• 디지털산업정보학회논문지
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• 제11권4호
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• pp.89-97
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• 2015

In this paper, a method of color image segmentation based on DBSCAN(Density Based Spatial Clustering of Applications with Noise) using compactness of superpixels and texture information is presented. The DBSCAN algorithm can generate clusters in large data sets by looking at the local density of data samples, using only two input parameters which called minimum number of data and distance of neighborhood data. Superpixel algorithms group pixels into perceptually meaningful atomic regions, which can be used to replace the rigid structure of the pixel grid. Each superpixel is consist of pixels with similar features such as luminance, color, textures etc. Superpixels are more efficient than pixels in case of large scale image processing. In this paper, superpixels are generated by SLIC(simple linear iterative clustering) as known popular. Superpixel characteristics are described by compactness, uniformity, boundary precision and recall. The compactness is important features to depict superpixel characteristics. Each superpixel is represented by Lab color spaces, compactness and texture information. DBSCAN clustering method applied to these feature spaces to segment a color image. To evaluate the performance of the proposed method, computer simulation is carried out to several outdoor images. The experimental results show that the proposed algorithm can provide good segmentation results on various images.

• 한국통신학회:학술대회논문집
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• 한국통신학회 1986년도 추계학술발표회 논문집
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• pp.83-86
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• 1986

In this paper, we propose the new color image segmentation algorithm using clustering method in the normalized r,g,b coordinates. The number of intrinsic clusters which are included in color image is estimated by the clustering quality measure and the initial centers of clusters are calculated by a hierarchical way. The proposed algorithm was varified by the computer simulation.

• 전기학회논문지
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• 제61권3호
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• pp.472-478
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• 2012

The inspection machine in PCB (printed circuit board) assembly line checks assembly errors by inspecting the images inside of the component inspection region. The component inspection region consists of region of component package and region of soldering. It is necessary to extract the regions automatically for auto-teaching system of the inspection machine. We propose an image segmentation method to extract the component inspection regions automatically from images of PCB. The acquired image is transformed to HSI color model, and then segmented by several regions by clustering method. We develop a modified K-means algorithm to increase the accuracy of extraction. The heuristics generating the initial clusters and merging the final clusters are newly proposed. The vertical and horizontal projection is also developed to distinguish the region of component package and region of soldering. The experimental results are presented to verify the usefulness of the proposed method.

• 대한기계학회논문집
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• 제17권11호
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• pp.2711-2722
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• 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.

• 한국통신학회논문지
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• 제21권4호
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• pp.848-854
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• 1996

Many image display devices allow only a limited number of colors to be simultaneously displayed. in disphaying of natural color image using color palette, it is necessary to construct an optimal color palette and the optimal mapping of each pixed of the original image to a color from the palette. In this paper, we proposed the clustering algorithm using local region block centered one color cluster in the prequantized 3-D histogram. Cluster pairs which have the least distortion error are merged by considering distortion measure. The clustering process is continued until to obtain the desired number of colors. The same as the clustering process, original color value. The proposed algorithm incroporated with a spatial activity weighting value which is reflected sensitivity of HVS quantization errors in smoothing region. This method produces high quality display images and considerably reduces computation time.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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• pp.111-114
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• 2008

The existence of noise bands may deform the typical shape of the spectrum, making the accuracy of clustering degraded. This paper proposes a statistical approach to remove noise bands in hyperspectral data using the correlation coefficient of bands as an indicator. Considering each band as a random variable, two adjacent signal bands in hyperspectral data are highly correlative. On the contrary, existence of a noise band will produce a low correlation. For clustering, the unsupervised ${\kappa}$-nearest neighbor clustering method is implemented in accordance with three well-accepted spectral matching measures, namely ED, SAM and SID. Furthermore, this paper proposes a hierarchical scheme of combining those measures. Finally, a separability assessment based on the between-class and the within-class scatter matrices is followed to evaluate the applicability of the proposed noise band removal method. Also, the paper brings out a comparison for spectral matching measures.

