• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.796-799
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• 2006

This paper presents a new method for automatic detection of skew in a document image using mathematical morphology. To speed up processing, we use reduced image but it still requires long time to estimate the skew angle so the proposed method works with region of interest, not with whole image. Character strings are connected by using morphological closing operation and a component labeling is used to select region of interest. The method considers the lowermost pixels of characters in candidate regions in the binary image of original document image. Experimental results shows that the proposed method is extremely fast and robust as well as independent of script forms.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.192-194
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• 2003

Since the details in quasi-homogeneous region will be destroyed from the conventional global image enhancement method such as histogram equalization. This defect is caused by the saturation of gray level in equalization process. So the local histogram equalization for each quasi-homogeneous region will be used in order to improve the details in the region itself. To obtain the quasi- homogeneous regions, the original image must be segmented. Here we applied the watershed transform to the interesting image. Since the watershed transform is based on mathematical morphology, therefore, the regions touch can be effectively separated. Hence two adjacent regions which have the similar gray pixels will be split off. The process will be independently applied to three different spectral images. Then three different colors are assigned to each processed image in order to produce a color composite image. By the proposed algorithm, the result image shows the better perception on image details. Therefore, the high efficiency of image classification can be obtained by using this color image.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.6_3
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• pp.1285-1291
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• 2022

In this paper, we propose a low-cost, low-power embedded environment-based deep learning lightweight model for input images to recognize laundry management codes. Laundry franchise companies mainly use barcode recognition-based systems to record laundry consignee information and laundry information for laundry collection management. Conventional laundry collection management systems using barcodes require barcode printing costs, and due to barcode damage and contamination, it is necessary to improve the cost of reprinting the barcode book in its entirety of 1 billion won annually. It is also difficult to do. Recognition performance is improved by applying the VGG model with 7 layers, which is a reduced-transformation of the VGGNet model for number recognition. As a result of the numerical recognition experiment of service parts drawings, the proposed method obtained a significantly improved result over the conventional method with an F1-Score of 0.95.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.05a
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• pp.185-191
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• 1999

Mathematical morphology based on set theory has been applied to various areas in image processing. Pitas proposed a object recognition algorithm using Morphological Shape Decomposition(MSD), and a new representation scheme called Morphological Shape Representation(MSR). The Pitas's algorithm is a simple and adequate approach to recognize objects that are rotated 45 degree-units with respect to the model object. However, this recognition scheme fails in case of random rotation. This disadvantage may be compensated by defining small angle increments. However, this solution may greatly increase computational complexity because the smaller the step makes more number of rotations to be necessary. In this paper, we propose a new method for object recognition based on MSD. The first step of our method decomposes a binary shape into a union of simple binary shapes, and then a new tree structure is constructed which ran represent the relations of binary shapes in an object. finally, we obtain the feature informations invariant to the rotation, translation, and scaling from the tree and calculate matching scores using efficient matching measure. Because our method does not need to rotate the object to be tested, it could be more efficient than Pitas's one. MSR has an intricate structure so that it might be difficult to calculate matching scores even for a little complex object. But our tree has simpler structure than MSR, and easier to calculated the matchng score. We experimented 20 test images scaled, rotated, and translated versions of five kinds of automobile images. The simulation result using octagonal structure elements shows 95% correct recognition rate. The experimental results using approximated circular structure elements are examined. Also, the effect of noise on MSR scheme is considered.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.3 s.309
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• pp.84-91
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• 2006

In this paper, we present a new segmentation algorithm for color images based on mathematical morphology and a Gaussian mixture model(GMM). We use the morphological operations to determine the number of components in a mixture model and to detect their modes of each mixture component. Next, we have adopted the GMM to represent the probability distribution of color feature vectors and used the deterministic annealing expectation maximization (DAEM) algorithm to estimate the parameters of the GMM that represents the multi-colored objects statistically. Finally, we segment the color image by using posterior probability of each pixel computed from the GMM. The experimental results show that the morphological operation is efficient to determine a number of components and initial modes of each component in the mixture model. And also it shows that the proposed DAEM provides a global optimal solution for the parameter estimation in the mixture model and the natural color images are segmented efficiently by using the GMM with parameters estimated by morphological operations and the DAEM algorithm.

