• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.5
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• pp.147-159
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• 2002

This paper proposes a method to recognize the noisiness of road images connected with the extraction of lane-related information in order to prevent the usage of erroneous information. The proposed method uses a fuzzy neural network(FNN) with the back-Propagation loaming algorithm. The U decides road images good or bad with respect to visibility of lane marks on road images. Most input parameters to the FNN are extracted from an edge distribution function(EDF), a function of edge histogram constructed by edge phase and norm. The shape of the EDF is deeply correlated to the visibility of lane marks of road image. Experimental results obtained by simulations with real images taken by various lighting and weather conditions show that the proposed method was quite successful, providing decision-making of noisiness with about 99%.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.35 no.11
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• pp.983-989
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• 2007

The bird strike simulation is a problem characterized by a high degree of uncertainty. It deals with nonlinear dynamics, complicated models of bird materials and geometry, as well as a plenty of possible boundary and initial conditions. In this complex field, uncertainty management plays an important role. This paper aims to assess the effect of input uncertainty of bird strike analysis on the impact behavior of the leading edge of the WIG(Wing in Ground Effect) craft obtained with finite element analysis using LS-DYNA 3D. The uncertainties of the bird strike simulation arise due to imprecision or lack of information, due to variability or scatter, or as a consequence of model simplification. These uncertain parameters are represented by fuzzy numbers with their membership functions quantifying an initial guess for the actual value of the model parameter. Using the transformation method as a special implementation of fuzzy arithmetic, the model can be analyzed with the intention of determining the influence of each uncertain parameter on the overall bird strike behavior.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.419-423
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• 2008

사람의 손동작은 오랫동안 하나의 언어역할을 하는 통신 수단으로 사용되어 왔다. 이러한 손동작 중에서 가장 체계를 갖춘 수화는 청각장애인이 일반인과 일상 대화를 할 수 있도록 도와주는 주요한 통신 수단이다. 하지만 건청인들의 대부분이 습득하고 있지 않아 청각장애인들과 의사소통이 거의 불가능 한 것이 현실이다. 따라서 본 논문에서는 건청인과 청각장애인들 간의 의사소통을 원활하게 하기 위해 색상 정보와 퍼지 클러스터링 알고리즘을 이용한 실시간 수화 인식 방법을 제안한다. 제안된 방법은 화상 카메라를 통해 얻어진 실시간 영상에서 YCbCr 컬러 공간에서 색차 정보에 해당하는 Cb, Cr 정보를 각각 추출한 후, 이진화한 영상과 원본 영상에서 마스크를 통한 에지를 추출한 이진화 영상에 대해 논리연산을 통해 두 손의 위치와 외곽을 추출한다. 추출된 각 정보를 조합하여 8 방향 윤곽선 추적 알고리즘을 적용하여 객체의 위치를 추적한다. 그리고 추적한 객체의 영역에 대해 형태학적 정보를 이용하여 잡음을 제거한 후, 최종적으로 두 손의 영역을 추출한다. 추출된 손의 영역은 퍼지 클러스터링 기법 중의 FCM 알고리즘을 적용하여 수화의 특징들을 분류하고 인식한다. 제안된 방법의 성능을 평가하기 위해 화상카메라를 통해 얻어진 실시간 영상을 대상으로 실험한 결과, 제안된 방법이 두 손 영역의 추출에 효과적이고 수화 인식에 있어서 가능성을 확인하였다.

• Journal of KIISE:Software and Applications
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• v.28 no.9
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• pp.649-661
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• 2001

While the conventional edge detection can be considered as the problem of determining the existence of edges at certain locations, the fuzzy edge modeling can be considered as the problem of determining the membership values of edges. Thus, if the location of an edge is unclear, or if the intensity function is different from the ideal edge model, the degree of edgeness at the location is represented as a fuzzy membership value. Using the concept of fuzzy edgeness, an automatic smoothing parameter evaluation and selection method for a conventional edge detector is proposed. This evaluation method uses the fuzzy edge modeling, and can analyze the effect of smoothing parameter to determine an optimal parameter for a given image. By using the selected parameter we can detect least ambiguous edges of a detection method for an image. The effectiveness of the parameter evaluation method is analyzed and demonstrated using a set of synthetic and real images.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.7
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• pp.901-906
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• 2007

