• Journal of Advanced Navigation Technology
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• v.25 no.5
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• pp.370-376
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• 2021

development of the aerospace industry is increasing the research of projectiles. In addition, many tests are under way and many failures occur accordingly. Projectiles should be able to minimize failures because they are more dangerous than other electronic equipment. Therefore, it is necessary to verify wiring before powering the projectile. Accordingly, the wiring status was verified by resistance measurements. However, the wiring test of the previous resistance measurement method cannot be accurately measured due to devices such as capacitors and inductors in the projectile circuit. In this paper, impedance is measured in the connection state of cables and projectiles using a TDR meter. The Z-Segmentation algorithm is used to set the reference value for the measured steady state impedance. The Z-Segmentation algorithm first finds the peak values of the impedance waveform using a Kalman filter and obtains the final impedance peak segment through segmentation. In this way, the wiring status is determined based on the reference value for the normal state of the wiring.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.2
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• pp.176-183
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• 2003

The paper presents a feature recognition and segmentation method for surface approximation in reverse engineering. Efficient digitizing plays an important role in constructing a computational surface model from a physical part-surface without its CAD model on hand. Depending on its measuring source (e.g., touch probe or structured light), each digitizing method has its own strengths and weaknesses in terms of speed and accuracy. The final goal of the research focuses on an integration of two different digitizing methods: measuring by the structured light and that by the touch probe. Gathering bulk of digitized points (j.e., cloud-of-points) by use of a laser scanning system, we construct a coarse surface model directly from the cloud-of-points, followed by the segmentation process where we utilize the z-map filleting & differencing to trace out feature boundary curves. The feature boundary curves and the approximate surface model could be inputs to further digitizing by a scanning touch probe. Finally, more accurate measuring points within the boundary curves can be obtained to construct a finer surface model.

• Journal of International Society for Simulation Surgery
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• v.2 no.2
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• pp.67-70
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• 2015

We propose an automatic vessel segmentation method of vertebral arteries in CT angiography using combined circular and cylindrical model fitting. First, to generate multi-segmented volumes, whole volume is automatically divided into four segments by anatomical properties of bone structures along z-axis of head and neck. To define an optimal volume circumscribing vertebral arteries, anterior-posterior bounding and side boundaries are defined as initial extracted vessel region. Second, the initial vessel candidates are tracked using circular model fitting. Since boundaries of the vertebral arteries are ambiguous in case the arteries pass through the transverse foramen in the cervical vertebra, the circle model is extended along z-axis to cylinder model for considering additional vessel information of neighboring slices. Finally, the boundaries of the vertebral arteries are detected using graph-cut optimization. From the experiments, the proposed method provides accurate results without bone artifacts and eroded vessels in the cervical vertebra.

• Journal of Korea Multimedia Society
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• v.15 no.12
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• pp.1417-1429
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• 2012

In neuroscientific research, image segmentation is one of the most important processes. The morphology of axons plays an important role for researchers seeking to understand axonal functions and connectivity. In this study, we evaluated the level set segmentation method for neuronal axons in a Brainbow confocal microscopy image. We first obtained a reconstructed image on an x-z plane. Then, for preprocessing, we also applied two methods: anisotropic diffusion filtering and bilateral filtering. Finally, we performed image segmentation using the level set method with three different approaches. The accuracy of segmentation for each case was evaluated in diverse ways. In our experiment, the combination of bilateral filtering with the level set method provided the best result. Consequently, we confirmed reasonable results with our approach; we believe that our method has great potential if successfully combined with other research findings.

