• 대한의용생체공학회:의공학회지
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• 제41권2호
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• pp.94-100
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• 2020

The purpose of this study was to present the models for classifying the wrist X-ray images by types and for segmenting the radius automatically in each image using deep learning and to verify the learned models. The data were a total of 904 wrist X-rays with the distal radius fracture, consisting of 472 anteroposterior (AP) and 432 lateral images. The learning model was the ResNet50 model for AP/lateral image classification, and the U-Net model for segmentation of the radius. In the model for AP/lateral image classification, 100.0% was showed in precision, recall, and F1 score and area under curve (AUC) was 1.0. The model for segmentation of the radius showed an accuracy of 99.46%, a sensitivity of 89.68%, a specificity of 99.72%, and a Dice similarity coefficient of 90.05% in AP images and an accuracy of 99.37%, a sensitivity of 88.65%, a specificity of 99.69%, and a Dice similarity coefficient of 86.05% in lateral images. The model for AP/lateral classification and the segmentation model of the radius learned through deep learning showed favorable performances to expect clinical application.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제18권1호
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• pp.27-41
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• 2014

In this paper, we present an efficient numerical method for multiphase image segmentation using a multiphase-field model. The method combines the vector-valued Allen-Cahn phase-field equation with initial data fitting terms containing prescribed interface width and fidelity constants. An efficient numerical solution is achieved using the recently developed hybrid operator splitting method for the vector-valued Allen-Cahn phase-field equation. We split the modified vector-valued Allen-Cahn equation into a nonlinear equation and a linear diffusion equation with a source term. The linear diffusion equation is discretized using an implicit scheme and the resulting implicit discrete system of equations is solved by a multigrid method. The nonlinear equation is solved semi-analytically using a closed-form solution. And by treating the source term of the linear diffusion equation explicitly, we solve the modified vector-valued Allen-Cahn equation in a decoupled way. By decoupling the governing equation, we can speed up the segmentation process with multiple phases. We perform some characteristic numerical experiments for multiphase image segmentation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권3호
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• pp.962-975
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• 2020

The accurate segmentation of infant brain MR image into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is very important for early studying of brain growing patterns and morphological changes in neurodevelopmental disorders. Because of inherent myelination and maturation process, the WM and GM of babies (between 6 and 9 months of age) exhibit similar intensity levels in both T1-weighted (T1w) and T2-weighted (T2w) MR images in the isointense phase, which makes brain tissue segmentation very difficult. We propose a deep network architecture based on U-Net, called Triple Residual Multiscale Fully Convolutional Network (TRMFCN), whose structure exists three gates of input and inserts two blocks: residual multiscale block and concatenate block. We solved some difficulties and completed the segmentation task with the model. Our model outperforms the U-Net and some cutting-edge deep networks based on U-Net in evaluation of WM, GM and CSF. The data set we used for training and testing comes from iSeg-2017 challenge (http://iseg2017.web.unc.edu).

• 한국인공지능학회지
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• 제11권4호
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• pp.1-8
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• 2023

In this paper, we propose a black ice detection platform framework using Convolutional Neural Networks (CNNs). To overcome black ice problem, we introduce a real-time based early warning platform using CNN-based architecture, and furthermore, in order to enhance the accuracy of black ice detection, we apply a multi-scale dilation convolution feature fusion (MsDC-FF) technique. Then, we establish a specialized experimental platform by using a comprehensive dataset of thermal road black ice images for a training and evaluation purpose. Experimental results of a real-time black ice detection platform show the better performance of our proposed network model compared to conventional image segmentation models. Our proposed platform have achieved real-time segmentation of road black ice areas by deploying a road black ice area segmentation network on the edge device Jetson Nano devices. This approach in parallel using multi-scale dilated convolutions with different dilation rates had faster segmentation speeds due to its smaller model parameters. The proposed MsCD-FF Net(2) model had the fastest segmentation speed at 5.53 frame per second (FPS). Thereby encouraging safe driving for motorists and providing decision support for road surface management in the road traffic monitoring department.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.740-745
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• 2003

