• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.377-380
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• 2012

본 연구에서는 학습데이터의 빈도요소를 반영하도록 수정된 구조의 FMM 신경망을 소개하고, 이로부터 패턴 분류를 위한 지식 표현을 생성하는 방법론을 제안한다. 하이퍼박스 멤버쉽함수는 5종류의 퍼지 분할을 기반으로 설정한 구간에 대하여 소속정도를 반영하여 결정하며, 각 차원별로 특징범위의 폭과 빈도 요소로부터 가중치 값이 학습된다. 본 연구에서는 제안된 이론을 수화인식 문제를 대상으로 고찰하였다. 인식 시스템의 구성은 특징추출을 위하여 3차원으로 확장된 구조의 CNN 모델을 사용하였으며, 수화패턴 데이터의 표현은 모션 히스토리 볼륨(Motion History Volume) 구조를 기반으로 하였다. 6종류의 수화패턴 동영상으로부터 27개 특징요소를 추출하고 이를 사용한 FMM 신경망의 학습과정과 지식의 추출 과정을 실험으로 보이고 그 유용성을 고찰한다.

• Journal of Korea Multimedia Society
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• v.25 no.5
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• pp.670-675
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• 2022

This study proposes an improved binarization method to improve image recognition rate. The research goal is to minimize the information loss that occurs during the binarization process, and to transform the object of the original image that cannot be determined through the transformation process into an image that can be judged. The proposed method uses a stepwise segmentation method of an image and divides blocks using prime numbers. Also, within one block, a trapezoidal type of fuzzy is applied. The fuzzy trapezoid is binarized by dividing the brightness histogram area into three parts according to the degree of membership. As a result of the experiment, information loss was minimized in general images. In addition, it was found that the converted binarized image expressed the object better than the original image in the special image in which the brightness region was tilted to one side.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.426-429
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• 2011

영상처리를 이용한 영상간의 유사도 비교 기법은 영상의 검색 및 영상의 자동 인식 등을 위한 연구로 최근 각광받고 있다. 최근 영상 처리 기법은 화소의 질적 향상 및 처리시간 최적화, 효율적인 특정 요소의 추출 등 다양한 방법으로 시도되고 있다. 특히, 영상의 유사도 비교는 유사 영상 검색과 같은 경우에 많이 쓰인다. 영상의 유사도를 비교하기 위한 기법으로는 영상 데이터의 특징에 따라 대상 영역을 여러 영역으로 나누는 영역분할 기법과 군집화, 퍼지, 유전자 알고리즘 등이 있다. 본 논문에서는 영상을 HSV 색공간으로 변환한 후 색상 값에 대하여 전역 정렬 기법을 사용하는 유사도 측정 방법을 제시한다. 전역 정렬 기법은 유전자 서열 비교 기법 중 하나로서 두 유전체의 유사도를 측정하는데 사용된다. 유사도 측정 효율을 높이기 위해 색상 값을 8단계로 양자화하여 영상의 서열을 생성하였다. 실험결과 제시한 방법을 영상 회전이나 대칭, 글자 삽입 등의 간단한 연산에 크게 영향을 받지 않는 것으로 드러났다.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.1
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• pp.56-63
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• 2016

In this study, we introduce a design of Fuzzy RBFNNs-based digit recognition system using the incremental-PCA in order to recognize the handwritten digits. The Principal Component Analysis (PCA) is a widely-adopted dimensional reduction algorithm, but it needs high computing overhead for feature extraction in case of using high dimensional images or a large amount of training data. To alleviate such problem, the incremental-PCA is proposed for the computationally efficient processing as well as the incremental learning of high dimensional data in the feature extraction stage. The architecture of Fuzzy Radial Basis Function Neural Networks (RBFNN) consists of three functional modules such as condition, conclusion, and inference part. In the condition part, the input space is partitioned with the use of fuzzy clustering realized by means of the Fuzzy C-Means (FCM) algorithm. Also, it is used instead of gaussian function to consider the characteristic of input data. In the conclusion part, connection weights are used as the extended diverse types in polynomial expression such as constant, linear, quadratic and modified quadratic. Experimental results conducted on the benchmarking MNIST handwritten digit database demonstrate the effectiveness and efficiency of the proposed digit recognition system when compared with other studies.

