• 제목/요약/키워드: color image segmentation

검색결과 411건 처리시간 0.029초

퍼지 클러스터링을 이용한 칼라 영상 분할 (A study on the color image segmentation using the fuzzy Clustering)

  • 이재덕;엄경배
    • 한국정보통신학회:학술대회논문집
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    • 한국해양정보통신학회 1999년도 춘계종합학술대회
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    • pp.109-112
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    • 1999
  • Image segmentation is the critical first step in image information extraction for computer vision systems. Clustering methods have been used extensively in color image segmentation. Most analytic fuzzy clustering approaches are divided from the fuzzy c-means(FCM) algorithm. The FCM algorithm uses fie probabilistic constraint that the memberships of a data point across classes sum to 1. However, the memberships resulting from the FCM do not always correspond to the intuitive concept of degree of belonging or compatibility. Moreover, the FCM algorithm has considerable trouble under noisy environments in the feature space. Recently, a possibilistic approach to clustering(PCM) for solving above problems was proposed. In this paper, we used the PCM for color image segmentation. This approach differs from existing fuzzy clustering methods for color image segmentation in that the resulting partition of the data can be interpreted as a possibilistic partition. So, the problems in the FCM can be solved by the PCM. But, the clustering results by the PCM are not smoothly bounded, and they often have holes. The region growing was used as a postprocessing after smoothing the noise points in the pixel seeds. In our experiments, we illustrate that the PCM us reasonable than the FCM in noisy environments.

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차감 및 중력 fuzzy C-means 클러스터링을 이용한 칼라 영상 분할에 관한 연구 (Segmentation of Color Image by Subtractive and Gravity Fuzzy C-means Clustering)

  • 진영근;김태균
    • 전기전자학회논문지
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    • 제1권1호
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    • pp.93-100
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    • 1997
  • 칼라 영상 분할의 한 방법으로 fuzzy C-means를 이용한 방법이 많이 연구되었으나, 이 방법은 클러스터의 개수가 정해져야 사용할 수 있는 방법이다. 분할해야 할 데이터가 많은 경우 예비 분할을 수행하여 예비 분할 되지 않는 데이터들에 대해서 상세 분할을 fuzzy C-means를 사용하여 분할 하나 예비 분할된 데이터의 클러스터 중심과 상세 분할로 만들어진 클러스터의 중심과는 연계성이 없어진다. 본 연구에서는 이것을 보완하기 위하여 차감 클러스터링을 사용하여 칼라 영상의 클러스터의 개수와 중심을 구한 후, 이것을 이용하여 영상을 예비 분할하고 중력을 가진 fuzzy C-means를 사용하여 분할되지 않은 나머지 부분과 클러스터의 중심을 최적화 시켜 분할하는 알고리듬을 제안한다. 제안된 방법의 정성적인 평가를 수행하여 본 논문에서 제시된 방법이 우수함을 보인다.

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컬러 영상 분할 기법을 활용한 치아 영역 자동 검출 (Image Segmentation of Teeth Region by Color Image Analysis)

  • 이성택;김경섭;윤태호;김기덕;박원서
    • 전기학회논문지
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    • 제58권6호
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    • pp.1207-1214
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    • 2009
  • In this study, we propose a novel color-image segmentation algorithm to discern the teeth region utilizing RG intensity and its relevant RGB histogram features with resolving the variations of its maximum intensity in terms of peaks and valleys. Tooth candidates in a CCD image are first extracted by applying RGB color multi-threshold levels and consequently the successive morphological image operations and a Sobel-mask edge processing are performed to resolve the teeth region and its contour.

칼라 영상 분할을 위한 경계선 보존 영역 병합 방법 (Region Merging Method Preserving Object Boundary for Color Image Segmentation)

  • 유창연;곽내정;김영길;안재형
    • 한국멀티미디어학회논문지
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    • 제7권3호
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    • pp.319-326
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    • 2004
  • 본 논문에서는 물체의 경계선을 고려한 칼라 영상 분할 방법을 제안한다. 제안 방법은 먼저 원영상을 벡터 양자화한 후 양자화된 영상의 인덱스 맵을 이용하여 초기 영역을 설정하였다. 그 후 HSI컬러 공간을 이용한 영역 병합에서 물체의 경계선을 고려하기 위해 경계선 제한 성분을 적용하여 영역들을 병합하였다. 또한 RGB 컬러 공간을 이용하여 HSI 컬러 공간에서 병합되지 않은 영역들을 병합하였다. 그리 고 영역병합 알고리즘을 통해 반복적인 처리를 감소시킴으로써 처리 시간을 줄였다. 실험 결과에서는 다양한 영상에 대해 주요 영역들의 분할 결과 및 처리소요시간에서 우수한 성능을 보였다.

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슈퍼픽셀의 밀집도 및 텍스처정보를 이용한 DBSCAN기반 칼라영상분할 (A Method of Color Image Segmentation Based on DBSCAN(Density Based Spatial Clustering of Applications with Noise) Using Compactness of Superpixels and Texture Information)

  • 이정환
    • 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.

