• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2009년도 추계학술발표대회
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• pp.343-344
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• 2009

The aim in this paper is to show how to extract scalp of a series of brain MR images by using region growing segmentation algorithm. Most researches are all forces on the segmentation of skull, gray matter, white matter and CSF. Prior to the segmentation of these inner objects in brain, we segmented the scalp and the brain from the MR images. The scalp mask makes us to quickly exclude background pixels with intensities similar those of the skull, while the brain mask obtained from our brain surface. We make use of connected threshold method (CTM) and confidence connected method (CCM). Both of them are two implementations of region growing in Insight Toolkit (ITK). By using these two methods, the results are displayed contrast in the form of 2D and 3D scalp images.

• 디지털콘텐츠학회 논문지
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• 제5권4호
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• pp.257-263
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• 2004

본 논문은 객체 기반 스테레오 정합에 있어서, 분할영역 내부에 존재하는 단계적인 변위의 변화를 추정할 수 있는 스테레오 정합 알고리즘에 관한 것이다. 우산 분할영역을 효과적으로 표현할 수 있는 복수의 샘플점들을 선정한 다음에 각 샘플점 주위에 인접한 영역 내부의 미소영역을 취하여 스테레오 정합을 수행한다. 이후, 선택된 각 샘플점의 변위에 의한 평면의 방정식을 이용하여 내부 변위들을 보간한다. 제안된 방법에 따르면, 샘플점에서 얻어진 변위 값을 영역내부로 전파시킴으로써 깊이 추정이 단지 특징점들에서만 국한되는 특징 기반 기법의 문제점을 해결할 수 있으며, 또한 분할영역의 외곽선에서 샘플점을 추출함으로써 단순 영역에서의 깊이 추정이 모호하게 되는 영역기반 기법의 문제점을 효과적으로 억제시킬 수 있다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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• pp.126-129
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• 2008

In this paper, we proposed a method extracting an object from background of the satellite image. The image segmentation techniques have been widely studied for the technology to segment image and to synthesis segment object with other images. Proposed algorithm is to perform the edge detection of a selected object using genetic algorithm. We segment region of object based on detection edge using watershed algorithm. We separated background and object in indefinite region using gradual region merge from segment object. And, we make GUI for the application of the proposed algorithm to various tests. To demonstrate the effectiveness of the proposed method, several analysis on the satellite images are performed.

• 대한원격탐사학회지
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• 제20권3호
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• pp.215-225
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• 2004

A multistage hierarchical clustering technique, which is an unsupervised technique, was suggested in this paper for classifying large remotely-sensed imagery. The multistage algorithm consists of two stages. The 'local' segmentor of the first stage performs region-growing segmentation by employing the hierarchical clustering procedure of CN-chain with the restriction that pixels in a cluster must be spatially contiguous. The 'global' segmentor of the second stage, which has not spatial constraints for merging, clusters the segments resulting from the previous stage, using the conventional agglomerative approach. Using simulation data, the proposed method was compared with another hierarchical clustering technique based on 'mutual closest neighbor.' The experimental results show that the new approach proposed in this study considerably increases in computational efficiency for larger images with a low number of bands. The technique was then applied to classify the land-cover types using the remotely-sensed data acquired from the Korean peninsula.

• 한국통신학회논문지
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• 제22권6호
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• pp.1195-1209
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• 1997

This paper presents a new morphological spatio-temporal segmentation algorithm. The algorithm incorporates luminance and motion information simultaneously, and uses morphological tools such as morphological filtersand watershed algorithm. The procedure toward complete segmentation consists of three steps:joint marker extraction, boundary decision, and motion-based region fusion. First, the joint marker extraction identifies the presence of homogeneours regions in both motion and luminance, where a simple joint marker extraction technique is proposed. Second, the spatio-temporal boundaries are decided by the watershed algorithm. For this purposek, a new joint similarity measure is proposed. Finally, an elimination ofredundant regions is done using motion-based region function. By incorporating spatial and temporal information simultaneously, we can obtain visually meaningful segmentation results. Simulation results demonstratesthe efficiency of the proposed method.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2004년도 추계학술발표논문집(상)
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• pp.891-894
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• 2004

