• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 ITC-CSCC -1
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• pp.482-485
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• 2000

A modified watershed transform is proposed which is context-free marker-controlled and minima imposition-free to reduce the over-segmentation and to speedup the transform. In contrast to the conventional methods in which a priori knowledge, such as flat zones, zones of homogeneous texture, and morphological distance, is required for marker extraction, context-free marker extraction is proposed by using the attention operator based on the GST (generalized symmetry transform). By using the context-free marker, the proposed watershed transform exploit marker-constrained labeling to speedup the computation and to reduce the over-segmentation by eliminating the unnecessary geodesic reconstruction such as the minima imposition and thereby eliminating the necessity of the post-processing of region merging. The simulation results show that the proposed method can extract context-free markers inside the objects from the complex background that includes multiple objects and efficiently reduces over-segmentation and computation time.

• 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.

• 융합신호처리학회 학술대회논문집
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• 한국신호처리시스템학회 2003년도 하계학술대회 논문집
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• pp.90-93
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• 2003

본 논문에서는 watershed segmentation을 이용해서 특정한 영역의 윤곽선정보를 바탕으로 영상을 추출하는 방법을 제안한다. 일반적인 영역추출방법에는 폐곡선을 이용한 영역추출방법, snake라 불리는 에너지 최소화를 이용한 active contour 방법 등 여러 가지가 있다. 특히 이러한 방법에는 연산시간이 많이 걸린다는 단점이 있는데, 이러한 문제점을 해결하기 위해서 canny edge detector를 사용하여 윤곽선 정보를 추출하고, watershed segmentation으로 영상을 분할한 후 윤곽선 정보를 이용해서 특정 영상의 영역에 대한 부분을 병합하여 추출하는 방법을 사용하였다. 제안하는 방법으로 기존의 추출 방법중의 하나인 levelset과 비교 실험한 결과 거의 동일한 영상의 결과를 얻을 수 있었으며 연산시간을 줄일 수 있었다.

• Journal of Information Processing Systems
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• 제18권5호
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• pp.701-710
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• 2022

With the development of artificial intelligence technology, various methods have been developed for recognizing objects in images using machine learning. Image segmentation is the most effective among these methods for recognizing objects within an image. Conventionally, image datasets of various classes are trained simultaneously. In situations where several classes require segmentation, all datasets have to be trained thoroughly. Such repeated training results in low training efficiency because most of the classes have already been trained. In addition, the number of classes that appear in the datasets affects training. Some classes appear in datasets in remarkably smaller numbers than others, and hence, the training errors will not be properly reflected when all the classes are trained simultaneously. Therefore, a new method that separates some classes from the dataset is proposed to improve efficiency during training. In addition, the accuracies of the conventional and proposed methods are compared.

• 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.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2011년도 추계학술발표대회
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• pp.429-430
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• 2011

Roughness set theory is a popular topic to use in color-image segmentation. A new popular color image segmentation algorithm is proposed by scientists with the point using traditional histogram and Histon construct roughness set histogram. But, there is still a problem about that is the correlativity of color vector in roughness set histogram, which take an inactive effect in the process of color-image segmentation. Therefore, this paper represents further research based on this and proposed an improved method proved through lot of experiments. The experimental result reduces the correlativity of color vector in roughness set histogram and calculation time remarkably.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2022년도 하계학술대회
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• pp.265-268
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• 2022

Video-based Point Cloud Compression(V-PCC) 부호화기의 세그먼트 정제(Refining segmentation) 과정은 3D 세그먼트를 2D 패치 데이터로 효율적으로 변환하기 위한 V-PCC 부호화기의 핵심 파트이지만, 많은 연산량을 필요로 하는 모듈이다. 때문에 이미 TMC2 에 Fast Grid-based refine segmentation 과정이 구현되어 있으나, 아직도 세그먼트 정제 기술의 연산량은 매우 높은 편이다. 본 논문에서는 현재 TMC2 에 구현되어 있는 Fast Gridbased Refine Segmentation 을 살펴보고, 복셀(Voxel) 타입에 따른 특성에 맞춰 두 가지 조건을 추가하는 고속화 알고리즘을 제안한다. 실험 결과 압축성능(BD-BR)은 TMC2 와 거의 차이를 보이지 않았지만, 모듈 단위 평균 10% 연산량이 절감되는 것을 확인하였다.

• 전자공학회논문지
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• 제50권8호
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• pp.225-231
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• 2013

의료영상분할은 다양한 의료영상처리를 수행하기에 앞서 먼저 수행되어야 하는 영상처리 기술이다. 그래서 빠르고 정확한 의료영상분할이 요구되고 있으며 다양한 의료영상분할 방법이 연구되고 있다. 의료영상에는 특성이 유사한 다양한 장기가 존재하기 때문에 분할영역의 정확한 판단이 필요하다. 그러나 의료영상은 장기의 일부가 작게 촬영되는 경우가 발생된다. 이 경우에는 분할영역을 판단하기 위한 정보가 부족하게 되며 그 결과 분할과정에서 분할영역이 제거된다. 본 논문에서는 볼륨 데이터와 선형 방정식을 이용하여 작은 영역에서의 분할결과를 개선하였다. 제안한 방법의 성능을 확인하기 위하여 흉부 CT 영상의 폐 분할을 수행하였다. 실험 결과, 의료영상의 분할 정확도는 0.978에서 0.981로 표준편차는 0.281에서 0.187로 개선되는 것을 확인하였다.

• International journal of advanced smart convergence
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• 제7권2호
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• pp.1-6
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• 2018

In this paper, we propose an automatic left ventricle segmentation method in computed tomography angiography (CTA) using separating energy function. First, we smooth the images by applying anisotropic diffusion filter to remove noise. Secondly, the volume of interest (VOI) is detected by using k-means clustering. Thirdly, we divide the left and right heart with split energy function. Finally, we extract only left ventricle from left and right heart with optimizing cost function including orientation term.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.1972-1975
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• 2003

Video segmentation is an essential part in region-based video coding and any other fields of the video processing. Among lots of methods proposed so far, the watershed method in which the region growing is performed for the gradient image can produce well-partitioned regions globally without any influence on local noise and extracts accurate boundaries. But, it generates a great number of small regions, which we call over segmentation problem. Therefore we proposes image segmentation improvement by selective application structuring element of mathematical morphology.