• 대한의용생체공학회:의공학회지
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• 제28권6호
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• pp.713-720
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• 2007

In image-guided surgery, automatic bone segmentation of Computed Tomography (CT) images is an important but challenging step. Previous attempts include intensity-, edge-, region-, and deformable curve-based approaches [1], but none claims fully satisfactory performance. Although active contour (AC) techniques possess many excellent characteristics, their applications in CT image segmentation have not worthily exploited yet. In this study, we have evaluated the automaticity and performance of the model of Chan-Vese Multiphase AC Without Edges towards knee bone segmentation from CT images. This model is suitable because it is initialization-insensitive and topology-adaptive. Its segmentation results have been qualitatively compared with those from four other widely used AC models: namely Gradient Vector Flow (GVF) AC, Geometric AC, Geodesic AC, and GVF Fast Geometric AC. To quantitatively evaluate its performance, the results from a commercial software and a medical expert have been used. The evaluation results show that the Chan-Vese model provides superior performance with least user interaction, proving its suitability for automatic bone segmentation from CT images.

• 한국의류학회지
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• 제26권11호
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• pp.1605-1614
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• 2002

The purpose of this study was to identify the consumers who use the level of price as the indicator of the product quality. In order to implement the purpose of this study, Jeans market had been segmented by the mixture regression model, and price response function was calibrated for each segment. Based on the types of price response function, segments were allocated into one of two groups; the group using the level of price as the quality indicator or the group not using the level of price as that. Then, characteristics of both groups were compared in terms of product attributes and demographic variables. Data were co]looted from the sample of the 23o undergraduate and graduate students in Seoul. For the data analysis, mixture regression model, conjoint analysis, and t-test were used. As a result, jeans market was divided into 5 segments. Segment 1,2,3 were allocated into the group not using the level of price as the quality indicator while segment 4,5 were done into the other group. Significant differences existed between two groups in product attributes, not in demographic variables. Mixture model and conjoint analysis were proved to be an effective set of tools in market segmentation.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제11권2호
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• pp.41-47
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• 2007

A fine segmentation algorithm is proposed for extracting objects in an image, which have both weak boundaries and highly non-convex shapes. The image has simple background colors or simple object colors. Two concepts, geometric attraction-driven flow (GADF) and edge-regions are combined to detect boundaries of objects in a sub-pixel resolution. The main strategy to segment the boundaries is to construct initial curves close to objects by using edge-regions and then to make a curve evolution in GADF. Since the initial curves are close to objects regardless of shapes, highly non-convex shapes are easily detected and dependence on initial curves in boundary-based segmentation algorithms is naturally removed. Weak boundaries are also detected because the orientation of GADF is obtained regardless of the strength of boundaries. For a fine segmentation, we additionally propose a local region competition algorithm to detect perceptible boundaries which are used for the extraction of objects without visual loss of detailed shapes. We have successfully accomplished the fine segmentation of objects from images taken in the studio and aphids from images of soybean leaves.

• 전기전자학회논문지
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• 제15권3호
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• pp.191-197
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• 2011

직관적 퍼지 c-평균 군집화 모델을 이용하는 자기공명 영상의 분할 방법이 본 논문에서 제안되었다. 본 논문에서 채택하는 fuzzy c-means with intuition (FCM-I)은 잡음의 영향을 줄이기 위하여 직관이라는 척도를 사용한다. 실제적 자기 공명 영상에 대해 영상 분할의 실험을 수행하고 기존의 몇몇 군집화 알고리즘과 성능을 비교하였다. 기존의 모델들과 성능을 비교한 결과, FCM-I 기반의 분할 방법은 잡음과 필요한 계수의 선택에 대해 상대적으로 강인하여, 영상 분할에 유용한 모델이 될 수 있음을 확인할 수 있었다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.610-614
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• 2006

In this paper the advanced method of geodesic active contours was developed for the task of building detection from aerial and satellite images. Automatic extraction of man-made structures including buildings, building blocks or roads from remote sensing data is useful for land use mapping, scene understanding, robotic navigation, image retrieval, surveillance, emergency management procedures, cadastral etc. A level set method based on a region-driven segmentation model was implemented with which building boundaries were detected, through this curve propagation technique. The essence of this approach is to optimize the position and the geometric form of the curve by measuring information along that curve, and within the regions that compose the image partition. To this end, one can consider uniform intensities inside objects and the background. Thus, given an initial position of the curve, one can determine global, region-driven functions and provide a statistical description of the inside and outside object area. The calculus of variations and a gradient descent method was used to optimize the variational functional by an iterative steady state process. Experimental results demonstrate the potential of the proposed processing scheme.

• International journal of advanced smart convergence
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• 제7권4호
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• pp.108-113
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• 2018

In this study, we introduce a new image gradient computation based on understanding of image generation. Most images consist of groups of pixels with similar color information because the images are generally obtained by taking a picture of the real world. The general gradient operator for an image compares only the neighboring pixels and cannot obtain information about a wide area, and there is a risk of falling into a local minimum problem. Therefore, it is necessary to attempt to introduce the gradient operator of the interval concept. We present a bow-tie gradient by color values of pixels on bow-tie region of a given pixel. To confirm the superiority of our study, we applied our bow-tie gradient to image segmentation algorithms for various images.

