• Proceedings of the KAIS Fall Conference
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• 2012.05b
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• pp.716-718
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• 2012

본 논문에서는 모바일 폰으로 획득한 영상의 텍스트영역 검출을 제안한다. 최근 모바일 폰을 이용한 영상기반 응용 분야의 연구가 활발히 진행되고 있으며, 특히 영상에서 텍스트를 인식하기 위한 전단계로 텍스트 영역 검출은 중요하다. 본 논문은 텍스트 영역 검출을 위해 먼저, 컬러 영상을 입력 받아 그레이 이미지로 변환하여 영상내에 내포된 잡음을 제거하고 열림/닫힘 연산의 특징을 이용해 각 연결요소를 검출하고 검출된 요소들을 레이블링 한다. 레이블링 된 영상은 텍스트가 갖는 특정 조건에 의해 텍스트 영역인지 텍스트 영역이 아닌지를 검출하고 검출된 텍스트 영역은 검증을 통해 최종 텍스트 영역을 검출한다. 제안한 방법은 기존의 택스트 영역 겁출보다 정확도가 향상할 수 있다.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.11
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• pp.188-194
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• 2013

In this paper, we proposed a new method for pulmonary vessels image segmentation and refinement from pulmonary image. Proposed method consist of following five steps. First, threshold estimation is performed by polynomial regression analysis of histogram variation rate of the pulmonary image. Second, segmentation of pulmonary vessels object is performed by density-based segmentation method based on estimated threshold in first step. Third, 2D connected component labeling method is applied to segmented pulmonary vessels. The seed point of both side diaphragms is determined by eccentricity and size of component. Fourth step is diaphragm extraction by 3D region growing method at the determined seed point. Finally, noise cancelation of pulmonary vessels image is performed by 3D connected component labeling method. The experimental result is showed accurately pulmonary vessels image segmentation, the diaphragm extraction and the noise cancelation of the pulmonary vessels image.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.1
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• pp.9-16
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• 2010

Moving object detection is to detect foreground object different from background scene in a new incoming image frame and is an essential ingredient process in some image processing applications such as intelligent visual surveillance, HCI, object-based video compression and etc. Most of previous object detection algorithms are still computationally heavy so that it is difficult to develop real-time multi-channel moving object detection in a workstation or even one-channel real-time moving object detection in an embedded system using them. Foreground mask correction necessary for a more precise object detection is usually accomplished using morphological operations like opening and closing. Morphological operations are not computationally cheap and moreover, they are difficult to be rendered to run simultaneously with the subsequent connected component labeling routine since they need quite different type of processing from what the connected component labeling does. In this paper, we first devise a fast and precise foreground mask correction algorithm, "Neighbor Foreground Pixel Propagation (NFPP)" which utilizes neighbor pixel checking employed in the connected component labeling. Next, we propose a novel moving object detection method based on the devised foreground mask correction algorithm, NFPP where the connected component labeling routine can be executed simultaneously with the foreground mask correction. Through experiments, it is verified that the proposed moving object detection method shows more precise object detection and more than 4 times faster processing speed for a image frame and videos in the given the experiments than the previous moving object detection method using morphological operations.

• Journal of Korea Entertainment Industry Association
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• v.5 no.1
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• pp.127-133
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• 2011

Characters in natural image are an important information with various context. Previous work of character region detection algorithms is not detect of character region in case of image complexity and the surrounding lighting, similar background to character, so this paper propose an method of character region detection in natural image using edge and connected component by morphological reconstructions. Firstly, we detect edge using Canny-edge detector and connected component with local min/max value by morphological reconstructed-operation in gray-scale image, and labeling each of detected connected component elements. lastly, detected candidate of text regions was merged for generation for one candidate text region, Final text region detected by checking the similarity and adjacency of neighbor of text candidate individual character. As the results of experiments, proposed algorithm improved the correctness of character regions detection using edge and connected components.