• Smart Structures and Systems
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• 제8권1호
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• pp.69-92
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• 2011

This paper presents an emergent pattern recognition approach based on the immune network theory and hierarchical clustering algorithms. The immune network allows its components to change and learn patterns by changing the strength of connections between individual components. The presented immune-network-based approach achieves emergent pattern recognition by dynamically generating an internal image for the input data patterns. The members (feature vectors for each data pattern) of the internal image are produced by an immune network model to form a network of antibody memory cells. To classify antibody memory cells to different data patterns, hierarchical clustering algorithms are used to create an antibody memory cell clustering. In addition, evaluation graphs and L method are used to determine the best number of clusters for the antibody memory cell clustering. The presented immune-network-based emergent pattern recognition (INEPR) algorithm can automatically generate an internal image mapping to the input data patterns without the need of specifying the number of patterns in advance. The INEPR algorithm has been tested using a benchmark civil structure. The test results show that the INEPR algorithm is able to recognize new structural damage patterns.

• 한국표면공학회지
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• 제55권3호
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• pp.164-172
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• 2022

Hot-dip galvanized steel(GI) is widely used throughout the industry as a corrosion resistance material. Corrosion of steel is a common phenomenon that results in the gradual degradation under various environmental conditions. Corrosion monitoring is to track the degradation progress for a long time. Corrosion on steel plate appears as discoloration and any irregularities on the surface. This study developed a quantitative evaluation method of the rust formed on GI steel plate using a superpixel-based DBSCAN clustering method and k-means clustering from the corroded area in a given image. The superpixel-based DBSCAN clustering method decrease computational costs, reaching automatic segmentation. The image color of the rusty surface was analyzed quantitatively based on HSV(Hue, Saturation, Value) color space. In addition, two segmentation methods are compared for the particular spatial region using their histograms.

• 한국정보처리학회논문지
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• 제7권9호
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• pp.3029-3036
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• 2000

영상분할은 컴퓨터비전 시스템에서 영상정보추출의 중요한 과정 중의 하나이다. 이중에서 퍼지 클러스터링 방법은 영상분할에 광범위하게 사용되고 있다. 대부분의 퍼지 클러스터링 방법으로는 FCM 알고리즘이 사용된다. 그러나 FCM 알고리즘은 클러스터의 중심과 데이터간의 거리에 의존하기 때문에 클러스터 크기가 다를 경우에는 데이터가 오분류될 수 있다. 본 논문에서는 클러스트 크기에 상관없이 데이터를 분류할 수 있는 평균내부거리를 이용한 퍼지 클러스터링 알고리즘을 제안하였다. 평균내부거리는 각 데이터로부터 해당 클러스터 중심까지의 거리를 평균한 값으로 클러스터의 크기와 밀도에 비례한다. 실험 결과를 통하여 제안된 방법이 분류 엔트로피와 적합도 함수에 의해서 좋은 결과를 보여주고 있음을 증명하였다.

• 정보처리학회논문지B
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• 제12B권1호
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• pp.31-38
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• 2005

본 논문은 컬러 인접성과 클러스터링 기법을 이용한 객체 기반 영상 검색 기법을 제안한다. 컬러 인접성이란 영상내의 서로 이웃한 영역에서 나타나는 컬러의 특징값을 말하고, 영상 데이터베이스로부터 사용자가 찾고자하는 영역과 유사한 후보 영역들을 우선 추출하는데 사용된다. 또한 클러스터링 기법은 후보 영역들 가운데 객체가 존재하는 영역만을 추출하는데 사용되고, 질의 영상과 데이터베이스 영상 사이의 유사도 측정을 위하여 히스토그램 인터섹션(histogram intersection) 방법이 사용된다. 제안하는 방법에서 사용되는 영상의 컬러쌍 정보는 객체의 이동, 회전 그리고 크기 변화에 강건하며, 실험을 통하여 제안하는 방법이 기존의 방법보다 우수함을 확인하였다.