• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.188-191
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• 2004

야간에 비디오카메라로 촬영시 열악한 주위 환경과 영상 전송에 기인하여 다양한 잡음에 의하여 왜곡되거나 흐린 저대비(low contrast)영상을 가질 수 있다. 본 논문에서는 획득한 저대비 영상을 대비 향상시켜주는 기법을 제안한다. 동영상 압축표준인 MPEG-2는 인간의 시각 특성상 색차(chrominance)신호보다 밝기(luminance)신호에 더 민감하기 때문에 밝기신호와 색차 신호를 분리하여 압축한다. 밝기신호만을 추출한 후 K-means 알고리듬을 사용하여 교차점을 자동으로 선정하는 방법을 사용하는데, 이 최적의 교차점을 선정하는 과정은 획득한 영상을 물체와 배경으로 분리하는 두 개의 클래스 문제로 보고 K-means 알고리듬을 적용하였고 구한 교차점을 사용하여 영상을 양분하여 히스토그램 평활화 방법을 적용하였다 븐 논문에서는 퍼지성 지수(index of fuzziness)를 사용하여 향상의 정도를 측정하였다. 제안된 기법을 저대비 영상에 적용하였으며 그 결과를 히스토그램 평활화 기법의 결과와 비교하였다.

• Journal of Korean Tunnelling and Underground Space Association
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• v.12 no.1
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• pp.15-29
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• 2010

Spalling is a kind of instability phenomenon of surrounding rock around underground openings subjected to high in-situ stress according to the development of extension fractures. Three kinds of spalling criteria have been presented so far; however, all spalling criteria have the range of values so that the fuzziness and vagueness of spalling criterion cannot be avoided. In this study, a new fuzzy probability model is proposed to predict the probability of spalling in a systematic way by using fuzzy probability theory. Many of the underground opening projects worldwide are evaluated with the proposed method. Prediction results expressed as the spalling probability agree well with the in-situ observations. In particular, a new fuzzy probability model considering all three evaluation indices of spalling by adopting weighting factors based on relative reliability among three evaluation indices is able to resolve erroneous prediction of spalling by choosing only one prediction method. Moreover, the more reasonable value of spalling probability could have been obtained by adopting the modified damage index to the newly proposed fuzzy probability model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1205-1223
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• 2018

Image enhancement is an emerging method for analyzing the images clearer for interpretation and analysis in the spatial domain. The goal of image enhancement is to serve an input image so that the resultant image is more suited to the particular application. In this paper, a novel method is proposed based on Mamdani fuzzy inference system (FIS) using multiple fuzzy membership functions. It is observed that the shape of membership function while converting the input image into the fuzzy domain is the essential important selection. Then, a set of fuzzy If-Then rule base in fuzzy domain gives the best result in image contrast enhancement. Based on a different combination of membership function shapes, a best predictive solution can be determined which can be suitable for different types of the input image as per application requirements. Our result analysis shows that the quality attributes such as PSNR, Index of Fuzziness (IOF) parameters give different performances with a selection of numbers and different sized membership function in the fuzzy domain. To get more insight, an optimization algorithm is proposed to identify the best combination of the fuzzy membership function for best image contrast enhancement.

• Journal of Advanced Navigation Technology
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• v.16 no.6
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• pp.1109-1115
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• 2012

In this paper, we propose a system that recommends the appropriate menu using the fuzzy rules and the case database. The rules are defined by using the user's body information such as height and weight and these information is often vague. Due to its fuzziness, we use the fuzzy logic to represent the information. In our system, it firstly gets the body information for computing the BMI (Body Mass Index) values. Then it combines the muscle mass factor and BMI values to make a fuzzification for calculating the obesity rate. It finally recommends the most relative menu by comparing with the user's obesity rate from each cases in the database. We implement the system on the Android platform and show that our proposed method can achieve reasonable performance through the various experiments,