• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 하계종합학술대회 논문집(3)
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• pp.207-210
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• 2002

In this paper, content-based retrieval for region of interest(ROI) has been described, using maximum bin color. From a given query image, the object of interest is selected by a user. Using maximum bin color of the selected object, candidate regions are extracted from database images. The final regions of interest are determined by comparing the normalized histograms of the selected object and each candidate region.

• Journal of Information Processing Systems
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• 제11권4호
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• pp.528-537
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• 2015

Text in images is one of the most important cues for understanding a scene. In this paper, we propose a novel approach based on interest points to localize text in natural scene images. The main ideas of this approach are as follows: first we used interest point detection techniques, which extract the corner points of characters and center points of edge connected components, to select candidate regions. Second, these candidate regions were verified by using tensor voting, which is capable of extracting perceptual structures from noisy data. Finally, area, orientation, and aspect ratio were used to filter out non-text regions. The proposed method was tested on the ICDAR 2003 dataset and images of wine labels. The experiment results show the validity of this approach.

• International Journal of Contents
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• 제8권4호
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• pp.56-63
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• 2012

In recent decades, analyzing the activities of human brain achieved some accomplishments by using the functional Magnetic Resonance Imaging (fMRI) technique. fMRI data provide a sequence of three-dimensional images related to human brain's activity which can be used to detect instantaneous cognitive states by applying machine learning methods. In this paper, we propose a new approach for distinguishing human's cognitive states such as "observing a picture" versus "reading a sentence" and "reading an affirmative sentence" versus "reading a negative sentence". Since fMRI data are high dimensional (about 100,000 features in each sample), extremely sparse and noisy, feature selection is a very important step for increasing classification accuracy and reducing processing time. We used the Fisher Discriminant Ratio to select the most powerful discriminative features from some Regions of Interest (ROIs). The experimental results showed that our approach achieved the best performance compared to other feature extraction methods with the average accuracy approximately 95.83% for the first study and 99.5% for the second study.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.810-813
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• 2004

Indexing and Retrieving images from large and varied collections using image content as a key is a challenging and important problem in computer vision application. In this paper, a color Content-based Image Retrieval (CBIR) system using hierarchical Region of Interest (ROI) query and indexing is presented. During indexing process, First, The ROIs on every image in the image database are extracted using a region-based image segmentation technique, The JSEG approach is selected to handle this problem in order to create color-texture regions. Then, Color features in form of histogram and correlogram are then extracted from each segmented regions. Finally, The features are stored in the database as the key to retrieve the relevant images. As in the retrieval system, users are allowed to select ROI directly over the sample or user's submission image and the query process then focuses on the content of the selected ROI in order to find those images containing similar regions from the database. The hierarchical region-of-interest query is performed to retrieve the similar images. Two-level search is exploited in this paper. In the first level, the most important regions, usually the large regions at the center of user's query, are used to retrieve images having similar regions using static search. This ensures that we can retrieve all the images having the most important regions. In the second level, all the remaining regions in user's query are used to search from all the retrieved images obtained from the first level. The experimental results using the indexing technique show good retrieval performance over a variety of image collections, also great reduction in the amount of searching time.

• 한국멀티미디어학회논문지
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• 제7권5호
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• pp.648-657
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• 2004

본 논문에서는 특이 칼라 분포에 대한 정보를 활용함으로써 어떠한 사전 지식없이 칼라 영상으로부터 중심 객체를 추출하는 방법에 대해 제안한다. 중심 객체는 영상 중심 부근에 위치하면서 특이 칼라 분포를 갖는 영역들의 집합으로 정의한다. 특이 칼라는 영상 경계 주변에 비해 영상의 중심 위치에서 보다 높은 밀도로 존재하는 칼라로 정의한다. 중심 객체 추출을 위해 우선 특이 칼라 정보를 사용하여 영상 분할된 영역 중에서 객체의 특징을 대표하는 영역들의 집합을 핵심객체영역을 선택한다. 핵심객체영역에 인접하며 이와 높은 칼라 유사도를 갖고 또한 배경이 아닌 영역들을 반복적으로 핵심객체영역에 병합하여 핵심객체영역을 확장함으로써 생성된 최종 병합 결과를 중심 객체로 추출한다. 따라서 중심 객체는 상이한 칼라 특징을 갖는 영역으로 구성될 수 있으며 상호 연결되어 있을 경우에는 두개 이상의 객체가 중심 객체에 포함될 수 있다. 제안된 방법의 타당성 및 중요 칼라의 유용성은 다양한 실험 영상을 통해 확인하였다. 본 논문에서 제안된 방법으로 추출된 중심 객체는 영상 검색 응용 분야에 유용하게 사용될 수 있을 것으로 기대한다.