• Imaging Science in Dentistry
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• v.39 no.1
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• pp.41-49
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• 2009

Purpose: To detect the progression of experimentally induced periapical lesions on periapical radiograph and cone beam computed tomograph (CBCT) by quantitative analysis. Materials and Methods: After the removal of coronal pulps from premolars of two Beagle dogs, the root canals of premolars were exposed to oral environment during one week and then sealed for 70 days. Digital periapical radiographs and CBCTs were taken at baseline and every 7 days for 77 days after pulp exposure. We examined occurrence and areas of periapical bone resorption. Three comparative groups of CBCT radiographs were prepared by average projection of thin slabs with different bucco-lingual thicknesses (0.1, 3.0, and 8.0 mm) using a 3D visualization software. Radiographic densities were compensated by image normalization. Digital images were processed with mathematical morphology operations. The radiographic density and morphological features of periapical lesions were compared among three groups of CBCT in different time points. Results: In the CBCT group with 0.1 mm thickness, radiographic density (p<0.05) and trabecular bone area (p<0.01) were significantly decreased at the fifth week. However, in the CBCT groups with 3 mm and 8 mm thickness and periapical radiographs, none of densitometric and morphological features showed any significant differences in different time points. Radiographic density of periapical lesion showed increasing tendency at the eleventh week after pulp exposure. Conclusion: Radiographic detection of periapical lesions was possible at the fifth week after pulp contamination by quantitative method and was affected by buccolingual bone thickness.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.1
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• pp.33-42
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• 2001

Recently, the interests in the 3D image, generated from the range data and CAD, have exceedingly increased, accordingly a various 3D image database is being constructed. The efficient and fast scheme to access the desired image data is the important issue in the application area of the Internet and digital library. However, it is difficult to manage the 3D image database because of its huge size. Therefore, a proper descriptor is necessary to manage the data efficiently, including the content-based search. In this paper, the proposed shape descriptor is based on the voxelization of the 3D image. The medial axis transform, stemming from the mathematical morphology, is performed on the voxelized 3D image and the graph, which is composed of node and edge, is generated from skeletons. The generated graph is adequate to the novel shape descriptor due to no loss of geometric information and the similarity of the insight of the human. Therefore the proposed shape descriptor would be useful for the recognition of 3D object, compression, and content-based search.

• Explosives and Blasting
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• v.14 no.4
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• pp.13-19
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• 1996

Lately, the improtance of smooth blasting is increasing on every construction fields, suchas underground caves, tunnels, and roadconstruction, etc. The main purpose of smooth blasting is to prevent unnecessary cracks from the base rockwhich preserved permanently and is to gain the smooth fracture plane. So, in smooth blashing, explosives with low detonating velocity are generally used. But it is difficult to discuss general theory on the smooth blashing because the mechanical properties of pertienent rocks are difficult regionally. Accordingly basic reserches on the smooth blasting are demended. In this paper, the mechanisms of the smooth blasting on the rocks containing discontinuities were discussd. Firstly, the writer predicted the formation of fracture plane and unevenness using mathematical methodology, the next the model blast tests were conducted in order to simulate the crack propagation modes from the blast holes. Through the research, the following conclusions were obtained l)The blast test results were in reasonally good agreement with the theoretical prediction. 2)The degree of discontinuity has an influence on the fracture morphology.

• Imaging Science in Dentistry
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• v.42 no.4
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• pp.225-229
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• 2012

Purpose: This study aimed to evaluate the trabecular bone changes after alveolar bone grafting in unilateral cleft lip and palate (UCLP) patients using a computer-aided diagnosis (CAD) system. Materials and Methods: The occlusal radiographs taken from 50 UCLP patients were surveyed retrospectively. The images were categorized as: 50 images in group 0 (before bone grafting), 33 images in group 1 (one month after bone grafting), 24 images in group 2 (2-4 months after bone grafting), 15 images in group 3 (5-7 months after bone grafting), and 21 images in group 4 (8 or more months after bone grafting). Each image was grouped as either "non-cleft side" or "cleft side". The CAD system was used five times for each side to calculate the pixel area based on the mathematical morphology. Significant differences were found using a Wilcoxon signed ranks test or paired samples t test. Results: The pixel area showed a significant difference between the "non-cleft side" and "cleft side" in group 0 ($404.27{\pm}103.72/117.73{\pm}92.25$; p=0.00), group 1 ($434.29{\pm}86.70/388.31{\pm}109.51$; p=0.01), and group 4 ($430.98{\pm}98.11/366.71{\pm}154.59$; p=0.02). No significant differences were found in group 2 ($423.57{\pm}98.12/383.47{\pm}135.88$; p=0.06) or group 3 ($433.02{\pm}116.07/384.16{\pm}146.55$; p=0.19). Conclusion: Based on the design of this study, alveolar bone grafting was similar to normal bone within 2-7 months postoperatively.

• The Transactions of the Korea Information Processing Society
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• v.3 no.3
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• pp.449-459
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• 1996

The main goal of this paper is to introduce a novel definition for fuzzy mathematical morphology and a neural network implementation. The generalized- mean operator plays the key role for the definition. Such definition is well suited for neural network implementation. The first stage of the shared-weight neural network has adequate architecture to perform morphological operation. The shared- weight network performs classification based on the features extracted with the fuzzy morphological operation defined in this paper. Therefore, the parameters for the fuzzy definition can be optimized using neural network learning paradigm. Learning rules for the structuring elements, degree of membership, and weighting factors are precisely described. In application to handwritten digit recognition problem, the fuzzy morphological shared-weight neural network produced the results which are comparable to the state-of art for this problem.