• Journal of KIISE:Software and Applications
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• v.31 no.1
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• pp.89-99
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• 2004

This paper proposes a morphological analysis method that enables morphological analysis by checking conditions between two adjacent morphemes. These conditions are fed from a dictionary. This method eliminates a code conversion module and the application of transformational rules for candidate generation. The method claims that very high speed morphological analysis is attainable through simple bit operations for adjacency condition check. MACH, an implementation of the proposed method, is a supersonic Korean morphological analyzer which is able to analyze a document of 1 GB in 5 minutes on a PC with 1.13 GHz Pentium III CPU. The analysis accuracy of MACH is 99.2 %.

• The Journal of Korean Institute of Information Technology
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• v.15 no.12
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• pp.165-171
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• 2017

This paper proposes a method of support-generation using morphological image processing instead of geometric calculations. The geometric computational cost is dependent on the shape, but our method is independent on the shape. For obtaining the external support area for extrusion shape, we represents morphological operations between two sliced layer images and shows results of each operation stages. Internal support area is evaluated from erosion and opening operations with the sliced-layer image. In these support areas, the supporter image is generated using the designed support structures. Also, we made a DLP printer and the STL model included supporter-structure is printed by the DLP printer. We confirmed the necessity of support-generation method with the support structures individually dependent on materials by looking at the printed results.

• The Journal of Internal Korean Medicine
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• v.37 no.6
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• pp.1030-1041
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• 2016

Objectives: This study investigated the administration of Jeungmiyijin-tang (JYT) to rats with reflux esophagitis (RE) induced by pylorus and forestomach ligation operations. Methods: Twenty laboratory rats were divided into three groups with 5~7 rats in each group. The control group consisted of rats with no inflammation (CON). The RE group had rats with gastroesophageal reflux elicited by pylorus and forestomach ligation operations. The JYT group had rats that were orally administered Jeungmiyijin-tang (1.5 ml/day/300 g) once a day for 14 days before reflux esophagitis was induced by the pylorus and forestomach ligation operations. Six hours after the operations, the rats were sacrificed, morphological changes were observed, and histological examinations were done in the stomach and esophagus lesion areas. If apoptosis was observed, the apoptotic cells in the esophagus lesion areas were counted. Results: The morphological and histochemical changes consisted of various injuries from hemorrhagic erosion in the RE group, while there were significantly fewer in the JYT group. The RE group marked increases of gastric mucosa erosion and infiltration of inflammatory cells in the submucosa, as well as cell division in the epithelial layer, the proliferation and degranulation of mast cells, and increases in the IL-$1{\beta}$, TNF-${\alpha}$, and MMP-9 expressions in the esophagus of the rats. The JYT group was inhibited above expression compared with the RE group. Apoptosis was statistically significantly decreased in the JYT group compared with the RE group. Conclusions: According to the above results, it appears that Jeungmiyijin-tang inhibits the expression of pro-inflammatory cytokines (TNF-${\alpha}$, IL-$1{\beta}$, and MMP-9) and apoptosis in the esophagus mucosa, thereby preventing esophageal mucosal damage from esophageal reflux.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.23-29
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• 2009

Blob detection is an essential ingredient process in some computer applications such as intelligent visual surveillance. However, previous blob detection algorithms are still computationally heavy so that supporting real-time multi-channel intelligent visual surveillance in a workstation or even one-channel real-time visual surveillance in a embedded system using them turns out prohibitively difficult. In this paper, we propose a fast and precise blob detection algorithm for visual surveillance. Blob detection in visual surveillance goes through several processing steps: foreground mask extraction, foreground mask correction, and connected component labeling. Foreground mask correction necessary for a precise detection is usually accomplished using morphological operations like opening and closing. Morphological operations are computationally expensive and moreover, they are difficult to run in parallel with connected component labeling routine since they need much different processing from what connected component labeling does. In this paper, we first develop a fast and precise foreground mask correction method utilizing on neighbor pixel checking which is also employed in connected component labeling so that the developed foreground mask correction method can be incorporated into connected component labeling routine. Through experiments, it is verified that our proposed blob detection algorithm based on the foreground mask correction method developed in this paper shows better processing speed and more precise blob detection.

• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
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• 2006.06b
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• pp.423-426
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• 2006

Paper has fibers and fines network structure and it is strongly affected by interface bonding between fibers. Depending on the inter-fiber bonding, paper bulk is determined. Fines play an important roll in Campbell and consolidation effect through wet pressing and drying operations. Refined Sw-BKP, Hw-BKP and BCTMP fines were used to investigate the fines effect. Wet-web strength, breaking length, scattering coefficient, and hydrodynamic specific volume were measured. According to the result of experiments, chemical and morphological compositions of fines do not strongly affect to wet-web forming, but strongly affect to drying operations which form hydrogen bonding among fiber-fines-fiber matrixes. Paper bulk should be controlled by the extent of hydrogen bonding between fibers during drying operations.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.1
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• pp.57-61
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• 2013

A hybrid genetic algorithm based learning method for the morphological neural networks (MNN) is proposed. The morphological neural networks are based on max-plus algebra, therefore, it is difficult to optimize the coefficients of MNN by the learning method with derivative operations. In order to solve the difficulty, a hybrid genetic algorithm based learning method to optimize the coefficients of MNN is used. Through the image compression/reconstruction experiment using test images extracted from standard image database(SIDBA), it is confirmed that the quality of the reconstructed images obtained by the proposed method is better than that obtained by the conventional neural networks.

• The Korean Journal of Applied Statistics
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• v.22 no.3
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• pp.569-584
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• 2009

Edge detection in images has been widely used in image processing system and computer vision. Morphological edge detection has used structuring elements with fixed shapes. This paper presents morphological operators with non-fixed shape kernels, or amoebas, which take into account the image contour variations to adapt their shape. Experimental results are analyzed in both qualitative analysis through visual inspection and quantitative analysis with PFOM and ROC curves. The Experiments demonstrate that these novel operators outperform classical morphological operations with a fixed, space-invariant structuring elements for edge detection applications.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.799-802
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• 2017

We attempt to combines concepts of Morphological Operator(MO) and Convolutional Neural Networks(CNN) to improve image-to-image translation. To do this, we propose an operation that approximates morphological operations. Also we propose S-Convolution, an operation that extends the operation to use multiple filters like CNN. The experiment result shows that it can learn MO with big filter using multiple S-convolution layer of small filter. To validate effectiveness of the proposed layer in image-to-image translation we experiment with GAN with S-convolution applied. The result showed that GAN with S-convolution can achieve distinct result from that of GAN with CNN.

• Computers and Concrete
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• v.21 no.6
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• pp.727-739
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• 2018

Crack is the most common typical feature of concrete deterioration, so routine monitoring and health assessment become essential for identifying failures and to set up an appropriate rehabilitation strategy in order to extend the service life of concrete structures. At present, image segmentation algorithms have been applied to crack analysis based on inspection images of concrete structures. The results of crack segmentation offering crack information, including length, width, and area is helpful to assist inspectors in surface inspection of concrete structures. This study proposed an algorithm of image segmentation enhancement, named morphological segmentation based on edge detection-II (MSED-II), to concrete crack segmentation. Several concrete pavement and building surfaces were imaged as the study materials. In addition, morphological operations followed by cross-curvature evaluation (CCE), an image segmentation technique of linear patterns, were also tested to evaluate their performance in concrete crack segmentation. The result indicates that MSED-II compared to CCE can lead to better quality of concrete crack segmentation. The least area, length, and width measurement errors of the concrete cracks are 5.68%, 0.23%, and 0.00%, respectively, that proves MSED-II effective for automatic measurement of concrete cracks.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.9
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• pp.1167-1174
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• 1999

In this paper, we present a new technique for the local decomposition of convex structuring elements for morphological image processing. Local decomposition of a structuring element consists of local structuring elements, in which each structuring element consists of a subset of origin pixel and its eight neighbors. Generally, local decomposition of a structuring element reduces the amount of computation required for morphological operations with the structuring element. A unique feature of our approach is the use of linear integer programming technique to determine optimal local decomposition that guarantees the minimal amount of computation. We defined a digital convex polygon, which, in turn, is defined as a convex structuring element, and formulated the necessary and sufficient conditions to decompose a digital convex polygon into a set of basis digital convex polygons. We used a set of linear equations to represent the relationships between the edges and the positions of the original convex polygon, and those of the basis convex polygons. Further. a cost function was used represent the total processing time required for computation of dilation/erosion with the structuring elements in a decomposition. Then integer linear programming was used to seek an optimal local decomposition, that satisfies the linear equations and simultaneously minimize the cost function.