Algorithms of path planning and obstacle avoidance are essential to autonomous mobile robots that are working in unknown environments in the real time. This paper presents a new navigation algorithm for an autonomous mobile robot with vision and IR sensors using fuzzy rules. Temporary targets are set up by distance variation method and then the algorithms of trajectory planning and obstacle avoidance are designed using fuzzy rules. In this approach, several digital image processing technique is employed to detect edge of obstacles and the distances between the mobile robot and the obstacles are measured. An autonomous mobile robot with single vision and IR sensors is built up for experiments. We also show that the autonomous mobile robot with the proposed algorithm is navigating very well in complex unknown environments.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.207-213
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• 2022

Recently, with the improvement of the performance of IoT technology and AI, automation and unmanned work are progressing in a wide range of fields, and interest in image processing, which is the basis of automation such as object recognition and object classification, is increasing. Image noise removal is an important process used as a preprocessing step in an image processing system, and various studies have been conducted. However, in most cases, it is difficult to preserve detailed information due to the smoothing effect in high-frequency components such as edges. In this paper, we propose an algorithm to restore damaged images in AWGN(additive white Gaussian noise) using fuzzy weights based on Gaussian distribution. The proposed algorithm switched the filtering process by comparing the filtering mask and the noise estimate with each other, and reconstructed the image by calculating the fuzzy weights according to the low-frequency and high-frequency components of the image.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.7
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• pp.1380-1386
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• 2007

This paper proposed an automatic recognition system of shipping container identifiers using fuzzy-based noise removal method and ART2-based self-organizing supervised learning algorithm. Generally, identifiers of a shipping container have a feature that the color of characters is blacker white. Considering such a feature, in a container image, all areas excepting areas with black or white colors are regarded as noises, and areas of identifiers and noises are discriminated by using a fuzzy-based noise detection method. Areas of identifiers are extracted by applying the edge detection by Sobel masking operation and the vertical and horizontal block extraction in turn to the noise-removed image. Extracted areas are binarized by using the iteration binarization algorithm, and individual identifiers are extracted by applying 8-directional contour tacking method. This paper proposed an ART2-based self-organizing supervised learning algorithm for the identifier recognition, which improves the performance of learning by applying generalized delta learning and Delta-bar-Delta algorithm. Experiments using real images of shipping containers showed that the proposed identifier extraction method and the ART2-based self-organizing supervised learning algorithm are more improved compared with the methods previously proposed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.9
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• pp.2013-2018
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• 2011

Coronary angioplasty and coronary artery bypass graft, both are for the treatment of myocardial infarction widely used methods. For these procedures, there are especially difficulties in stenosis of blood vessels to diagnose accurately. To remedy this problem, by several researchers by using edge detection to detect stenosis of blood vessels has been studying. However, the results of using these methods vary defend on the vascular structure and the quality of the image. In this study, to improve these problems, the new algorithm is proposed. The proposed algorithm consists of methods to detect bifurcation of blood vessels and its ending point by using multi sampling, threshold and fuzzy algorithm. To evaluate the performance of the proposed algorithm, angiography was used for the different results of the blood vessels of the proposed algorithm, and the result was effective in detecting bifurcation of blood vessels and its ending point.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.6
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• pp.621-628
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• 2015

This paper describes an algorithm for detecting vehicles detection in real time. The proposed algorithm has the technique based on computer vision and image processing. In real, complex environment such as one with road traffic, many algorithms have great difficulty such as low detection rate and increasing computational time due to complex backgrounds and rapid changes. To overcome this problem in this paper, the proposed algorithm consists of the following methods. First, to effectively separate the candidate regions, we use vertical and horizontal edge information, and shadow values from input image sequences. Second, we extracts features by using HOG from the selected candidate regions. Finally, this paper uses the OS fuzzy-ELM based on SLFN to classify the extracted features. The experimental results show that the proposed method perform well for detecting vehicles and improves the accuracy and the computational time of detecting.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.294-296
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• 1996

The process of detecting edge in an image is an important component of many Pattern Recognition and Computer Vision applications. In many practical cases, there exist blurred images due to defocussing, movement of an object and so on. In addition, local perturbation noise can be added to the images. We propose the edge detection technique in blurred and noisy image. For this, we use Fuzzy pyramid linking mothod to remove noise and enhance the edge in images. We develop contrast intensifier using the concept of Fuzzy sets as a postprocessing.