• Journal of KIISE:Software and Applications
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• v.32 no.7
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• pp.625-635
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• 2005

In this paper, we propose a hybrid approach for segmenting the lungs efficiently and automatically in chest CT images. The proposed method consists of the following three steps. first, lungs and airways are extracted by two- and three-dimensional automatic seeded region growing and connected component labeling in low-resolution. Second, trachea and large airways are delineated from the lungs by two-dimensional morphological operations, and the left and right lungs are identified by connected component labeling in low-resolution. Third, smooth and accurate lung region borders are obtained by refinement based on image subtraction. In experiments, we evaluate our method in aspects of accuracy and efficiency using 10 chest CT images obtained from 5 patients. To evaluate the accuracy, we Present results comparing our automatic method to manually traced borders from radiologists. Experimental results show that proposed method which use connected component labeling in low-resolution reduce processing time by 31.4 seconds and maximum memory usage by 196.75 MB on average. Our method extracts lung surfaces efficiently and automatically without additional processing like hole-filling.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.705-711
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• 2011

This paper proposes an effective algorithm of license plate recognition for industrial applications. By applying Canny edge detection on a vehicle image, it is possible to find a connected rectangular, which is a strong candidate for license plate. The color information of license plate separates plates into white and green. Then, OTSU binary image processing and foreground neighbor pixel propagation algorithm CLNF will be applied to each license plates to reduce noise except numbers and letters. Finally, through labeling, numbers and letters will be extracted from the license plate. Letter and number regions, separated from the plate, pass through mesh method and thinning process for extracting feature vectors by X-Y projection method. The extracted feature vectors are compared with the pre-learned weighting values by backpropagation neural network to execute final recognition process. The experiment results show that the proposed license plate recognition algorithm works effectively.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.831-832
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• 2017

링크드오픈데이터를 통해 다양한 분야의 RDF 데이터가 공개되고 있으며 그 양이 지속적으로 증가하고 있다. RDF 데이터는 그래프 형태이기 때문에 대용량 RDF 데이터를 효율적으로 관리하기 위한 그래프 데이터베이스에 대한 연구가 중요하다. 2개의 RDF 리소스가 그래프 상에서 연결됐는지 여부를 알아내는 기능은 RDF 요소간 연관관계를 식별하는 데에 관련이 있기 때문에 그래프 데이터베이스의 중요한 기능 중 하나이다. 대용량 그래프 데이터에 대한 그래프 도달가능성을 빠르게 처리하기 위해서 2-Hop 레이블링 변형들이 제안됐다. 최근에 2-Hop 레이블 크기를 줄이기 위해 2-Hop 레이블링이 진행되기 전에 노드 아이디를 부여하는 방법이 제안됐다. 하지만 그래프의 지역 정보만을 활용하기 때문에 복잡한 형태의 그래프에 대해서는 비효율적이라는 문제점이 있다. 본 논문에서는 그래프의 전역 정보를 반영할 수 있는 Topological Sort를 활용한 노드 아이디 부여 기법에 대한 설계를 제안한다.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.904-906
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• 2005

본 논문에서는 뇌 CT 혈관조영영상에서 슬라이스 정보를 이용한 뇌 분할 방법을 제안한다. 뇌 분할 과정은 현재 슬라이스와 이전 슬라이스 간 분할 영역의 크기 정보를 가지고 영역 성장 단계와 전파 단계로 구분하여 수행된다. 영역 성장 단계에서는 이차원 영역성장법을 통해 뇌 분할을 수행하고 누출이 발생하는 슬라이스에 대하여 방사선 투과 기법을 통해 영역보정을 수행한다. 전파 단계에서는 이전 슬라이스에서 분할된 뇌 영역을 현재 슬라이스로 전파함으로써 장벽을 생성하고 장벽 내에서 이차원 영역성장법을 수행함으로써 누출을 최소화한다. 또한 뇌 영역과 유사한 밝기값을 형성하고 있는 미세 요소들을 제거하기 위해 이차원 연결화소군 레이블링 기법을 통해서 최종적으로 뇌 분할을 수행한다. 본 논문의 실험을 위하여 뇌 CT 혈관조영영상을 사용하여 정확한 뇌분할 결과를 얻었다.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.17-22
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• 2009

DaVinci processors are popular media processors for implementing embedded multimedia applications. They support dual core architecture: ARM9 core for video I/O handling as well as system management and peripheral handling, and DSP C64+ core for effective digital signal processing. In this paper, we propose our efforts for optimal implementation of object tracking algorithm in DaVinci-based smart camera which is being designed and implemented by our laboratory. The smart camera in this paper is supposed to support object detection, object tracking, object classification and detection of intrusion into surveillance regions and sending the detection event to remote clients using IP protocol. Object tracking algorithm is computationally expensive since it needs to process several procedures such as foreground mask extraction, foreground mask correction, connected component labeling, blob region calculation, object prediction, and etc. which require large amount of computation times. Thus, if it is not implemented optimally in Davinci-based processors, one cannot expect real-time performance of the smart camera.