• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.148-153
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• 2009

Few methods have dealt with segmenting multiple images with analogous content. Concurrent images of a scene and gathered images of a similar foreground are examples of these images, which we term consistent scene images. In this paper, we present a method to segment these images based on manual segmentation of one image, by iteratively propagating information via multi-level cues with adaptive confidence. The cues are classified as low-, mid-, and high- levels based on whether they pertain to pixels, patches, and shapes. Propagated cues are used to compute potentials in an MRF framework, and segmentation is done by energy minimization. Through this process, the proposed method attempts to maximize the amount of extracted information and maximize the consistency of segmentation. We demonstrate the effectiveness of the proposed method on several sets of consistent scene images and provide a comparison with results based only on mid-level cues [1].

• 대한의용생체공학회:의공학회지
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• 제28권3호
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• pp.332-337
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• 2007

In the early twenty-first century, minimally invasive surgery is the mainstay of various kinds of surgical fields. Surgeons gave percutaneously surgical treatment of the screw directly using a fluoroscopic view in the past. The latest date, they began to operate the fractured carpal bone surgery using Computerized Tomography (CT). Carpal bones composed of wrist joint consist of eight small bones which have hexahedron and sponge shape. Because of these shape, it is difficult to grasp the shape of carpal bones using only CT image data. Although several image segmentation studies have been conducted with carpal bone CT image data, more studies about carpal bone using CT data are still required. Especially, to apply the software implemented from the studies to clinical fIeld, the outcomes should be user friendly and very accurate. To satisfy those conditions, we propose modified multi-seed region growing segmentation method which uses simple threshold and the canny edge detector for finding edge information more accurately. This method is able to use very easily and gives us high accuracy and high speed for extracting the edge information of carpal bones. Especially, using multi-seed points, multi-bone objects of the carpal bone are extracted simultaneously.

• 방송공학회논문지
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• 제13권5호
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• pp.685-695
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• 2008

본 논문은 다시점에서 물체를 촬영한 영상들의 집합, 즉, 다시점 영상 집합(multi-view image set)이 주어진 경우, 적은 사용자 입력을 통해 효율적으로 영상 집합 내 관심 물체의 영역을 추출하는 기법을 제안한다. 제안하는 기법은 사용자가 직접 입력을 통해 영역화한 하나의 영상을 바탕으로, 그 영상의 배경 및 전경과 인접 영상 간의 변형을 각각 근사하여 전경 및 배경에 대응되는 인접 영상의 영역을 파악하고, 이 영역들을 통해 인접 영상을 영역화한 후, 영역화된 영상을 바탕으로 다음 인접 영상을 영역화하는 과정을 순차적으로 반복하여 영상 집합 전체를 영역화한다. 이때 전경 및 배경의 변형은 각각 특징점 기반 레지스트레이션(registration) 기법과 선형성 거리비율 보존(affine) 변형을 가정한 대응점 기반 변형행렬(homography)을 통해 근사되며, 각 대응 영역을 기반으로 하는 화소 색 분포 및 형상 정보(shape prior)를 마르코프 랜덤 장(Markov random field)에서의 에너지 최소화에 기반을 둔 영역화 기법에 적용하여 영역화를 수행한다. 제시하는 실험 결과는 제안하는 기법이 적은 사용자 입력으로 다시점 영상 집합 전체를 효과적으로 영역화한다는 것을 뒷받침한다.

• Journal of Information Processing Systems
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• 제13권5호
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• pp.1126-1134
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• 2017

A variety of medical service applications in the field of the Internet of Things (IoT) are being studied. Segmentation is important to identify meaningful regions in images and is also required in 3D images. Previous methods have been based on gray value and shape. The Visible Korean dataset consists of serially sectioned high-resolution color images. Unlike computed tomography or magnetic resonance images, automatic segmentation of color images is difficult because detecting an object's boundaries in colored images is very difficult compared to grayscale images. Therefore, skilled anatomists usually segment color images manually or semi-automatically. We present an out-of-core 3D segmentation method for large-scale image datasets. Our method can segment significant regions in the coronal and sagittal planes, as well as the axial plane, to produce a 3D image. Our system verifies the result interactively with a multi-planar reconstruction view and a 3D view. Our system can be used to train unskilled anatomists and medical students. It is also possible for a skilled anatomist to segment an image remotely since it is difficult to transfer such large amounts of data.

• 한국정보통신학회논문지
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• 제17권4호
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• pp.935-944
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• 2013

본 논문에서는 영상 분할을 이용한 다시점 영상의 조명보상 기법을 제안한다. 제안하는 기법에서는 깊이 정보를 이용하여 일정 거리에 따라 참조 영상의 깊이 영상을 레이어로 분리한다. 분리된 레이어에서 서로 다른 객체를 분리하기 위하여 각 레이어에 레이블링 과정을 수행한다. 레이블링 된 참조 영상의 깊이 영상은 3D 워핑 기법을 통하여 왜곡 영상의 시점으로 변환되고 레이블링 된 영역을 찾아 히스토그램을 이용한 조명 보상을 각 영역에서 독립적으로 수행한다. 3D 워핑으로 발생하는 가려짐 영역은 전역적인 방법을 이용하여 보상하게 된다. 다양한 실험을 통해 제안하는 기법으로 조명보상 전처리를 수행한 다시점 영상의 부호화 효율이 향상되는 것을 확인할 수 있었다.