• 대한전자공학회논문지TE
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• 제42권1호
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• pp.41-46
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• 2005

채널의 잡음 영향을 극복하기 위하여 새로운 멀티플 디스크립션 기법을 제안한다. 본 기법은 임베디드 부호화기법을 채택하고 있으며, 부대역 계수가 한번에 비트 플레인 단위로 부호화 되는데 이때 비적응적 산술부호화 방식을 사용한다. 각 영역의 중요도에 따라 관심영역을 부호화할 때, 다른 부분에 비해서 패스 횟수를 늘려서 부호화를 행함으로써 관심영역이 다른 부분에 비해서 고화질로 부호화되도록 한다. 또 채널 에러를 극복하기 위해서 부호화 과정에서 소스 데이터에 제어된 중복성을 허용하는 멀티플 디스크립션 기법을 채택한다. 본 방식은 고압축률을 지향하고, 특정한 영역이 다른 부분에 비해서 중요한 시스템의 경우에 보다 중요한 특성을 나타낸다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2015년도 추계학술대회
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• pp.687-690
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• 2015

전방 추돌 경보 시스템(FCWS) 및 차선 이탈 경보 시스템(LDWS)에서 차선 및 객체 검출을 위한 관심영역은 차도영역으로 설정되어야 한다. 분수계 분할(watershed segmentation)방법은 차도영역을 분리하기에 효과적인 알고리즘이다. 이 알고리즘은 초기 seed에 속해있는 watershed line과 국부 최소값에 따라서 분할 결과가 다르게 나타나는데 차도 seed에 그 이외의 영역이나 차량이 포함될 경우에 차도 이외의 부분이 차도영역으로 포함되어 분할된다. 이런 문제점을 보완하기 위해 도로 환경에 따라 차도 seed를 적응적으로 변경해야 한다. 그 방법으로 영상을 여러 개의 관심영역으로 분할하여 차선을 검출하고 자기차선을 잇는 직선을 초기 seed로 설정한다. 설정된 seed에 차량이 검출되면 seed 위치를 조정하고 조정된 위치에서 차선을 지나지 않는다면 차선을 지나도록 seed의 크기를 조정하여 최종적인 seed를 결정한다. 최종적으로 결정된 seed를 통해서 도로환경에 따라 적응적으로 차도영역을 검출을 가능하게 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권5호
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• pp.1676-1689
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• 2014

At present, the Content-Based Image Retrieval (CBIR) system has become a hot research topic in the computer vision field. In the CBIR system, the accurate extractions of low-level features can reduce the gaps between high-level semantics and improve retrieval precision. This paper puts forward a new retrieval method aiming at the problems of high computational complexities and low precision of global feature extraction algorithms. The establishment of the new retrieval method is on the basis of the SIFT and Harris (APISH) algorithm, and the salient region of interest points (SRIP) algorithm to satisfy users' interests in the specific targets of images. In the first place, by using the IPDSH and SRIP algorithms, we tested stable interest points and found salient regions. The interest points in the salient region were named as salient interest points. Secondary, we extracted the pseudo-Zernike moments of the salient interest points' neighborhood as the feature vectors. Finally, we calculated the similarities between query and database images. Finally, We conducted this experiment based on the Caltech-101 database. By studying the experiment, the results have shown that this new retrieval method can decrease the interference of unstable interest points in the regions of non-interests and improve the ratios of accuracy and recall.

• 제어로봇시스템학회논문지
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• 제8권4호
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• pp.327-332
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• 2002

The main problems of computational complexity in object tracking are definition of objects, segmentations and identifications in non-structured environments with erratic movements and collisions of objects. The object's information as a region that corresponds to objects without discriminating among objects are considered. This paper describes the algorithm that, automatically and efficiently, recognizes and keeps tracks of interest-regions selected by users in video or camera image sequences. The block-based feature matching method is used for the region tracking. This matching process considers only dominant feature points such as corners and curved-edges without requiring a pre-defined model of objects. Experimental results show that the proposed method provides above 96% precision for correct region matching and real-time process even when the objects undergo scaling and 3-dimen-sional movements In successive image sequences.

• Journal of Electrical Engineering and Technology
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• 제13권3호
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• pp.1371-1376
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• 2018

Photoplethysmography (PPG) is a convenient method for monitoring a heart rhythm. In addition to specialized devices, smartphones can be used to obtain PPG signals. However, as smartphones are not intended for this purpose, optimization is required to efficiently obtain PPG signals. Determining the optimal region of interest (ROI) is one such optimization method. There are two significant advantages in employing an optimized ROI. One is that the computing load is decreased by reducing the image size used to extract the PPG signal. The other is that stronger and more reliable PPG signals are obtained by removing noisy regions. In this paper, we propose an optimal ROI determination method by recursively splitting regions to locate the region that produces the strongest PPG signal.