• 한국인터넷방송통신학회논문지
• /
• 제17권2호
• /
• pp.147-157
• /
• 2017

최근에는 슈퍼-픽셀 (super-pixel)은 컴퓨터 발전 응용에 널리 사용되고 있다. 슈퍼 픽셀 알고리즘은 픽셀을 지각적으로 실행이 가능한 영역으로 변환하여 그리드 픽셀의 경직된 특징을 줄일 수 있다. 특히, 슈퍼 픽셀은 깊이 추정, 골격 작업, 바디 라벨링 및 기능 국소화 등에 사용된다. 그러나 이러한 작업을 수행하기 위해 우수한 슈퍼 픽셀 파티션을 생성하는 것은 쉽지 않다. 특히 슈퍼 픽셀은 비합, 지속, 폐쇄, 지각 불변과 같은 형태 측면을 고려할 때보다 의미있는 특징을 만족시키지는 못한다. 본 논문에서는 단순 선형 반복 클러스터링과 퍼지 클러스터링 개념을 결합한 고급 알고리즘을 제안한다. 단순 선형 반복 클러스터링 기술은 이미지 경계, 속도, 메모리 효율이 기존 방법보다 높다. 그것은 형태 측면의 맥락에서 슈퍼 픽셀 형태에 대해 양호하게 작거나 규칙적인 특성을 제안하는 것은 아니다. 퍼지 유사성 측정은 제한된 크기와 이웃을 고려하여 합리적인 그래프를 제공한다. 보다 작고 규칙적인 픽셀을 얻으며 부분적으로 관련된 특징을 추출 할 수 있다. 시뮬레이션은 퍼지 유사성 기반 슈퍼 픽셀 생성은 사람의 이미지를 분해하는 방식으로 자연적 특징을 대표적으로 나타낸다.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅲ
• /
• pp.1507-1510
• /
• 2003

We propose UPGMA(Unweighted Pair Group Method using Average distance) as hierarchical clustering to detect abrupt shot changes using multiple features such as pixel-by-pixel difference, global and local histogram difference. Conventional $\kappa$-means algorithm which is a method of the partitional clustering, has to select an efficient initial cluster center adaptively UPGMA that we propose, does not need initial cluster center because of agglomerative algorithm that it starts from each sample for clusters. And UPGMA results in stable performance. Experiment results show that the proposed algorithm works not only well but also stably.

• 대한원격탐사학회지
• /
• 제34권1호
• /
• pp.151-166
• /
• 2018

This paper compares the four selections of performance used in the application of genetic algorithms (GAs) to automatically optimize multispectral pixel cluster for unsupervised classification from KOMPSAT-3 data, since the selection among three main types of operators including crossover and mutation is the driving force to determine the overall operations in the clustering GAs. Experimental results demonstrate that the tournament selection obtains a better performance than the other selections, especially for both the number of generation and the convergence rate. However, it is computationally more expensive than the elitism selection with the slowest convergence rate in the comparison, which has less probability of getting optimum cluster centers than the other selections. Both the ranked-based selection and the proportional roulette wheel selection show similar performance in the average Euclidean distance using the pixel clustering, even the ranked-based is computationally much more expensive than the proportional roulette. With respect to finding global optimum, the tournament selection has higher potential to reach the global optimum prior to the ranked-based selection which spends a lot of computational time in fitness smoothing. The tournament selection-based clustering GA is used to successfully classify the KOMPSAT-3 multispectral data achieving the sufficient the matic accuracy assessment (namely, the achieved Kappa coefficient value of 0.923).

• 로봇학회논문지
• /
• 제14권4호
• /
• pp.301-310
• /
• 2019

A new algorithm has been propose to detect the reflected light region as disturbances in a real-time vision system. There have been several attempts to detect existing reflected light region. The conventional mathematical approach requires a lot of complex processes so that it is not suitable for a real-time vision system. On the other hand, when a simple detection process has been applied, the reflected light region can not be detected accurately. Therefore, in order to detect reflected light region for a real-time vision system, the detection process requires a new algorithm that is as simple and accurate as possible. In order to extract the reflected light, the proposed algorithm has been adopted several filter equations and clustering processes in the HSI (Hue Saturation Intensity) color space. Also the proposed algorithm used the pre-defined reflected light data generated through the clustering processes to make the algorithm simple. To demonstrate the effectiveness of the proposed algorithm, several images with the reflected region have been used and the reflected regions are detected successfully.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2004년도 ICEIC The International Conference on Electronics Informations and Communications
• /
• pp.447-450
• /
• 2004

This paper proposes remotely sensed image classification method by fuzzy c-means clustering algorithm using average intra-cluster distance. The average intra-cluster distance acquires an average of the vector set belong to each cluster and proportionates to its size and density. We perform classification according to pixel's membership grade by cluster center of fuzzy c-means clustering using the mean-values of training data about each class. Fuzzy c-means algorithm considered membership degree for inter-cluster of each class. And then, we validate degree of overlap between clusters. A pixel which has a high degree of overlap applies to the maximum likelihood classification method. Finally, we decide category by comparing with fuzzy membership degree and likelihood rate. The proposed method is applied to IKONOS remote sensing satellite image for the verifying test.

