• Journal of Biomedical Engineering Research
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• v.42 no.5
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• pp.201-210
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• 2021

In Korean medicine, tongue diagnosis is one of the important diagnostic methods for diagnosing abnormalities in the body. Representative features that are used in the tongue diagnosis include color, shape, texture, cracks, and tooth marks. When diagnosing a patient through these features, the diagnosis criteria may be different for each oriental medical doctor, and even the same person may have different diagnosis results depending on time and work environment. In order to overcome this problem, recent studies to automate and standardize tongue diagnosis using machine learning are continuing and the basic process of such a machine learning-based tongue diagnosis system is tongue segmentation. In this paper, image data is augmented based on the main tongue features, and backbones of various famous deep learning architecture models are used for automatic tongue segmentation. The experimental results show that the proposed augmentation technique improves the accuracy of tongue segmentation, and that automatic tongue segmentation can be performed with a high accuracy of 99.12%.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.463-467
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• 2006

Several segmentation algorithms have been proposed for DTM generation or building modeling from airborne LiDAR data. Three components are important for accurate segmentation: (i) the adjacent relationship of n-nearest points or mesh, etc. (ii) the effective decision parameters of height, slope, curvature, and plane condition, (iii) grouping methods. In this paper, we created the topology of point cloud data using the contour tree and implemented the region-growing Terrain and non-terrain points were classified correctly in the segmented data, which can be used also for feature classification.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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• pp.67-68
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• 2010

Segmentation of LiDAR data is an important procedure in building modeling. Therefore, in this study, aerial imagery is used to group LiDAR data for both improving segmentation accuracy and modeling detail surface patches of the roofs. The results show that the proposed method is efficient to analyze and to model various types of roof shape.

• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1407-1416
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• 2018

The purpose of this study was to train a model for the ulna and radius bone segmentation based on Convolutional Neural Networks and to verify the segmentation model. The data consisted of 840 training data, 210 tuning data, and 200 verification data. The learning model for the ulna and radius bone bwas based on U-Net (19 convolutional and 8 maximum pooling) and trained with 8 batch sizes, 0.0001 learning rate, and 200 epochs. As a result, the average sensitivity of the training data was 0.998, the specificity was 0.972, the accuracy was 0.979, and the Dice's similarity coefficient was 0.968. In the validation data, the average sensitivity was 0.961, specificity was 0.978, accuracy was 0.972, and Dice's similarity coefficient was 0.961. The performance of deep convolutional neural network based models for the segmentation was good for ulna and radius bone.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.3
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• pp.35-46
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• 1992

Recently, the fuzzy theory has been adopted broadly to the applications of image processing. Especially the fuzzy clustering algorithm is adopted to image segmentation to reduce the ambiguity and the influence of noise in an image.But this needs lots of memory and execution time because of the great deal of image data. Therefore a new image segmentation algorithm is needed which reduces the memory and execution time, doesn't change the characteristices of the image, and simultaneously has the same result of image segmentation as the conventional fuzzy clustering algorithm. In this paper, for image segmentation, an extended fuzzy clustering algorithm is proposed which uses the occurence of data of the same characteristic value as the weight of the characteristic value instead of using the characteristic value directly in an image and it is proved the memory reduction and execution time reducted in comparision with the conventional fuzzy clustering algorithm in image segmentation.

• Journal of Institute of Convergence Technology
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• v.11 no.1
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• pp.1-5
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• 2021

Deep learning based methods achieve state-of-the-art accuracy, however, they typically rely on supervised training with large labeled datasets. It is known in many medical applications that labeling medical images requires significant expertise and much time, and typical hand-tuned approaches for data augmentation fail to capture the complex variations in such images. This paper proposes a 3D image augmentation method to overcome these difficulties. It allows us to enrich diversity of training data samples that is essential in medical image segmentation tasks, thus reducing the data overfitting problem caused by the fact the scale of medical image dataset is typically smaller. Our numerical experiments demonstrate that the proposed approach provides significant improvements over state-of-the-art methods for 3D medical image segmentation.

• Journal of Korean Society of Transportation
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• v.24 no.4 s.90
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• pp.103-113
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• 2006

Market Segmentation for Freight Transportation has been used to know the strategies both efficiency of freight transportation market and attraction of freight volume for carriers. It was so difficult to understand the individual preference of shippers that all shippers could be only homogenized through market segmentation. In Korea. standard Industrial classification has been used for freight market segmentation. This study evaluated another new market segmentation method for manufacturing industry. From the study, we knew that the best relevant market segmentation criterion was annual input-output volume, which showed excellent segmenting ability. Also. the results showed many differences against segmentation results according to standard industrial classification. This study had a meaning as a new trial which segmented freight transportation market using company size and shipment characteristic data.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.435-438
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• 2001

Reverse engineering technology refers to the process that creates a CAD model of an existing part using measuring devices. Recently, non-contact scanning devices have become more accurate and the speed of data acquisition has increased drastically. However, they generate thousands of points per second and various types of point data. Therefore, it becomes a major issue to handle the huge amount and various types of point data. To generate a CAD model from scanned point data efficiently, these point data should be well arranged through point data handling processes such as data reduction and segmentation. This paper proposes a new point data handling method using 3D grids. The geometric information of a part is extracted from point cloud data by estimating normal values of the points. The non-uniform 3D grids for data reduction and segmentation are generated based on the geometric information. Through these data reduction and segmentation processes, it is possible to create CAD models autmatically and efficiently. The proposed method is applied to two quardric medels and the results are discussed.

• Journal of the Korea Society of Computer and Information
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• v.23 no.4
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• pp.147-153
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• 2018

In this paper, we propose an efficient WBC 14-Diff classification which performs using the WBC-ResNet-152, a type of CNN model. The main point of view is to use Super-pixel for the segmentation of the image of WBC, and to use ResNet for the classification of WBC. A total of 136,164 blood image samples (224x224) were grouped for image segmentation, training, training verification, and final test performance analysis. Image segmentation using super-pixels have different number of images for each classes, so weighted average was applied and therefore image segmentation error was low at 7.23%. Using the training data-set for training 50 times, and using soft-max classifier, TPR average of 80.3% for the training set of 8,827 images was achieved. Based on this, using verification data-set of 21,437 images, 14-Diff classification TPR average of normal WBCs were at 93.4% and TPR average of abnormal WBCs were at 83.3%. The result and methodology of this research demonstrates the usefulness of artificial intelligence technology in the blood cell image classification field. WBC-ResNet-152 based morphology approach is shown to be meaningful and worthwhile method. And based on stored medical data, in-depth diagnosis and early detection of curable diseases is expected to improve the quality of treatment.

• MALSORI
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• no.58
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• pp.67-81
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• 2006

For a large corpus of time-aligned data, HMM based approaches are most widely used for automatic segmentation, providing a consistent and accurate phone labeling scheme. There are two methods for training in HMM. Flat starting method has a property that human interference is minimized but it has low accuracy. Bootstrap method has a high accuracy, but it has a defect that manual segmentation is required In this paper, a new algorithm is proposed to minimize manual work and to improve the performance of automatic segmentation. At first phase, voiced, unvoiced and silence classification is performed for each speech data frame. At second phase, the phoneme sequence is aligned dynamically to the voiced/unvoiced/silence sequence according to the acoustic phonetic rules. Finally, using these segmented speech data as a bootstrap, phoneme model parameters based on HMM are trained. For the performance test, hand labeled ETRI speech DB was used. The experiment results showed that our algorithm achieved 10% improvement of segmentation accuracy within 20 ms tolerable error range. Especially for the unvoiced consonants, it showed 30% improvement.