Motion tracking and object segmentation are the most fundamental and critical problems in vision tasks suck as motion analysis. An active contour model, snake, was developed as a useful segmenting and tracking tool for rigid or non-rigid objects. Snake is designed no the basis of snake energies. Segmenting and tracking can be executed successfully by energy minimization. In this research, two new paradigms for segmentation and tracking are suggested. First, because the conventional method uses only intensity information, it is difficult to separate an object from its complex background. Therefore, a new energy and design schemes should be proposed for the better segmentation of objects. Second, conventional snake can be applied in situations where the change between images is small. If a fast moving object exists in successive images, conventional snake will not operate well because the moving object may have large differences in its position or shape, between successive images. Snakes's nodes may also fall into the local minima in their motion to the new positions of the target object in the succeeding image. For robust tracking, the condensation algorithm was adopted to control the parameters of the proposed snake model called "adaptive color snake model(SCSM)". The effectiveness of the ACSM is verified by appropriate simulations and experiments.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2009년도 학술대회
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• pp.193-196
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• 2009

단일 영상에서 피부색 영역을 추출하기 위해서 기존의 많은 방법들이 하나의 고정된 피부색 모델을 사용한다. 그러나 영상에 특성에 따라 영상에 포함된 피부색의 분포가 다양하기 때문에 이러한 방법을 이용하여 피부색을 검출할 경우 낮은 검출율이나 높은 긍정 오류율이 발생할 수 있다. 따라서 영상의 특징에 따라 적응적으로 피부색 영역을 추출할 수 있는 방법이 필요하다. 이에 본 논문에서는 영상의 특징에 따라 2단계의 과정을 거쳐 피부색 모델을 수정하는 방법으로, 다양한 조명과 환경 조건에서 높은 검출율과 낮은 긍정 오류율을 동시에 가지는 알고리즘을 제안한다.

• 한국정밀공학회지
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• 제20권2호
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권8호
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• pp.1996-2015
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• 2023

High-definition (HD) maps can provide precise road information that enables an autonomous driving system to effectively navigate a vehicle. Recent research has focused on leveraging semantic segmentation to achieve automatic annotation of HD maps. However, the existing methods suffer from low recognition accuracy in automatic driving scenarios, leading to inefficient annotation processes. In this paper, we propose a novel semantic segmentation method for automatic HD map annotation. Our approach introduces a new encoder, known as the convolutional transformer hybrid encoder, to enhance the model's feature extraction capabilities. Additionally, we propose a multi-level fusion module that enables the model to aggregate different levels of detail and semantic information. Furthermore, we present a novel decoupled boundary joint decoder to improve the model's ability to handle the boundary between categories. To evaluate our method, we conducted experiments using the Bird's Eye View point cloud images dataset and Cityscapes dataset. Comparative analysis against stateof-the-art methods demonstrates that our model achieves the highest performance. Specifically, our model achieves an mIoU of 56.26%, surpassing the results of SegFormer with an mIoU of 1.47%. This innovative promises to significantly enhance the efficiency of HD map automatic annotation.

• 한국병원경영학회지
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• 제10권4호
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• pp.75-97
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• 2005

The research objective is to empirically investigate the compensation packages maximizing the utilities of internal customers by applying the market segmentation theory. Data was collected from four Korean hospitals in Seoul, Busan and Gyunggi-do. The research is designed to seek the compensation package maximizing the utility of doctors by mixture regression model, which has been applied as latent structure and other type of finite mixture models from various academic fields since early 1980s. The mixture regression model shows the optimal segments number and fuzzy classification for each observation by EM(expectation-maximization algorism). The finite mixture regression model is to unmix the sample, to identify the groups, and to estimate the parameters of the density function underlying the observed data within each group. The doctors were segmented into 5 groups by their preference for the compensation package. The results of this study imply that the utility of doctors increases with differentiated compensation package segmented by their preference.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.2010-2014
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• 2005

Object detection and parameter estimation in point cloud data is a relevant subject to robotics, reverse engineering, computer vision, and sport mechanics. In this paper a software is presented for fully-automatic object detection and parameter estimation in unordered, incomplete and error-contaminated point cloud with a large number of data points. The software consists of three algorithmic modules each for object identification, point segmentation, and model fitting. The newly developed algorithms for orthogonal distance fitting (ODF) play a fundamental role in each of the three modules. The ODF algorithms estimate the model parameters by minimizing the square sum of the shortest distances between the model feature and the measurement points. Curvature analysis of the local quadric surfaces fitted to small patches of point cloud provides the necessary seed information for automatic model selection, point segmentation, and model fitting. The performance of the software on a variety of point cloud data will be demonstrated live.