• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.12
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• pp.1752-1755
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• 2016

In this letter, to determine how mathematical morphology operators can be best used to enhance the accuracy of background subtraction for "soccer videos", we conducted an experimental comparative study. We investigated six different mathematical morphology operators under the same experimental setup. We found that the closing by reconstruction-opening by reconstruction is optimal through the experiments using the F-measure. We believe that this comprehensive comparative study serves as a reference point and guide for developers and practitioners in choosing an appropriate mathematical morphology operator adopted for building intelligent soccer video analysis systems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.45-51
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• 1995

A novel definition for fuzzy mathematical morphology is described The generalized-mean operator plays the key role for this definition. Several hard constraints for standard generalized-mean have been eliminated. Complete mathematical description for obtaining fuzzy erosion and dilation is provided. The definitions are well suited for neural network implementation. Therefore, the parameters for the fuzzy definition can be optimized using neural network learning paradigm.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.1
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• pp.23-28
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• 2017

Shape from focus (SFF) is a technique used to reconstruct 3D shape of objects from a sequence of images obtained at different focus settings of the lens. In this paper, a new shape from focus method for 3D reconstruction of microscopic objects is described, which is based on gradient operator in Mathematical Morphology. Conventionally, in SFF methods, a single focus measure is used for measuring the focus quality. Due to the complex shape and texture of microscopic objects, single measure based operators are not sufficient, so we propose morphological operators with multi-structuring elements for computing the focus values. Finally, an optimal focus measure is obtained by combining the response of all focus measures. The experimental results showed that the proposed algorithm has provided more accurate depth maps than the existing methods in terms of three-dimensional shape recovery.

• The korean journal of orthodontics
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• v.48 no.3
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• pp.133-142
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• 2018

Objective: The purpose of this study was to evaluate differences in three-dimensional (3D) morphology of the hard palate between Korean adults with and without mild-to-moderate obstructive sleep apnea (OSA) using cone-beam computed tomographic (CBCT) data. Methods: The protocol for the two-dimensional (2D) and 3D mathematical modeling was established by analyzing CBCT images of 30 adults with OSA and 30 matched controls without OSA, using MIMICS software. The linear and angular measurements were also determined using this software. The measurements were repeated for 30 palates, by the same operator, to assess reliability. Results: The palates of OSA patients were higher in the posterior part and narrower in the anterior-superior part than those of the control group (p < 0.05). The nasal cavities of patients with OSA were narrower (p < 0.05) than those of controls. The increasing angle of the first molar palatal root is a compensation of the upper dental arch to improve occlusion. However, for most palatal measurements, there were no significant differences between the OSA and control groups (p > 0.05). The results of 2D and 3D mathematical models were consistent for linear and angular measurements, indicating that 2D and 3D mathematical modeling of the palate is a reliable methodology. Conclusions: OSA is a multifactorial disease; the palates of adults with mild-to-moderate OSA do not have specific morphological features distinct from those of healthy controls.

• Proceedings of the KSRS Conference
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• v.2
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• pp.643-646
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• 2006

This paper addresses the approach to extract linear features from satellite imagery using an efficient segmentation method. The extraction of linear features from satellite images has been the main concern of many scientists. There is a need to develop a more capable and cost effective method for the Iranian map revision tasks. The conventional approaches for producing, maintaining, and updating GIS map are time consuming and costly process. Hence, this research is intended to investigate how to obtain linear features from SPOT satellite imagery. This was accomplished using a discontinuity-based segmentation technique that encompasses four stages: low level bottom-up, middle level bottom-up, edge thinning and accuracy assessment. The first step is geometric correction and noise removal using suitable operator. The second step includes choosing the appropriate edge detection method, finding its proper threshold and designing the built-up image. The next step is implementing edge thinning method using mathematical morphology technique. Lastly, the geometric accuracy assessment task for feature extraction as well as an assessment for the built-up result has been carried out. Overall, this approach has been applied successfully for linear feature extraction from SPOT image.

• 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.