• Journal of the Korean Institute of Navigation
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• v.19 no.3
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• pp.21-28
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• 1995

Mathematical morphology skeleton image processing makes many partial skeleton image planes from an original binary image. And the original binary image can be reconstructed without any distortion by summing the first partial skeleton image plane and each dilated partial skeleton image planes using the same structuring element. Especially compression effects of Elias coding to the morphological globally minimal skeleton(GMS) image, is better than that of PCX and Huffman coding. And then this paper proposes mathematical morphological GMS image processing which can be applied to a binary image transmitting for facimile and big size(bigger than $64{\times}64$ size) bitmap fonts storing in a memory.

• School Mathematics
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• v.12 no.4
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• pp.563-584
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• 2010

This study is aimed at analysing the mathematical thinking processes based on image by the mathematically gifted. For this, the 'Splitting a Tetrahedron' Task was used and mathematical thinking of the two middle school students were investigated. One of them deduced how many tetrahedral and octahedral were there when a tetrahedra was splitted by the surfaces which were parallel to each face of the tetrahedra without using any physical material. The other one solved the task using physical material and invented new images. A concrete image, indexical image and symbolic image were founded and the various roles of images could be confirmed.

• Journal of Information Processing Systems
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• v.17 no.1
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• pp.1-13
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• 2021

In this research, we study the problem of font image skeletonization using an end-to-end deep adversarial network, in contrast with the state-of-the-art methods that use mathematical algorithms. Several studies have been concerned with skeletonization, but a few have utilized deep learning. Further, no study has considered generative models based on deep neural networks for font character skeletonization, which are more delicate than natural objects. In this work, we take a step closer to producing realistic synthesized skeletons of font characters. We consider using an end-to-end deep adversarial network, SkelGAN, for font-image skeletonization, in contrast with the state-of-the-art methods that use mathematical algorithms. The proposed skeleton generator is proved superior to all well-known mathematical skeletonization methods in terms of character structure, including delicate strokes, serifs, and even special styles. Experimental results also demonstrate the dominance of our method against the state-of-the-art supervised image-to-image translation method in font character skeletonization task.

• Journal of the Korean School Mathematics Society
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• v.9 no.2
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• pp.195-208
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• 2006

Among different geometrical representations of a mathematical concept, learners are likely to form their geometrical concept image of the given concept based on a specific one. A learner's image is not always in accord with the definition of a concept. This can induce his or her errors in mathematical problem solving. We need to analyse types of such errors and the cause of the errors. In this study, we analyse learners' geometrical concept images for geometrical concepts and errors related to such images. Furthermore we propose a theoretical framework for error analysis related to a learner's concept image for a general mathematical concept in mathematical problem solving.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.745-749
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• 1998

Mathematical morphology (MM) has been introduced as a powerful tool for studying the geometrical properties of images, MM is a good approach to digital image processing , which is based on the shape feature. The MM operators such as dilation, erosion, closing and opening have been applied successfully to image noise reduction. The MM filters can easily filter the noise when the noise factors are known. However it is very difficult to reduce the noise when images are ambiguous, because the boundary between the noise and object is vague. In this paper, we propose a new method to reduce noise from ambiguous images by using Fuzzy Mathematical Morphology (FMM) operators. Performance evaluation via simulations show that the FMM filters efficiently reduce the image noise. Furthermore, the FMM filters show a good performance compared with the conventional filters.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.14 no.1
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• pp.57-66
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• 2010

In this paper, we develop the Automatic Number Plate Recognition (ANPR) System. ANPR is generally composed of the following four steps: i) The acquisition of the image; ii) The extraction of the region of the number plate; iii) The partition of the number and iv) The recognition. The second and third steps incorporate image processing technique. We propose to resolve this by using Partial Differential Equation(PDE) based segmentation method. This method is computationally efficient and robust. Results indicate that our methods are capable to recognize the plate number on difficult situations.

• Communications of Mathematical Education
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• v.31 no.1
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• pp.71-84
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• 2017

Taylor series has a complicated structure comprising of various concepts in college major mathematics. This subject is a strong tool which has usefulness and applications not only in calculus, analysis, and complex analysis but also in physics, engineering etc., and other study. However, students have difficulties in understanding mathematical structure of Taylor series convergence correctly. In this study, after classifying students' mathematical characteristic into three categories, we use structural image of Taylor series convergence which associated with mathematical structure and operation acted on that structure. Thus, we try to analyze the understanding of Taylor series convergence and present the results of this study.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.17-22
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• 2002

This paper is concerned with a local detection of road on an airborne SAR image. The roads can be characterized by their geometry and radiometry. Roads are assumed as linear, thin, and elongated objects that are darker than their surroundings on an airborne SAR image. With these assumptions, a series of morphological filters are applied and tested successively. This approach is simple and almost non parametric and has been successfully applied to an airborne SAR image.

• Journal of the Chungcheong Mathematical Society
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• v.25 no.2
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• pp.367-377
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• 2012

The goal of the image restoration is to find a good approximation of the original image for the degraded image, the blurring matrix, and the statistics of the noise vector given. Fast truncated Lagrange (FTL) method has been proposed by G. Landi as a image restoration method for large-scale ill-conditioned BTTB linear systems([3]). We implemented FTL method for the image restoration problem with reflective boundary condition which gives better reconstructions of the unknown, the true image.

• Communications of the Korean Mathematical Society
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• v.30 no.1
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• pp.1-5
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• 2015

Let G be a cyclic group of prime power order. There is a natural embedding of $\mathbb{Z}[G]$ into a product of rings of integers of cyclotomic fields. In this paper the image of the embedding is determined, and we also compute the index of the image.