• Proceedings of the IEEK Conference
• /
• 1999.06a
• /
• pp.583-586
• /
• 1999

In this paper, the wavelet transform is performed in the input 256$\times$256 color image and decomposes a image into low-pass and high-pass components. Since the high-pass band contains the components of three directions, edges are detected by combining three parts. After finding the position of face using the histogram of the edge component, a face region in low-pass band is cut off. Since RGB color image is sensitively affected by luminances, the image of low pass component is normalized, and a facial region is detected using face color informations. As the wavelet transform decomposes the detected face region into three layer, the dimension of input image is reduced. In this paper, we use the 3000 images of 10 persons, and KL transform is applied in order to classify face vectors effectively. FCM(Fuzzy C-Means) algorithm classifies face vectors with similar features into the same cluster. In this case, the number of cluster is equal to that of person, and the mean vector of each cluster is used as a codebook. We verify the system performance of the proposed algorithm by the experiments. The recognition rates of learning images and testing image is computed using correlation coefficient and Euclidean distance.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.17 no.11
• /
• pp.2711-2722
• /
• 1993

Most of the current 2-D object recognition systems are model-based. In such systems, the representation of each of a known set of objects are precompiled and stored in a database of models. Later, they are used to recognize the image of an object in each instance. In this thesis, the approach method for the 2-D object recognition is treating an object boundary as a string of structral units and utilizing string matching to analyze the scenes. To reduce string matching time, models are rebuilt by means of fuzzy c-means clustering algorithm. In this experiments, the image of objects were taken at initial position of a robot from the CCD camera, and the models are consturcted by the proposed algorithm. After that the image of an unknown object is taken by the camera at a random position, and then the unknown object is identified by a comparison between the unknown object and models. Finally, the amount of translation and rotation of object from the initial position is computed.

• Proceedings of the Korean Information Science Society Conference
• /
• 2002.10d
• /
• pp.598-600
• /
• 2002

영상에서 타원을 추출하는 것은 얼굴 인식, 홍채 인식과 같은 컴퓨터 비전분야에서 인식할 영역을 찾는 방법으로 상당히 유용하게 사용된다. 본 논문에서는 기존의 퍼지 C-means 기법이 초기의 클러스터 개수와 중심 값에 따라서 결과가 민감하다는 단점을 보완한 개선된 퍼지 C-means 기법을 타원 추출에 적용한다. 이것은 영상 분할(Segmentation)로부터 후보 초기 클러스터 개수 및 초기 클러스터 중심을 결정하는 방법으로서 본 논문에서는 이 기법으로 영상 클러스터링을 수행하여 타원 영역 추출에 필요한 타원 후보 영역의 최소 인접 사각형(Minimum Enclosed Rectangle)을 찾아낸다. 이렇게 찾아진 최소 인접 사각형에 대해서 면적에 맞는 초기 타원들을 영역 내에 설정한 뒤 적합도(fittness)검사를 기반으로 한 타원 검증을 실시하고 적합도가 높은 영역을 타원 영역으로 추출한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• v.9 no.2
• /
• pp.403-407
• /
• 2005

자궁 경부 세포진 영상의 핵 영역 분할은 자궁 경부암 자동화 검색 시스템의 가장 어렵고도 중요한 분야로 알려져 있다. 본 논문에서는 자궁 경부 세포진 영상에서 HSI 모델을 이용하여 세포진 핵 영역을 추출한다. 추출된 세포진 핵 영역은 형태학적 정보(morphometric feature)와 명암 정보(densitometric feature), 색상 정보(colorimetric feature), 질감 정보(textural features)를 분석하여 핵의 특징을 추출한다. 또한 Bethesda System에서의 분류 기준에 따라 핵의 분류 기준을 정하고 추출된 핵의 특징들을 퍼지 c-Means 클러스터링 알고리즘에 적용하여 실험한 결과, 제안된 방법이 자궁 세포진 핵 추출과 인식에 있어서 효율적임을 확인하였다.

• The KIPS Transactions:PartC
• /
• v.8C no.3
• /
• pp.311-318
• /
• 2001

본 논문에서는 연결수락 제어시 사용자가 전송하는 트래픽 파라메타(샐 개수의 분산값과 평균값)를 압축하여 망에 신고하는 방법을 제안하고, 압축방법에 의한 연결수락제어의 성능을 분석 비교한다. 트래픽 파라메타 압축방법은 K-means, CL(Competitive Learning), Fuzzy ISODATA,FNC(Fuzzy Neural Clustering)를 사용한다. 제안한 트래픽 파라메타의 압축에 의한 연결수락제어는 퍼지 매핑함수(Fuzzy Mapping Funciton)fp 의해 신고한 트래픽 패턴을 추정하고, 전방향 구조의 신경망을 사용하여 연결의 수락/거절을 결정한다. ON-OFF 트래픽 모델 환경에서 컴퓨터 실험을 통하여 여러 가지 압축방법들을 사용한 연결수락제어의 성능을 Fuzziness 값에 따라 비교하였고, 그 결과 FNC 방법이 우수함을 알 수 있었다. EH한 연결수락제어의 성능을 높히기 위해서 관측 프레임의 셀 분산값이 크면 Fuzziness 값을 작게 선정하고, 작으면 상대적으로 크게 선정해야 함을 알 수 있었다.

