• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.7
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• pp.816-822
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• 2016

Content-Based image retrieval is a method to search by image features such as local color, texture, and other image content information, which is different from conventional tag or labeled text-based searching. In real life data, the number of images having tags or labels is relatively small, so it is hard to search the relevant images with text-based approach. Existing image search method only based on image feature similarity has limited performance and does not ensure that the results are what the user expected. In this study, we propose and validate a machine learning based approach to improve the performance of the image search engine. We note that when users search relevant images with a query image, they would expect the retrieved images belong to the same category as that of the query. Image classification method is combined with the traditional image feature similarity method. The proposed method is extensively validated on a public PASCAL VOC dataset consisting of 11,530 images from 20 categories.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10b
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• pp.826-831
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• 1988

This paper presents an approach to segment an image into areas of surfaces, and to compute the surface properties from a gray-scale image in order to describe the surfaces for reconstruction of the 3-D shape of the objects. In general, an rigid body has several surfaces and many edges. But if it is not polyhedoron, it is necessary not only to describe the relation between surfaces, i.e. its line drawings but also to represent the surfaces' equations itself. In order to compute the surfaces' equation we use a probability of edge distribution. At first it is extracted edges from a gray-level image as much as possible. These are not only the points that maximize the change of an image intensuty but candidates which can be seemed to be edges. Next, other character of a surface (color, coordinates and image intensity) are extracted. In our study, we call the all feature of a surface as "texture", for example color, intensity level, orientation of an edge, shape of a surface and so on. These features of a surface on a pixel of an image plane are mapped to a point of the feature space, and segmented to each groups by cluster analysis on this space. These groups are considered to represent object surface in an image plane. Finally, the states of object surface in 3-D space are computed from distributional probability of local and overall statistical features of a surface, and from shape of a surface.a surface.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.39.2-39.2
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• 2017

Over the last decade, deep studies of nearby galaxies have led to the discovery of vast stellar envelopes that are often rich in substructure. These components are naturally predicted in models of hierarchical galaxy assembly, and their observed properties place important constraints on the amount, nature, and history of satellite accretion. One of the most effective ways of mapping the peripheral regions of galaxies is through resolved star studies. Using wide-field cameras equipped to 8 m class telescopes, it has recently become possible to extend these studies to systems beyond the Local Group. Located at a distance of 3.6 Mpc, M81 is a prime target for wide-field mapping of its resolved stellar content. In this talk, we present the detailed results from our deep wide-field imaging survey of the M81 group with the Hyper Suprime-Cam (HSC), on the Subaru Telescope. We report on the analysis of the structures, stellar populations, and metallicities of old dwarf galaxies such as NGC3077, IKN, KDG061, as well as young stellar systems such as Arp's Loop and Holmberg IX. Several candidates for yet-undiscovered faint dwarf galaxies and young stellar clumps in the M81 group will also be introduced. The peculiar galaxy NGC3077 has been classified as the irregular galaxy. Okamoto et al. (2015, ApJ 809, L1) discovered an extended halo structure with S-shape elongated tails, obvious feature of tidal interaction. With a help of numerical simulation by Penarrubia et al. (2009, ApJ 698, 222), we will demonstrate that this tidal feature was formed during the latest close encounters between M81, M82, and NGC 3077, which induced star formation in tidally stripped gas far from the main bodies of galaxies. It is not clear whether the latest tidal interaction was the first close encounters of three galaxies. If NGC3077 is still surrounded by the dark matter halo, it implies that NGC3077 has undergone the first tidal stripping by larger companions. Kinematic studies of inter galactic globular clusters and planetary nebulae would tell us the past history of tidal interaction in this group of galaxies.

• Journal of the Korea Society of Computer and Information
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• v.16 no.9
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• pp.77-85
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• 2011

In this paper, we propose an region segmentation and texture based feature extraction method which split the pavement and roadway from the camera which equipped to the visually impaired person during a walk. We perform the hough transformation method for detect the boundary between pavement and roadway, and devide the segmented region into 3-level according to perspective. Next step, split into pavement and roadway according to the extracted texture feature of segmented regions. Our walking assistance system use rotation-invariant LBP and GLCM texture features for compare the characteristic of pavement block with various pattern and uniformity roadway. Our proposed method show that can segment two regions with illumination invariant in day and night image, and split there regions rotation and occlution invariant in complexed outdoor image.

