• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.525-527
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• 2003

Recently, the spatial resolution of earth observation satellites is significantly increased to a few meters. Such high spatial resolution images definitely will provide lots of information for detail-thirsty remote sensing users. However, it is more difficult to develop automated image algorithms for automated image feature extraction and pattern recognition. In this study, we propose a two-stage procedure to extract road information from high resolution satellite images. At first stage, a watershed segmentation technique is developed to classify the image into various regions. Then, a knowledge is built for road and used to extract the road regions. In this study, we use panchromatic and multi-spectral images of the IKONOS satellite as test dataset. The experiment result shows that the proposed technique can generate suitable and meaningful road objects from high spatial resolution satellite images. Apparently, misclassified regions such as parking lots are recognized as road needed further refinement in future research.

• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.2
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• pp.157-165
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• 2009

Existing methods for image segmentation using diffusion can't preserve contour information, or noises with high gradients become more salient as the umber of times of the diffusion increases, resulting in over-segmentation when applied to watershed. This thesis proposes a method for image segmentation by applying morphology operation together with robust anisotropic diffusion. For an input image, transformed into LUV color space, closing by reconstruction and anisotropic diffusion are applied to obtain a simplified image which preserves contour information with noises removed. With gradients computed from this simplifed images, watershed algorithm is applied. Experiments show that color images are segmented very effectively without over-segmentation.

• Journal of Korea Multimedia Society
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• v.19 no.3
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• pp.531-538
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• 2016

In this paper, we propose a novel post-processing method of semantic segmentation from indoor scenes with RGBD inputs. For accurate segmentation, various post-processing methods such as superpixel from color edges or Conditional Random Field (CRF) method considering neighborhood connectivity have been used, but these methods are not efficient due to high complexity and computational cost. To solve this problem, we maximize the efficiency of post processing by using depth superpixel extracted from disparity image to handle object silhouette. Our experimental results show reasonable performances compared to previous methods in the post processing of semantic segmentation.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.661-664
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• 1998

Since the region segmentation at high resolution contains most of viable semantic object contours in an image, the bottom-up approach for image segmentation is appropriate for the application such as MPEG-4 which needs to preserve semantic object contours. However, the conventioal region merging methods, that follow the region segmentation, have poor performance in keeping low-contrast semantic object contours. In this paper, we propose an image segmentation algorithm based on classified region merging. The algorithm pre-segments an image with a large number of small regions, and also classifies it into several classes having similar gradient characteristics. Then regions only in the same class are merged according to the boundary weakness or statisticsal similarity. The simulation result shows that the proposed image segmentation preserves semantic object contours very well even with a small number of regions.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.389-391
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• 2007

This paper describes about the printed character segmentation and recognition system for slabs in steel manufacturing process. To increase the recognition rate, it is important to improve success rate of character segmentation. Since Slabs front area surface are not uniform and surface temperature is very high, marked characters not only undergo damages but also have much noise. On the other hand, since almost marked characters are very thick and the space between characters is only about 10 $^{\sim}$ 15 mm, there are many touching characters. Therefore appropriate character image preprocessing and segmentation algorithm is needed. In this paper we propose a multi-local thresholding method for damaged character restoration, a modified touching character segmentation, algorithm for marked characters. Finally a effective Multi-Class SVM is used to recognize segmented characters.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.25-28
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• 2020

Automatic segmentation of brain tissues such as WM, GM, and CSF from brain MRI scans is helpful for the diagnosis of many neurological disorders. Accurate segmentation of these brain structures is a very challenging task due to low tissue contrast, bias filed, and partial volume effects. With the aim to improve brain MRI segmentation accuracy, we propose an end-to-end convolutional based U-SegNet architecture designed with multi-scale kernels, which includes cascaded dilated convolutions for the task of brain MRI segmentation. The multi-scale convolution kernels are designed to extract abundant semantic features and capture context information at different scales. Further, the cascaded dilated convolution scheme helps to alleviate the vanishing gradient problem in the proposed model. Experimental outcomes indicate that the proposed architecture is superior to the traditional deep-learning methods such as Segnet, U-net, and U-Segnet and achieves high performance with an average DSC of 93% and 86% of JI value for brain MRI segmentation.

