to obtain an accurate representation of the location and extent of discontinuities within the 3D space. The performance of the segmentation model is evaluated by comparing the segmented results with their corresponding ground truths, which demonstrates the high accuracy of segmentation results with the intersection-over-union metric of approximately 0.8. Despite still being limited in training data, this method exhibits promising potential to address the limitations of conventional approaches, which only rely on normal vectors and unsupervised machine learning algorithms for grouping points in the 3D model into distinct sets of discontinuities.

• Progress in Medical Physics
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• v.20 no.2
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• pp.62-71
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• 2009

Recently, optical coherence tomography (OCT) has demonstrated considerable promise for the noninvasive assessment of biological tissues. However, OCT images difficult to analyze due to speckle noise. In this paper, we tested various image processing techniques for speckle removal of human and rabbit cartilage OCT images. Also, we distinguished the images which get with methods of image segmentation for OCT images, and found the most suitable method for segmenting an image. And, we selected image segmentation suitable for OCT before image reconstruction. OCT was a weak point to system design and image processing. It was a limit owing to measure small a distance and depth size. So, good edge matching algorithms are important for image reconstruction. This paper presents such an algorithm, the chamfer matching algorithm. It is made of background for 3D image reconstruction. The purpose of this paper is to describe good image processing techniques for speckle removal, image segmentation, and the 3D reconstruction of cartilage OCT images.

• Journal of Internet Computing and Services
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• v.20 no.6
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• pp.1-10
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• 2019

Object-based image matching algorithms have been widely used in the image processing and computer vision fields. A variety of applications based on image matching algorithms have been recently developed for object recognition, 3D modeling, video tracking, and biomedical informatics. One prominent example of image matching features is the Scale Invariant Feature Transform (SIFT) scheme. However many applications using the SIFT algorithm have implemented based on stand-alone basis, not client-server architecture. In this paper, We initially implemented based on client-server structure by using SIFT algorithms to identify and match objects in biomedical images to provide useful information to the user based on the recently released Mobile platform. The major methodological contribution of this work is leveraging the convenient user interface and ubiquitous Internet connection on Mobile device for interactive delineation, segmentation, representation, matching and retrieval of biomedical images. With these technologies, our paper showcased examples of performing reliable image matching from different views of an object in the applications of semantic image search for biomedical informatics.

• The KIPS Transactions:PartA
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• v.9A no.2
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• pp.217-224
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• 2002

Many systems handle 3D-image were used. High-performance computer systems and techniques of compressing images to handle 3D-image were used. But there will be cost Problems, if GIS system is implemented, using the high-performance system. And if GIS system is implemented, using the techniques of compressing images, there will be some loss of a image. It will take a long processing time to handle 3D-images using a general PC because the size of 3D-image files are very huge. The parallel algorithm presented in the paper can improve speed to handle 3D-image using parallel computer system. The system uses the method of displacing images from nodes to screens, dividing a 3D-image into multiple sub images on multiple nodes. The performance of the presented algorithm showers improving speed by experiments.

• Journal of the Korea Society of Computer and Information
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• v.26 no.2
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• pp.27-37
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• 2021

In this paper, we propose Automatic detection system of underground pipe which automatically detects underground pipe to help experts. Actual location of underground pipe does not match with blueprint due to various factors such as ground changes over time, construction discrepancies, etc. So, various accidents occur during excavation or just by ageing. Locating underground utilities is done through GPR exploration to prevent these accidents but there are shortage of experts, because GPR data is enormous and takes long time to analyze. In this paper, To analyze 3D GPR data automatically, we use 3D image segmentation, one of deep learning technique, and propose proper data generation algorithm. We also propose data augmentation technique and pre-processing module that are adequate to GPR data. In experiment results, we found the possibility for pipe analysis using image segmentation through our system recorded the performance of F1 score 40.4%.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.3
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• pp.10-17
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• 2012

In general, image-based 3D scenes can now be found in many popular vision systems, computer games and virtual reality tours. In this paper, we propose a method for creating 3D virtual scenes based on 2D image that is completely automatic and requires only a single scene as input data. The proposed method is similar to the creation of a pop-up illustration in a children's book. In particular, to estimate geometric structure information for 3D scene from a single outdoor image, we apply the tensor voting to an image segmentation. The tensor voting is used based on the fact that homogeneous region in an image is usually close together on a smooth region and therefore the tokens corresponding to centers of these regions have high saliency values. And then, our algorithm labels regions of the input image into coarse categories: "ground", "sky", and "vertical". These labels are then used to "cut and fold" the image into a pop-up model using a set of simple assumptions. The experimental results show that our method successfully segments coarse regions in many complex natural scene images and can create a 3D pop-up model to infer the structure information based on the segmented region information.

• Journal of KIBIM
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• v.12 no.1
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• pp.1-9
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• 2022

Concrete formwork is a crucial component for any construction project. Artificial intelligence offers great potential to automate formwork design by offering various design options and under different criteria depending on the requirements. This study applied image segmentation in 2D formwork drawings to extract sheathing, strut and pipe support formwork elements. The proposed artificial intelligence model can recognize, classify, and extract formwork elements from 2D CAD drawing image and training and test results confirmed the model performed very well at formwork element recognition with average precision and recall better than 80%. Recognition systems for each formwork element can be implemented later to generate 3D BIM models.

• Journal of Advanced Navigation Technology
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• v.11 no.1
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• pp.59-63
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• 2007

In this paper, an image segmentation algorithm is described to extract both the contour line and the inner grid patterns of body in case of ambiguous environment. The binary method using a threshold is used to extract image boundary. To reduce image noise, the $3{\times}3$ hybrid high pass filter adjusted for applying 3D information extraction of complicated shape object is proposed. This hybrid high pass filter algorithm can be applied to extract complicated shape object such as 3D body shape, CAD system, and factory automation since the processing time for image denoising is shorter than the conventional methods.

• Proceedings of the KAIS Fall Conference
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• 2011.05b
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• pp.761-764
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• 2011

본 연구에서는 대면적 백색광 간섭계의 개발과 개발 되어진 대면적 백색광 간섭계의 고속화를 위하여 Multi-PC를 이용한 동기화 이미지 획득 및 이미지 분할연산과 최적의 Multi-Thread 구성을 통한 영역분할 ROI 알고리즘에 대한 연구결과를 기술하였다.