워터쉐드 기법을 이용한 개별적 치아 영역 자동 검출 (Individual Tooth Image Segmentation by Watershed Algorithm)

  • 이성택;김경섭;윤태호
    • 전기학회논문지
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    • 제59권1호
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    • pp.210-216
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    • 2010
  • In this study, we propose a novel method to segment an individual tooth region in a true color image. The difference of the intensity in RGB is initially extracted and subsequent morphological reconstruction is applied to minimize the spurious segmentation regions. Multiple seeds in the tooth regions are chosen by searching regional minima and a Sobel-mask edge operations is performed to apply MCWA(Marker-Controlled Watershed Algorithm). As the results of applying MCWA transform for our proposed tooth segmentation algorithm, the individual tooth region can be resolved in a CCD tooth color image.

Possibilistic C-mean 클러스터링과 영역 확장을 이용한 칼라 영상 분할 (Color image segmentation using the possibilistic C-mean clustering and region growing)

  • 엄경배;이준환
    • 전자공학회논문지S
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    • 제34S권3호
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    • pp.97-107
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    • 1997
  • Image segmentation is teh important step in image infromation extraction for computer vison sytems. Fuzzy clustering methods have been used extensively in color image segmentation. Most analytic fuzzy clustering approaches are derived from the fuzzy c-means (FCM) algorithm. The FCM algorithm uses th eprobabilistic constraint that the memberships of a data point across classes sum to 1. However, the memberships resulting from the FCM do not always correspond to the intuitive concept of degree of belongingor compatibility. moreover, the FCM algorithm has considerable trouble above under noisy environments in the feature space. Recently, the possibilistic C-mean (PCM) for solving growing for color image segmentation. In the PCM, the membersip values may be interpreted as degrees of possibility of the data points belonging to the classes. So, the problems in the FCM can be solved by the PCM. The clustering results by just PCM are not smoothly bounded, and they often have holes. So, the region growing was used as a postprocessing. In our experiments, we illustrated that the proposed method is reasonable than the FCM in noisy enviironments.

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이방성 확산과 형태학적 연산을 이용한 영상 분할 (Image Segmentation Using Anisotropic Diffusion and Morphology Operation)

  • 김희숙;조정래;임숙자
    • 디지털산업정보학회논문지
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    • 제5권2호
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    • pp.157-165
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    • 2009
  • Existing methods for image segmentation using diffusion can't preserve contour information, or noises with high gradients become more salient as the umber of times of the diffusion increases, resulting in over-segmentation when applied to watershed. This thesis proposes a method for image segmentation by applying morphology operation together with robust anisotropic diffusion. For an input image, transformed into LUV color space, closing by reconstruction and anisotropic diffusion are applied to obtain a simplified image which preserves contour information with noises removed. With gradients computed from this simplifed images, watershed algorithm is applied. Experiments show that color images are segmented very effectively without over-segmentation.

엔트로피에 기반한 영상분할을 이용한 영상검색 (Image Retrieval Using Entropy-Based Image Segmentation)

  • 장동식;유헌우;강호증
    • 제어로봇시스템학회논문지
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    • 제8권4호
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    • pp.333-337
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    • 2002
  • A content-based image retrieval method using color, texture, and shape features is proposed in this paper. A region segmentation technique using PIM(Picture Information Measure) entropy is used for similarity indexing. For segmentation, a color image is first transformed to a gray image and it is divided into n$\times$n non-overlapping blocks. Entropy using PIM is obtained from each block. Adequate variance to perform good segmentation of images in the database is obtained heuristically. As variance increases up to some bound, objects within the image can be easily segmented from the background. Therefore, variance is a good indication for adequate image segmentation. For high variance image, the image is segmented into two regions-high and low entropy regions. In high entropy region, hue-saturation-intensity and canny edge histograms are used for image similarity calculation. For image having lower variance is well represented by global texture information. Experiments show that the proposed method displayed similar images at the average of 4th rank for top-10 retrieval case.

안개영상의 의미론적 분할 및 안개제거를 위한 심층 멀티태스크 네트워크 (Deep Multi-task Network for Simultaneous Hazy Image Semantic Segmentation and Dehazing)

  • 송태용;장현성;하남구;연윤모;권구용;손광훈
    • 한국멀티미디어학회논문지
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    • 제22권9호
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    • pp.1000-1010
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    • 2019
  • Image semantic segmentation and dehazing are key tasks in the computer vision. In recent years, researches in both tasks have achieved substantial improvements in performance with the development of Convolutional Neural Network (CNN). However, most of the previous works for semantic segmentation assume the images are captured in clear weather and show degraded performance under hazy images with low contrast and faded color. Meanwhile, dehazing aims to recover clear image given observed hazy image, which is an ill-posed problem and can be alleviated with additional information about the image. In this work, we propose a deep multi-task network for simultaneous semantic segmentation and dehazing. The proposed network takes single haze image as input and predicts dense semantic segmentation map and clear image. The visual information getting refined during the dehazing process can help the recognition task of semantic segmentation. On the other hand, semantic features obtained during the semantic segmentation process can provide cues for color priors for objects, which can help dehazing process. Experimental results demonstrate the effectiveness of the proposed multi-task approach, showing improved performance compared to the separate networks.