• Journal of Institute of Control, Robotics and Systems
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• v.8 no.6
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• pp.468-476
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• 2002

The confidence of information from image processing depends on the original image quality. Enhancing the confidence by an algorithm has an essential limitation. Especially, road images are exposed to lots of noisy sources, which makes image processing difficult. We, in this paper, propose a FNN (fuzzy-neural network) capable oi deciding the quality of a road image prior to extracting lane-related information. According to the decision by the FNN, road images are classified into good or bad to extract lane-related information. A CDF (cumulative distribution function), a function of edge histogram, is utilized to construct input parameters of the FNN, it is based on the fact that the shape of the CDF and the image quality has large correlation. Input pattern vector to the FNN consists of ten parameters in which nine parameters are from the CDF and the other one is from intensity distribution of raw image. Correlation analysis shows that each parameter represents the image quality well. According to the experimental results, the proposed FNN system was quite successful. We carried out simulations with real images taken by various lighting and weather conditions and achieved about 99% successful decision-making.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.530-536
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• 2008

Localization is one of the fundamental problems in wireless sensor networks (WSNs) that forms the basis for many location-aware applications. Localization in WSNs is to determine the position of node based on the known positions of several nodes. Most of previous localization method use triangulation or multilateration based on the angle of arrival (AOA) or distance measurements. In this paper, we propose an enhanced centroid localization method based on edge weights of adjacent nodes using fuzzy modeling and genetic algorithm when node connectivities are known. The simulation results shows that our proposed centroid method is more accurate than the simple centroid method using connectivity only.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.133-135
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• 2022

With the development of IoT and AI technologies and media, various digital devices are being used, and unmanned and automation is progressing rapidly. In particular, high-level image processing technology is required in fields such as smart factories, autonomous driving technology, and intelligent CCTV. However, noise present in the image affects processes such as edge detection and object recognition, and causes deterioration of system accuracy and reliability. In this paper, we propose a filtering algorithm using fuzzy weights to reconstruct images damaged by complex noise. The proposed algorithm obtains a reference value using noise judgment and calculates the final output by applying a fuzzy weight. Simulation was conducted to verify the performance of the proposed algorithm, and the result image was compared with the existing filter algorithm and evaluated.

• Journal of the Korea Society of Computer and Information
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• v.14 no.7
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• pp.33-40
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• 2009

Edge detection is very significant in low level image processing. However, majority edge detection methods are not only effective enough cause of the noise points' influence, even not flexible enough to different input images. In order to sort these problems, in this paper an algorithm is presented that has an extra noise reduction stage at first, and then automatically selects the both thresholds depending on gradient amplitude histogram and intra class minimum variance. Using this algorithm, can fade out almost all of the sensitive noise points, and calculate the propose thresholds for different images without setting up the practical parameters artificially, and then choose edge pixels by fuzzy algorithm. In finally, get the better result than the former Canny algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.11
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• pp.2429-2434
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• 2010

In this paper, we propose a method to extract and analyze palm line with fuzzy method. In order to extract the palm part, we transform the original RGB color space to YCbCr color space and extract sin colors ranging Y:65-255, Cb:25-255, Cr:130-255 and use it as a threshold. Possible noise is removed by 8-directional contour tracking algorithm and morphological characteristic of the palm. Then the edge is extracted from that noise-free image by stretching method and sobel mask Then the fuzzy binarization algorithm is applied to remove any minute noise so that we have only the palm lines and the boundary of the hand. Since the palm line reading is done with major lines, we use the morphological characteristics of the analyzable palm lines and fuzzy inference rules. Experiment verifies that the proposed method is better in visibility and thus more analyzable in palm reading than the old method.