This paper proposes an improved image segmentation algorithm by the watershed algorithm based on the local adaptive threshold on local minima search and the fixing threshold on label allocation. The previous watershed algorithm generates the problem of over-segmentation. The over-segmentation makes the boundary in the inaccuracy region by occurring around the object. In order to solve those problems we quantize the input color image by the vector quantization, remove noise and find the gradient image. We sorted local minima applying the local adaptive threshold on local minima search of the input color image. The simulation results show that the proposed algorithm controls over-segmentation and makes the fine boundary around segmented region applying the fixing threshold based on sorted local minima on label allocation.

• 한국멀티미디어학회:학술대회논문집
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• 한국멀티미디어학회 2003년도 춘계학술발표대회논문집
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• pp.431-434
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• 2003

In this paper, we propose an efficient method not only f3r producing accurate region segmentation, solving the over-segmentation problem of watershed algorithm but also f3r reducing post-processing time by reducing computation loads. Through this proposed method, region segmentation of neighboring objects and discrimination of similar intensities were effectively obtained. Input image of watershed algorithm has used the derivative-based detectors such as Sobel and Canny. But proposed method uses the pixels-similarity-based detector, that is, SUSAN. By adopting this proposed method, we can reduce the noise problem and solve the problem of over-segmentation and not lose the edge information of objects. We also propose Zero-Crossing SUSAN. With Zero-Crossing SUSAN, the edge localization, times and computation loads can be improved over those obtained from existing SUSAN

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.862-864
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• 2003

Thresholding is a popular image segmentation method that converts a gray-level image into a binary image. The selection of optimum threshold has remained a challenge over decades. Many image segmentation techniques are developed using information about image in other space rather than the image space itself. Most of the technique based on histogram analysis information-theoretic approaches. In this paper, the criterion function for finding optimal threshold is developed using an intensity-based classuncertainty (a histogram-based property of an image) and region-homogeneity (an image morphology-based property). The theory of the optimum thresholding method is based on postulates that objects manifest themselves with fuzzy boundaries in any digital image acquired by an imaging device. The performance of the proposed method is illustrated on experimental data obtained by W-band millimeter-wave radiometer image under different noise level.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 추계종합학술대회 논문집
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• pp.1093-1096
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• 1999

In this paper, video image segmentation algorithm based on color histogram and change detector is proposed. Color histograms are calculated from both changed region which is detected in the previous and current frame and unchanged region. With each histogram, modes and valleys are detected. Then, color vectors are calculated by averaging pixels in modes. Markers are extracted by labeling color vectors that represent modes, the watershed algorithm is applied to determine uncertain region. In growing region, the root mean square(RMS) of the distance between average pixel in marker region and adjacent pixel is used as a measure. The proposed algorithm based on color histogram and change detector segments video image fastly and effectively. And simulation results show that the proposed method determines the exact boundary between background and foreground.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권7호
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• pp.2345-2358
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• 2022

This paper presents a method to generate stained-glass animation from video inputs. The method initially segments an input video volume into several regions considered as fragments of glass by mean-shift segmentation. However, the segmentation predominantly results in over-segmentation, causing several tiny segments in a highly textured area. In practice, assembling significantly tiny or large glass fragments is avoided to ensure architectural stability in stained glass manufacturing. Therefore, we use low-frequency components in the segmentation to prevent over-segmentation and subdivide segmented regions that are oversized. The subdividing must be coherent between adjacent frames to prevent temporal artefacts, such as flickering and the shower door effect. To temporally subdivide regions coherently, we obtain a panoramic image from the segmented regions in input frames, subdivide it using a weighted Voronoi diagram, and thereafter project the subdivided regions onto the input frames. To render stained glass fragment for each coherent region, we determine the optimal match glass fragment for the region from a dataset consisting of real stained-glass fragment images and transfer its color and texture to the region. Finally, applying lead came at the boundary of the regions in each frame yields temporally coherent stained-glass animation.