• Korean Circulation Journal
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• v.54 no.1
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• pp.30-39
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• 2024

Background and Objectives: Intravascular ultrasound (IVUS) evaluation of coronary artery morphology is based on the lumen and vessel segmentation. This study aimed to develop an automatic segmentation algorithm and validate the performances for measuring quantitative IVUS parameters. Methods: A total of 1,063 patients were randomly assigned, with a ratio of 4:1 to the training and test sets. The independent data set of 111 IVUS pullbacks was obtained to assess the vessel-level performance. The lumen and external elastic membrane (EEM) boundaries were labeled manually in every IVUS frame with a 0.2-mm interval. The Efficient-UNet was utilized for the automatic segmentation of IVUS images. Results: At the frame-level, Efficient-UNet showed a high dice similarity coefficient (DSC, 0.93±0.05) and Jaccard index (JI, 0.87±0.08) for lumen segmentation, and demonstrated a high DSC (0.97±0.03) and JI (0.94±0.04) for EEM segmentation. At the vessel-level, there were close correlations between model-derived vs. experts-measured IVUS parameters; minimal lumen image area (r=0.92), EEM area (r=0.88), lumen volume (r=0.99) and plaque volume (r=0.95). The agreement between model-derived vs. expert-measured minimal lumen area was similarly excellent compared to the experts' agreement. The model-based lumen and EEM segmentation for a 20-mm lesion segment required 13.2 seconds, whereas manual segmentation with a 0.2-mm interval by an expert took 187.5 minutes on average. Conclusions: The deep learning models can accurately and quickly delineate vascular geometry. The artificial intelligence-based methodology may support clinicians' decision-making by real-time application in the catheterization laboratory.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.10
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• pp.38-47
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• 1998

The syntactic analysis for the practical machine translation should be able to analyze a long sentence, but the long sentence analysis is a critical problem because of its high analysis complexity. In this paper a sentence segmentation method is proposed for an efficient analysis of a long sentence and the method of determining optimal sentence segmentation positions using statistical information and genetic learning is introduced. It consists of two modules: (1) decomposable position determination which uses lexical contextual constraints acquired from a training data tagged with segmentation positions. (2) segmentation position selection by the selection function of which the weights of parameters are determined through genetic learning, which selects safe segmentation positions with enhancing the analysis efficiency as much as possible. The safe segmentation by the proposed sentence segmentation method and the efficiency enhancement of the analysis are presented through experiments.

• Korean Journal of Remote Sensing
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• v.33 no.6_1
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• pp.1003-1017
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• 2017

Multispectral image data of high spatial resolution is required to obtain correct information on the ground surface. The multispectral image data has lower resolution compared to panchromatic data. PAN-sharpening fusion technique produces the multispectral data with higher resolution of panchromatic image. Recently the object-based approach is more applied to the high spatial resolution data than the conventional pixel-based one. For the object-based image analysis, it is necessary to perform image segmentation that produces the objects of pixel group. Image segmentation can be effectively achieved by the process merging step-by-step two neighboring regions in RAG (Regional Adjacency Graph). In the satellite remote sensing, the operational environment of the satellite sensor causes image degradation during the image acquisition. This degradation increases variation of pixel values in same area, and results in deteriorating the accuracy of image segmentation. An iterative approach that reduces the difference of pixel values in two neighboring pixels of same area is employed to alleviate variation of pixel values in same area. The size of segmented regions is associated with the quality of image segmentation and is decided by a stopping rue in the merging process. In this study, the image restoration and segmentation was quantitatively evaluated using simulation data and was also applied to the three PAN-sharpened multispectral images of high resolution: Dubaisat-2 data of 1m panchromatic resolution from LA, USA and KOMPSAT3 data of 0.7m panchromatic resolution from Daejeon and Chungcheongnam-do in the Korean peninsula. The experimental results imply that the proposed method can improve analytical accuracy in the application of remote sensing high resolution PAN-sharpened multispectral imagery.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.8
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• pp.1843-1859
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• 2013

The continuous increase of the spatial resolution of remote sensing images brings great challenge to image analysis and processing. Traditional prior knowledge-based region detection and target recognition algorithms for processing high resolution remote sensing images generally employ a global searching solution, which results in prohibitive computational complexity. In this paper, a more efficient region of interest (ROI) detection algorithm based on visual attention and threshold segmentation (VA-TS) is proposed, wherein a visual attention mechanism is used to eliminate image segmentation and feature detection to the entire image. The input image is subsampled to decrease the amount of data and the discrete moment transform (DMT) feature is extracted to provide a finer description of the edges. The feature maps are combined with weights according to the amount of the "strong points" and the "salient points". A threshold segmentation strategy is employed to obtain more accurate region of interest shape information with the very low computational complexity. Experimental statistics have shown that the proposed algorithm is computational efficient and provide more visually accurate detection results. The calculation time is only about 0.7% of the traditional Itti's model.