• Journal of Electrical Engineering and Technology
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• v.9 no.4
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• pp.1426-1436
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• 2014

In this article, we present a new grey scale image segmentation method based on Fuzzy logic and bitplane techniques which combines the bits of different bitplanes of a pixel inorder to increase the segmentation quality and to get a more reliable and accurate segmentation result. The proposed segmentation approach is conceptually different and explores a new strategy. Infact, our technique consists in combining many realizations of the image together inorder to increase the information quality and to get an optimal segmented image. For segmentation, we proceed in two steps. In the first step, we begin by identifying the bitplanes that represent the lungs clearly. For this purpose, the intensity value of a pixel is separated into bitplanes. In the second step, segmentation values are assigned for each bitplane based on membership table. The segmented values of foreground are combined and the segmentation values of background are combined. The algorithm is demonstrated through the medical computed tomography (CT) images. The segmentation accuracy of the proposed method is compared with two existing techniques. Satisfactory segmentation results have been obtained showing the effectiveness and superiority of the proposed method.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.436-442
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• 2017

A novel method of Skin lesion segmentation based on the combination of Texture and Neural Network is proposed in this paper. This paper combines the textures of different pixels in the skin images in order to increase the performance of lesion segmentation. For segmenting skin lesions, a two-step process is done. First, automatic border detection is performed to separate the lesion from the background skin. This begins by identifying the features that represent the lesion border clearly by the process of Texture analysis. In the second step, the obtained features are given as input towards the Recurrent Echo state neural networks in order to obtain the segmented skin lesion region. The proposed algorithm is trained and tested for 862 skin lesion images in order to evaluate the accuracy of segmentation. Overall accuracy of the proposed method is compared with existing algorithms. An average accuracy of 98.8% for segmenting skin lesion images has been obtained.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.51-56
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• 2022

This paper presents an Ground Segmentation algorithm to eliminate unnecessary Lidar Point Cloud Data (PCD) in an autonomous driving system. We consider Random Sample Consensus (Ransac) Algorithm to process lidar ground data. Ransac designates inlier and outlier to erase ground point cloud and classified PCD into two parts. Test results show removal of PCD from ground area by distinguishing inlier and outlier. The paper validates ground rejection algorithm in real time calculating the number of objects recognized by ground data compared to lidar raw data and ground segmented data based on the z-axis. Ground Segmentation is simulated by Robot Operating System (ROS) and an analysis of autonomous driving data is constructed by Matlab. The proposed algorithm can enhance performance of autonomous driving as misrecognizing circumstances are reduced.

• Korean Journal of Remote Sensing
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• v.36 no.2_1
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• pp.199-215
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• 2020

A seagrass that is marine vascular plants plays an important role in the marine ecosystem, so periodic monitoring ofseagrass habitatsis being performed. Recently, the use of dronesthat can easily acquire very high-resolution imagery is increasing to efficiently monitor seagrass habitats. And deep learning based on a convolutional neural network has shown excellent performance in semantic segmentation. So, studies applied to deep learning models have been actively conducted in remote sensing. However, the segmentation accuracy was different due to the hyperparameter, various deep learning models and imagery. And the normalization of the image and the tile and batch size are also not standardized. So,seagrass habitats were segmented from drone-borne imagery using a deep learning that shows excellent performance in this study. And it compared and analyzed the results focused on normalization and tile size. For comparison of the results according to the normalization, tile and batch size, a grayscale image and grayscale imagery converted to Z-score and Min-Max normalization methods were used. And the tile size isincreased at a specific interval while the batch size is allowed the memory size to be used as much as possible. As a result, IoU was 0.26 ~ 0.4 higher than that of Z-score normalized imagery than other imagery. Also, it wasfound that the difference to 0.09 depending on the tile and batch size. The results were different according to the normalization, tile and batch. Therefore, this experiment found that these factors should have a suitable decision process.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.1
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• pp.151-162
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• 2022

LX Hausys, a leading manufacturer of building materials in South Korea is engaging in the interior business where it is critical to balance between the needs of end-users (B2C) and the needs of corporate consumers (B2B) in an effective manner. Therefore, it is utmost important for the company to ensure that customer communication takes place in two directions based on correct market segmentation strategies, which in turn require a deep understanding of current as well as potential consumers. To accomplish this, LX Hausys conducted both quantitative and qualitative studies to establish a market segmentation strategy which is genuinely different from "general" consumer goods market segmentation strategies. As a result, especially since 2018, its brand (LX Z:IN) began to move up to 1st or 2nd place in a variety of brand rankings. This paper aims to look closely into various characteristic aspects of LXH's market segmentation strategy, and also shows how it is implemented in the real world.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.939-951
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• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.