• 한국정보기술학회 영문논문지
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• 제10권1호
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• pp.25-36
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• 2020

Motivation: Fibers as the extracellular filamentous structures determine the shape of the cytoskeletal structures. Their characterization and reconstruction from a 3D cellular image represent very useful quantitative information at the cellular level. In this paper, we presented a novel automatic method to extract fiber diameter distribution through a pipeline to reconstruct fibers from 3D fluorescence confocal images. The pipeline is composed of four steps: segmentation, skeletonization, template fitting and fiber tracking. Segmentation of fiber is achieved by defining an energy based on tensor voting framework. After skeletonizing segmented fibers, we fit a template for each seed point. Then, the fiber tracking step reconstructs fibers by finding the best match of the next fiber segment from the previous template. Thus, we define a fiber as a set of templates, based on which we calculate a diameter distribution of fibers.

• 한국멀티미디어학회논문지
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• 제20권9호
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• pp.1491-1501
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• 2017

In this paper, we propose a method to measure respiration rate by dividing the respiration related region in depth image using level set method. In the conventional method, the respiration related region was separated using the pre-defined region designated by the user. We separate the respiration related region using level set method combining shape prior knowledge. Median filter and clipping are performed as a preprocessing method for noise reduction in the depth image. As a feasibility test, respiration activity was recorded using depth camera in various environments with arm movements or body movements during breathing. Respiration activity was also measured simultaneously using a chest belt to verify the accuracy of calculated respiration rate. Experimental results show that our proposed method shows good performance for respiration rate estimation in various situation compared with the conventional method.

• 대한전자공학회논문지SP
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• 제41권2호
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• pp.41-50
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• 2004

MPEG-4 표준에서는 객체 단위의 부호화를 수행하기 위해 자연영상으로부터 비디오 객체를 분리하는 영상분할(segmentation) 기술이 필요하다. 영상분할 방법은 크게 자동 영상분할(automatic segmentation)과 반자동 영상분할(semi-automatic segmentation)의 두 부류로 나눌 수 있다. 지금까지 개발된 대부분의 자동 영상분할 방법은 비디오 객체의 명확한 수학적인 모델을 제시하기 곤란하며 한 화면에서 개별 객체를 추출하기 어렵기 때문에 그 성능에 한계가 있다. 본 논문에서는 이러한 문제점을 극복하기 위해 active contour 알고리즘을 이용한 반자동 영상분할 알고리즘을 제안한다. 초기 곡선으로부터 변화 가능한 모든 곡선의 집합을 모양공간으로 정의하고 그 공간을 선형공간이라고 가정하면, 모양공간(shape space)은 모양 행렬에 의해 행(column) 공간과 남은 빈(left null) 공간으로 나뉘어진다. 본 논문에서 제안하는 알고리즘은 행공간의 모양공간 벡터를 이용하여 초기 곡선으로부터 영상의 특징점까지의 변화를 기술하고 동적 그래프 검색 알고리즘을 이용하여 객체의 세밀한 부분을 묘사한다. 모양 행렬과 객체의 윤곽을 추정하기 위한 SUSAN 연산자의 사용으로 제안한 알고리즘은 저수준 영상처리로부터 생성되는 불필요한 특징점을 무시할 수 있다. 또한, 모양 행렬의 사용으로 생긴 제약은 동적 그래프 검색 알고리즘으로 보상한다.

• 방송공학회논문지
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• 제14권4호
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• pp.450-462
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• 2009

최근까지 단일 영상이나 동영상을 영역화하는 기법들은 다양하게 제시되어 왔으나, 유사한 장면에 대한 여러 장의 영상을 동시에 영역화하는 기법은 많지 않았다. 본 논문에서는 한 장소에서 연속적으로 촬영하였거나 전경 물체가 유사한 여러 영상들을 동일 장면 영상으로 정의하고, 이런 동일 장면 영상들을 적은 양의 사용자 입력을 통해 효과적으로 영역화하는 기법을 제안한다. 구체적으로, 사용자가 최초의 영상 한 장을 직접 영역화한 후, 그 영상의 영역화 결과와 영상의 특성을 토대로 다중 단계 신호를 적응적 가중치를 주어서 인접 영상으로 전파하고, 이를 통해 제안하는 기법은 인접 영상을 반복적으로 영역화한다. 영역화는 마르코프 랜덤 장에서의 에너지 최소화를 통해 이루어지는데, 전파되는 신호는 각 픽셀에 대한 에너지를 정의하는 바탕이 되며, 픽셀, 픽셀 패치, 그리고 영상 전체로부터 비롯되었는가에 따라 낮은 단계, 중간 단계, 그리고 높은 단계의 신호로 지칭된다. 또한 에너지 최소화 틀 안에서 전파된 신호를 통해 정의되는 에너지 역시 낮은 단계, 중간 단계, 그리고 높은 단계의 세 단계로 정의한다. 이런 과정을 통해 전파된 신호를 최대한 다양하게 활용하고, 이를 통해 다양한 영상에 영역화 결과가 일관되게 유지된다. 다양한 동일 장면 영상들에 제안하는 기법을 적용하여 성능을 평가하고, 픽셀 패치를 바탕으로 하는 중간 단계 신호만을 이용한 결과와 제안하는 다중 신호를 적용하는 기법의 결과를 비교한다.