• Journal of the Korea Society of Computer and Information
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• v.18 no.6
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• pp.21-28
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• 2013

This paper proposes an effective fire detection approach that includes the following multiple heterogeneous algorithms: moving region detection using grey level histograms, color segmentation using fuzzy c-means clustering (FCM), feature extraction using a grey level co-occurrence matrix (GLCM), and fire classification using support vector machine (SVM). The proposed approach determines the optimal threshold values based on grey level histograms in order to detect moving regions, and then performs color segmentation in the CIE LAB color space by applying the FCM. These steps help to specify candidate regions of fire. We then extract features of fire using the GLCM and these features are used as inputs of SVM to classify fire or non-fire. We evaluate the proposed approach by comparing it with two state-of-the-art fire detection algorithms in terms of the fire detection rate (or percentages of true positive, PTP) and the false fire detection rate (or percentages of true negative, PTN). Experimental results indicated that the proposed approach outperformed conventional fire detection algorithms by yielding 97.94% for PTP and 4.63% for PTN, respectively.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.4
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• pp.306-310
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• 2002

In this paper, we realized a system that converts the Korean alphabet (Hangul) images, which are in any domain that is formed by the finger movement on the Hangul document, to the editable characters and then outputs them to the word editor. The domain of hand is separated from the sphere of document in the pre-process step of image. The centroid point of hand is drawn by the maximum circular movement method. After the system recognizes the hand with the circular pattern vector algorithm, finds out the position of finger by the distance spectrum and then draws out the sphere of selected character image by the finger movement to divide the characters into character units by applying the histogram between the Hangul characters. We standardized the characters of various sizes. We used the circular pattern vector algorithm that grafts on the fuzzy inference to divert the character images of the domain, which user wants, to the editable characters by comparing the characteristic vectors between the standard pattern character and the inputted character and by recognizing the character.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.4
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• pp.343-348
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• 2014

Face recognition system has advanctage to automatically recognize a person without causing repulsion at deteciton process. However, the face recognition system has a drawback to show lower perfomance according to illumination variation unlike the other biometric systems using fingerprint and iris. Therefore, this paper proposed a robust face recogntion method against illumination varition by slective fusion technique using both visible and infrared faces based on fuzzy linear disciment analysis(fuzzy-LDA). In the first step, both the visible image and infrared image are divided into four bands using wavelet transform. In the second step, Euclidean distance is calculated at each subband. In the third step, recognition rate is determined at each subband using the Euclidean distance calculated in the second step. And then, weights are determined by considering the recognition rate of each band. Finally, a fusion face recognition is performed and robust recognition results are obtained.

• The Journal of the Korea Contents Association
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• v.9 no.5
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• pp.84-90
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• 2009

In this paper, the new channel estimation was proposed that have the low complexity and high performance using Fuzzy inference method uses recently from various field for estimation about uncertainty in channel estimation of OFDM. Proposed method is channel estimation performance improve, calculation and interpolation for statistics character of channel using the pilot before LS channel estimation by Fuzzy inference method. Simulation result in QPSK proposed channel estimation method shows the enhancement of 5.5dB compared to the LS channel estimation and the deterioration of 1.3dB compared to the MMSE channel estimation in mean square error point $10^{-3}$. symbol error rate shows similarity performance the MMSE $10^{-1.96}$, proposed channel estimation $10^{-1.93}$ and enhancement of $10^{-0.35}$ compared to the LS channel estimation in signal to noise ratio point 20dB.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.3
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• pp.262-268
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• 2010

In this paper, this study restores the distorted image to its original image by compensating for the distortion of image from a fixed-focus camera lens. The various developments and applications of the imaging devices and the image sensors used in a wide range of industries and expanded use, but due to the needs of the small size and light weight of the camera, the distortion from acquiring images of the distorted curvature of the lens tends to affect many. In particular, the three-dimensional imaging camera, each different distortion of left and right lens cause the degradation of three-dimensional sensitivity and left-right image distortion ratio. we approached the way of generalizing the approximate equations to restore each part of left-right camera images to the coordinators of the original images. The adaptive Neuro-Fuzzy Inference System is configured for it. This system is divided from each membership function and is inferred by 1st order Sugeno Fuzzy model. The result is that the compensated images close to the left, right original images. Using low-cost and compact imaging lens by which also determine the exact three-dimensional image-sensing capabilities and will be able to expect from this study.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.4 s.310
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• pp.1-8
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• 2006

This paper proposes an improved FCM(Fuzzy C-means) algorithm using intercluster and entropy-based weight in gray image. The fuzzy clustering methods have been extensively used in the image segmentation since it extracts feature information of the region. Most of fuzzy clustering methods have used the FCM algorithm. But, FCM algorithm is still sensitive to noise, as it does not include spatial information. In addition, it can't correctly classify pixels according to the feature-based distributions of clusters. To solve these problems, we applied a weight and intercluster to the traditional FCM algorithm. A weight is obtained from the entropy information based on the cluster's number of neighboring pixels. And a membership for one pixel is given based on the information considering the feature-based intercluster. Experiments has confirmed that the proposed method was more tolerant to noise and superior to existing methods.