• 대한임베디드공학회논문지
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• 제16권5호
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• pp.209-214
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• 2021

Panoptic segmentation, which is now widely used in computer vision such as medical image analysis, and autonomous driving, helps understanding an image with holistic view. It identifies each pixel by assigning a unique class ID, and an instance ID. Specifically, it can classify 'thing' from 'stuff', and provide pixel-wise results of semantic prediction and object detection. As a result, it can solve both semantic segmentation and instance segmentation tasks through a unified single model, producing two different contexts for two segmentation tasks. Semantic segmentation task focuses on how to obtain multi-scale features from large receptive field, without losing low-level features. On the other hand, instance segmentation task focuses on how to separate 'thing' from 'stuff' and how to produce the representation of detected objects. With the advances of both segmentation techniques, several panoptic segmentation models have been proposed. Many researchers try to solve discrepancy problems between results of two segmentation branches that can be caused on the boundary of the object. In this survey paper, we will introduce the concept of panoptic segmentation, categorize the existing method into two representative methods and explain how it is operated on two methods: top-down method and bottom-up method. Then, we will analyze the performance of various methods with experimental results.

• Journal of International Society for Simulation Surgery
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• 제3권2호
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• pp.39-45
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• 2016

The augmented reality is the environment which consists of real-world view and information drawn by computer. Since the image which user can see through augmented reality device is a synthetic image composed by real-view and virtual image, it is important to make the virtual image generated by computer well harmonized with real-view image. In this paper, we present reviews of several works about computer vision and graphics methods which give user realistic augmented reality experience. To generate visually harmonized synthetic image which consists of a real and a virtual image, 3D geometry and environmental information such as lighting or material surface reflectivity should be known by the computer. There are lots of computer vision methods which aim to estimate those. We introduce some of the approaches related to acquiring geometric information, lighting environment and material surface properties using monocular or multi-view images. We expect that this paper gives reader's intuition of the computer vision methods for providing a realistic augmented reality experience.

• 대한전자공학회논문지
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• 제27권4호
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• pp.580-589
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• 1990

This paper describes an efficient method of texture image segmentation based on conditional 1-dimensional histograms. We consider the multi-dimensional histogram, and it is projected into each axis in order to obtain conditional 1-dimensional histograms. And we extract uniform regions by iteratively applying the peak-valley detection method to conditional 1-dimensional histograms. In view of the amount of memory and computation time, the proposed method is superior to the conventional method which uses the multi-dimensional histogram. By applying the proposed method to the artificial and natural texture images some desirable results are obtained.

• 한국정보과학회논문지:정보통신
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• 제36권3호
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• pp.198-203
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• 2009

최근 멀티미디어 기술의 발달과 더불어 3차원 영상에 대한 연구가 활발하게 이루어지고 있다. 이 중 다시점 영상은 사실감 넘치는 화면을 사용자에게 제공하지만, 대역폭의 급격한 증가는 풀어야 할 주요 문제이다. 본 논문은 부호화 과정의 복잡도와 시간 소요를 줄일 수 있는 빠른 예측모드 결정 알고리즘을 제안한다. 이것은 빠르게 움직이는 전경 객체를 효과적으로 구분할 수 있는 객체 분할을 기반으로 한다. 빠른 움직임을 가진 전경 객체가 시점 방향 예측 모드로 부호화 될 가능성이 더 높기 때문에 움직임 보상 과정을 사전에 제한할 수 있다. 제안한 기법을 적용한 결과, 기존의 부호화 과정과 비교하여 화질의 큰 저하 없이 평균 45% 연산량이 감소하였다.

• 한국BIM학회 논문집
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• 제11권3호
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• pp.22-33
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• 2021

In order to maximize the use of BIM, all data related to individual elements in the model must be correctly assigned, and it is essential to check whether it corresponds to the IFC entity classification. However, as the BIM modeling process is performed by a large number of participants, it is difficult to achieve complete integrity. To solve this problem, studies on semantic integrity verification are being conducted to examine whether elements are correctly classified or IFC mapped in the BIM model by applying an artificial intelligence algorithm to the 2D image of each element. Existing studies had a limitation in that they could not correctly classify some elements even though the geometrical differences in the images were clear. This was found to be due to the fact that the geometrical characteristics were not properly reflected in the learning process because the range of the region to be learned in the image was not clearly defined. In this study, the CRF-RNN-based semantic segmentation was applied to increase the clarity of element region within each image, and then applied to the MVCNN algorithm to improve the classification performance. As a result of applying semantic segmentation in the MVCNN learning process to 889 data composed of a total of 8 BIM element types, the classification accuracy was found to be 0.92, which is improved by 0.06 compared to the conventional MVCNN.