• 한국방송∙미디어공학회:학술대회논문집
• /
• 한국방송공학회 2012년도 하계학술대회
• /
• pp.294-297
• /
• 2012

본 논문에서는 컬러 영상의 검색을 위하여 영상을 색상 정보에 기반한 pixel layer (cluster)의 집합체로 모델링하고, 두 layer 간의 유사도를 각 layer 를 이루는 pixel 들의 색상 분포에 따른 공간적 분포를 이용하여 측정하는 기법을 제안한다. 먼저 pixel layering 단계에서는 HSV 색 공간에서 mean-shift clustering 알고리즘을 통해 초기 layer 들을 얻고, 비슷한 색상의 layer 들은 합쳐 영상의 soft segmentation 과 유사한 결과를 얻는다. 비교할 두 영상에서 pixel layering 을 한 후, 각 layer 를 이진화된 공간분포 지도로 형성하고 그 차이를 비교함으로써 유사도를 측정한다. 이 때, 사용하는 가중치로서 HSV 색 공간 분포의 비슷한 정도를 정의하는데, 이는 HSV 색 공간을 XYZ 의 3 차원 좌표로 설정하고, overlap 되는 pixel 수로 정의하였다. 본 논문에서 제안한 pixel layer 들 간의 색상 공간 분포에 따른 공간적 분포를 이용한 영상 검색 기법은 MPEG-7 에서 정의한 대표색상 기반의 영상 검색보다 우수한 성능을 보여주었다.

• 정보저장시스템학회논문집
• /
• 제6권1호
• /
• pp.31-36
• /
• 2010

Data storage related with writing and retrieving requires high storage capacity, fast transfer rate and less access time. Today any data storage system cannot satisfy these conditions, however holographic data storage system can perform faster data transfer rate because it is a page oriented memory system using volume hologram in writing and retrieving data. System can be constructed without mechanical actuating part so fast data transfer rate and high storage capacity about 1Tb/cm3 can be realized. In this research, to correct errors of binary data stored in holographic data storage system, a new method for reduction errors is suggested. First, find cluster centers using subtractive clustering algorithm then reduce intensities of pixels around cluster centers. By using this error reduction method following results are obtained ; the effect of Inter Pixel Interference noise in the holographic data storage system is decreased and the intensity profile of data page becomes uniform therefore the better data storage system can be constructed.

• Journal of information and communication convergence engineering
• /
• 제20권3호
• /
• pp.153-159
• /
• 2022

Plain radiographic analysis is the initial imaging modality for suspected small bowel obstruction. Among the many features that affect the diagnosis of small bowel obstruction (SBO), the presence of gas-filled or fluid-filled small bowel loops is the most salient feature that can be automatized by computer vision algorithms. In this study, we compare three frequently applied pixel-clustering algorithms for extracting gas-filled areas without human intervention. In a comparison involving 40 suspected SBO cases, the Possibilistic C-Means and Fuzzy C-Means algorithms exhibited initialization-sensitivity problems and difficulties coping with low intensity contrast, achieving low 72.5% and 85% success rates in extraction. The Adaptive Resonance Theory 2 algorithm is the most suitable algorithm for gas-filled region detection, achieving a 100% success rate on 40 tested images, largely owing to its dynamic control of the number of clusters.

• Journal of Information Processing Systems
• /
• 제12권4호
• /
• pp.555-576
• /
• 2016

In the conventional clustering algorithms, an object could be assigned to only one group. However, this is sometimes not the case in reality, there are cases where the data do not belong to one group. As against, the fuzzy clustering takes into consideration the degree of fuzzy membership of each pixel relative to different classes. In order to overcome some shortcoming with traditional clustering methods, such as slow convergence and their sensitivity to initialization values, we have used the Harmony Search algorithm. It is based on the population metaheuristic algorithm, imitating the musical improvisation process. The major thrust of this algorithm lies in its ability to integrate the key components of population-based methods and local search-based methods in a simple optimization model. We propose in this paper a new unsupervised clustering method called the Fuzzy Harmony Search-Fourier Transform (FHS-FT). It is based on hybridization fuzzy clustering and the harmony search algorithm to increase its exploitation process and to further improve the generated solution, while the Fourier transform to increase the size of the image's data. The results show that the proposed method is able to provide viable solutions as compared to previous work.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• 제27권2호
• /
• pp.146-159
• /
• 2023

This paper proposes a hybrid algorithm combining K-means clustering and watershed algorithms for flower segmentation and counting. We use the K-means clustering algorithm to obtain the main colors in a complex background according to the cluster centers and then take a color space transformation to extract pixel values for the hue, saturation, and value of flower color. Next, we apply the threshold segmentation technique to segment flowers precisely and obtain the binary image of flowers. Based on this, we take the Euclidean distance transformation to obtain the distance map and apply it to find the local maxima of the connected components. Afterward, the proposed algorithm adaptively determines a minimum distance between each peak and apply it to label connected components using the watershed segmentation with eight-connectivity. On a dataset of 30 images, the test results reveal that the proposed method is more efficient and precise for the counting of overlapped flowers ignoring the degree of overlap, number of overlap, and relatively irregular shape.