• Proceedings of the Korea Multimedia Society Conference
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• 2000.04a
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• pp.178-183
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• 2000

본 연구는 위성탐사 이미지에서 탑재 센서가 갖는 한계인 공간해상도의 문제를 퍼지함수의 정의를 이용하여 해석하는 방법을 제안한다. 이는 한 화소(SPOT 의 HRV의 경우 20 m$\times$20m)에 포함된 혼합정보들의 내용을 예측할 수 있으며, 대비되는 화소 군집들간에서의 윤곽선 추출이 가능해진다. 본 연구의 결과는 Landsat TM 위성이미지에서 강 유역의 경계선과 교량의 중앙선 추출에 적용시켜 보았으며 ,만족할 만한 결과를 보였다.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.172-175
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• 1997

Outline contour is detected firstly to simulate dose distribution in radiation therapy planning system. In this paper, we developed automatic contour detection system using temporal and spatial relationships of image sequences. The low level image analysis involves the use of directional gradient edge operators and Laplacian operator. The High level portion of algorithm uses a knowledge-based strategy that incorporates fuzzy resoning method.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.403-408
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• 2003

A general meaning of caricaturing is that a representation, especially pictorial or literary, in which the subject's distinctive features or peculiarities are deliberately exaggerated to produce a comic or grotesque effect. In other words, a caricature is defined as a rough sketch(dessin) which is made by detecting features from human face and exaggerating or warping those. There have been developed many methods which can make a caricature image from human face using computer. In this paper, we propose a new caricaturing system. The system uses a real-time image or supplied image as an input image and deals with it on four processing steps and then creates a caricatured image finally. The four Processing steps are like that. The first step is detecting a face from input image. The second step is extracting special coordinate values as facial geometric information. The third step is deforming the face image using local warping method and the coordinate values acquired in the second step. In fourth step, the system transforms the deformed image into the better improved edge image using a fuzzy Sobel method and then creates a caricatured image finally. In this paper , we can realize a caricaturing system which is simpler than any other exiting systems in ways that create a caricatured image and does not need complex algorithms using many image processing methods like image recognition, transformation and edge detection.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.4
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• pp.372-378
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• 2014

Noise in biomedical X-ray image degrades the quality so that it might causes to decrease the accuracy of diagnosis. Especially the noise reduction techniques is quite essential for low-dose biomedical X-ray images obtained from low radiation power in order to protect patients, because their noise level is usually high to well discriminate objects. This paper proposes an efficient method to remove the noise in low-dose X-ray images while preserving the edges with diverse resolutions. In the proposed method, a noisy image is at first decomposed into several images with different resolutions in pyramidal representation, then the stable map of edge confidence is obtained from each of analyzed image using a fuzzy logic-based edge detector. This map is used to adaptively determine the parameter for guided filters, which eliminate the noise while preserving edges in the corresponding image. The filtered images in the pyramid are extended and synthesized into a resulted image using interpolation technique. The superiority of proposed method compared to the median, bilateral, and guided filters has been experimentally shown in terms of noise removal and edge preserving properties.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1224-1242
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• 2018

Aiming at motion analysis and compensation, it is essential to conduct motion detection with images. However, motion detection and tracking from low-altitude images obtained from an unmanned aerial system may pose many challenges due to degraded image quality caused by platform motion, image instability and illumination fluctuation. This research tackles these challenges by proposing a modified joint transform correlation algorithm which includes two preprocessing strategies. In spatial domain, a modified fuzzy edge detection method is proposed for preprocessing the input images. In frequency domain, to eliminate the disturbance of self-correlation items, the cross-correlation items are extracted from joint power spectrum output plane. The effectiveness and accuracy of the algorithm has been tested and evaluated by both simulation and real datasets in this research. The simulation experiments show that the proposed approach can derive satisfactory peaks of cross-correlation and achieve detection accuracy of displacement vectors with no more than 0.03pixel for image pairs with displacement smaller than 20pixels, when addition of image motion blurring in the range of 0~10pixel and 0.002variance of additive Gaussian noise. Moreover,this paper proposes quantitative analysis approach using tri-image pairs from real datasets and the experimental results show that detection accuracy can be achieved with sub-pixel level even if the sampling frequency can only attain 50 frames per second.