• Proceedings of the IEEK Conference
• /
• 2002.07a
• /
• pp.285-288
• /
• 2002

Compression of magnetic resonance images (MRI) has proved to be more difficult than other medical imaging modalities. In an average sized hospital, many tora bytes of digital imaging data (MRI) are generated every year, almost all of which has to be kept. The medical image compression is currently being performed by using different algorithms. In this paper, Fuzzy C-Means (FCM) algorithm is used for the Vector Quantization (VQ). First, a digital image is divided into subblocks of fixed size, which consists of 4${\times}$4 blocks of pixels. By performing 2-D Discrete Cosine Transform (DCT), we select six DCT coefficients to form the feature vector. And using FCM algorithm in constructing the VQ codebook. By doing so, the algorithm can make good time quality, and reduce the processing time while constructing the VQ codebook.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.09a
• /
• pp.476-479
• /
• 2003

The Rough transform (HT) is often used for extracting global features in binary images, for example curve and line segments, from local features such as single pixels. The HT is useful due to its insensitivity to missing edge points and occlusions, and robustness in noisy images. However, it possesses some disadvantages, such as time and memory consumption due to the number of input data and the selection of an optimal and efficient resolution of the accumulator space can be difficult. Another problem of the HT is in the difficulty of peak detection due to the discrete nature of the image space and the round off in estimation. In order to resolve the problem mentioned above, a possibilistic C-means approach to clustering [1] is used to cluster neighboring peaks. Several experimental results are given.

• Proceedings of the KIEE Conference
• /
• 2006.10c
• /
• pp.217-219
• /
• 2006

본 연구는 정보입자와 유전알고리즘의 기호코딩을 통해 퍼지집합 기반 다항식 뉴럴네트워크(IG based gFSPNN)의 최적 설계 제안한다. 기존의 Furry Srt-based Polynomial Neural Networks의 최적설계를 위해 유전자 알고리즘의 이진코딩을 사용하였다. 이지코딩은 스티링 길이 때문에 연산시간이 급격히 증가되는 현상과 해밍절벽(Hamming Cliff)에 따른 급격한 비트변환이 힘들다는 단점이 내제 하였다. 이에 본 논문에서는 스티링 길이와 해밍절벽에 따른 문제를 해결 하기위해 기호코딩을 사용하였다._데이터들의 특성을 모델에 반영하기 위해 Hard C-Means(HCM)을 결합한 Information Granulation(IG)을 사용하여 최적모델 구축 속도를 빠르게 하였다. 실험적 예제를 통하여 제안된 모델의 성능을 평가한다.

• Journal of Computing Science and Engineering
• /
• v.10 no.1
• /
• pp.1-8
• /
• 2016

The fuzzy c-means (FCM) is a frequently utilized algorithm at present. Yet, the clustering quality and convergence rate of FCM are determined by the initial cluster centers, and so an improved FCM algorithm based on canopy cluster concept to quickly analyze the dataset has been proposed. Taking advantage of the canopy algorithm for its rapid acquisition of cluster centers, this algorithm regards the cluster results of canopy as the input. In this way, the convergence rate of the FCM algorithm is accelerated. Meanwhile, the MapReduce scheme of the proposed FCM algorithm is designed in a cloud environment. Experimental results demonstrate the hybrid canopy-FCM clustering algorithm processed by MapReduce be endowed with better clustering quality and higher operation speed.

• Journal of information and communication convergence engineering
• /
• v.6 no.1
• /
• pp.94-99
• /
• 2008

Segmentation for the region of nucleus in the image of uterine cervical cytodiagnosis is known as the most difficult and important part in the automatic cervical cancer recognition system. In this paper, the region of nucleus is extracted from an image of uterine cervical cytodiagnosis using the HSI model. The characteristics of the nucleus are extracted from the analysis of morphemetric features, densitometric features, colormetric features, and textural features based on the detected region of nucleus area. The classification criterion of a nucleus is defined according to the standard categories of the Bethesda system. The fuzzy C-means clustering algorithm is employed to the extracted nucleus and the results show that the proposed method is efficient in nucleus recognition and uterine cervical Pap-Smears extraction.