• Journal of the Korea Society of Computer and Information
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• v.16 no.4
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• pp.53-60
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• 2011

This paper proposes fusion methods of license plate detection and super-resolution for improving license plate recognition in low-resolution images. In the proposed method, we apply the license plate detection based on local structure pattern feature and the sequential super-resolution based on Kalman filter. The proposed fusion methods are divided into two according to whether the license plate is detected or not in the input image : (i) performing license plate detection after restoring whole image through super resolution, and (ii) restoring only the detected region through super-resolution after detecting the license plate. We demonstrated effectiveness of the proposed methods in various environments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.9C
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• pp.546-554
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• 2011

Near targets in the infrared (IR) images have the steady feature for inner region and the transient feature for the boundary region. Based on these features, this paper proposes a new method to extract the fine target shape of near targets in the IR images. First, we detect the boundary region of the candidate targets using the local variance weighted information entropy (WIE) of the original images. And then, a coarse target region can be estimated based on the labeling of the boundary region. For the coarse target region, we use the opening filter with an adaptive structure element to extract the fine target shape. The decision of the adaptive structure element size is optimized for the width information of target boundary by calculating the average WIE in the enlarged windows. The experimental results show that a proposed method has better extraction performance than the previous threshold algorithms.

• The Journal of the Korea Contents Association
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• v.10 no.4
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• pp.19-27
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• 2010

In recent years, high-throughput genome-wide RNA interference screening is emerging as an essential tool to biologists in understanding complex cellular processes. The manual analysis of the large number of images produced in each study spends much time and the labor. Hence, automatic cellular image analysis becomes an urgent need, where segmentation is the first and one of the most important steps. However, those factors such as the region overlapping, a variety of shapes, and non-uniform local characteristics of cellular images become obstacles to efficient cell segmentation. To avoid the problem, a new watershed-based cell segmentation algorithm using a localized segmentation method and a feature vector is proposed in this paper. Localized approach in segmentation resolves the problems caused by a variety of shapes and non-uniform characteristics. In addition, the poor performance of segmentation in overlapped regions can be improved by taking advantage of a feature vector whose component features complement each other. Simulation results show that the proposed method improves the segmentation performance compared to the method in Cellprofiler.

• Journal of Broadcast Engineering
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• v.8 no.1
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• pp.91-100
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• 2003

In this paper, to improve the retrieval Performance, an efficient combination of the MPEG-7 visual descriptors, such as the edge histogram descriptor (EHD), the color layout descriptor (CLD), and the homogeneous texture descriptor (HTD), is proposed in the framework of the relevance feedback approach. The EHD represents spatial distribution of edges in local image regions and it is considered as an important feature to represent the content of the image. The CLD specifies spatial distribution of colors and is widely used in image retrieval due to its simplicity and fast operation speed. The HTD describes precise statistical distribution of the image texture. Both the feature vector for the query image and the weighting factors among the combined descriptors are adaptively determined during the relevance feedback. Experimental results show that the proposed method improves the retrieval performance significantly tot natural images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.5
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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.

• Journal of KIISE:Software and Applications
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• v.31 no.7
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• pp.939-948
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• 2004

In this paper. we Propose a robust and fast image registration technique for motion correction in brain CT-CT angiography obtained from same patient to be taken at different time. First, the feature points of two images are respectively extracted by 3D edge detection technique, and they are converted to locally weighted 3D distance map in reference image. Second, we search the optimal location whore the cross-correlation of two edges is maximized while floating image is transformed rigidly to reference image. This optimal location is determined when the maximum value of cross-correlation does't change any more and iterates over constant number. Finally, two images are registered at optimal location by transforming floating image. In the experiment, we evaluate an accuracy and robustness using artificial image and give a visual inspection using clinical brain CT-CT angiography dataset. Our proposed method shows that two images can be registered at optimal location without converging at local maximum location robustly and rapidly by using locally weighted 3D distance map, even though we use a few number of feature points in those images.