• Journal of Astronomy and Space Sciences
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• v.32 no.3
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• pp.201-207
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• 2015

An algorithm to automatically extract coordinate and time information from optical observation data of geostationary orbit satellites (GEO satellites) or geosynchronous orbit satellites (GOS satellites) is developed. The optical wide-field patrol system is capable of automatic observation using a pre-arranged schedule. Therefore, if this type of automatic analysis algorithm is available, daily unmanned monitoring of GEO satellites can be possible. For data acquisition for development, the COMS1 satellite was observed with 1-s exposure time and 1-m interval. The images were grouped and processed in terms of "action", and each action was composed of six or nine successive images. First, a reference image with the best quality in one action was selected. Next, the rest of the images in the action were geometrically transformed to fit in the horizontal coordinate system (expressed in azimuthal angle and elevation) of the reference image. Then, these images were median-combined to retain only the possible non-moving GEO candidates. By reverting the coordinate transformation of the positions of these GEO satellite candidates, the final coordinates could be calculated.

• Journal of KIISE:Databases
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• v.33 no.4
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• pp.372-383
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• 2006

Among the approaches to image database management, content-based image retrieval (CBIR) is viewed as having the best support for effective searching and browsing of large digital image libraries. Typical CBIR systems allow a user to provide a query image, from which low-level features are extracted and used to find 'similar' images in a database. However, there exists the semantic gap between human visual perception and low-level representations. An effective methodology for overcoming this semantic gap involves relevance feedback to perform feature re-weighting. Current approaches to feature re-weighting require the number of components for a feature representation to be the same for every image in consideration. Following this assumption, they map each component to an axis in the n-dimensional space, which we call the metric space; likewise the feature representation is stored in a fixed-length vector. However, with the emergence of features that do not have a fixed number of components in their representation, existing feature re-weighting approaches are invalidated. In this paper we propose a feature re-weighting technique that supports features regardless of whether or not they can be mapped into a metric space. Our approach analyses the feature distances calculated between the query image and the images in the database. Two-sided confidence intervals are used with the distances to obtain the information for feature re-weighting. There is no restriction on how the distances are calculated for each feature. This provides freedom for how feature representations are structured, i.e. there is no requirement for features to be represented in fixed-length vectors or metric space. Our experimental results show the effectiveness of our approach and in a comparison with other work, we can see how it outperforms previous work.

• Journal of Astronomy and Space Sciences
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• v.22 no.4
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• pp.483-490
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• 2005

For an image sensor CCD, electrons can be trapped at the front-side $Si-SiO_2$ surface interface in a case of exceeding the full well by bright source. Residual images can be made by the electrons remaining in the interface. These residual images are seen in the font-side-illuminated CCDs especially. It is not easy to find a quantitative analysis for this phenomenon in the domestic reports, although it is able to contaminate observation data. In this study, we find residual images iB dark frames which were obtained from the front-side-illuminated CCD at Mt. Lemmon Optical Astronomy Observatory (LOAO), and analyze the effect to contaminated observation data by residual charges.

• Publications of The Korean Astronomical Society
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• v.28 no.1
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• pp.7-13
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• 2013

We present a multi-dimensional reduction method of the surveyed cube database obtained using a single- dish radio telescope in Taeduk Radio Astronomy Observatory (TRAO). The multibeam receiver system installed at the 14 m telescope in TRAO was not optimized at the initial stage, though it became more stabilized in the following season. We conducted a Galactic Plane survey using the multibeam receiver system. We show that the noise level of the first part of the survey was higher than expected, and a special reduction process seemed to be definitely required. Along with a brief review of classical methods, a multi-dimensional method of reduction is introduced; It is found that the 'background' task within IRAF (Image Reduction and Analysis Facility) can be applied to all three directions of the cube database. Various statistics of reduction results is tested using several IRAF tasks. The rms value of raw survey data is 0.241 K, and after primitive baseline subtraction and elimination of bad channel sections, the rms value turned out to be 0.210 K. After the one-dimensional reduction using 'background' task, the rms value is estimated to be 0.176 K. The average rms of the final reduced image is 0.137 K. Thus, the image quality is found to be improved about 43% using the new reduction method.

• Korean Institute of Interior Design Journal
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• v.17 no.6
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• pp.120-127
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• 2008

Korean space design image is a kind of writing as well as one of the sign that dissembles itself as a direct transcript of what it represents. Moreover it is pictorial representation and notions such as mental and perceptual imaginary. Significance of Iconology lies in how we transform image and the imagination that produces it into power of trust and respect. From this point of view, the process of this study is illustrated as follows: At first, this study search out concept of archetype, collective unconsciousness and collective representation that found principles on basic theory for interpretation of korean space icon. Secondly, it mentions theoretical background of iconological contents and structure. And it clarifies Iconology as a method that is applicable logic for Korean space design. Finally, as an analysis of korean space design, this study analyse in three steps that are pre-iconological description, iconological analysis, iconological interpretation each in terms of modernization at regional korean space design. In the step of the pre-iconological description, it describe visual representative style based on era and place. In the step of the iconological analysis, the typical structure is classified in status, vernacular, ethnic, traditional. In the step of the iconological interpretation, connotation is categorized into allegory, multivalence, potential. Through this process, this study suggest that iconology is an appropriate analysis system of Korean space design images that represent symbols combined with our collective emotion.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4317-4335
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• 2018

Semi-supervised multi-view latent space learning is gaining considerable popularity recently in many machine learning applications due to the high cost and difficulty to obtain the large amount of label information of data. Although some semi-supervised multi-view latent space learning methods have been presented, there is still much space for improvement: 1) How to learn latent discriminant intact feature representations by employing data of multiple views; 2) How to exploit the manifold structure of both labeled and unlabeled point in the learned latent intact space effectively. To address the above issues, we propose an approach called semi-supervised multi-view manifold discriminant intact space learning ($SM^2DIS$) for image classification in this paper. $SM^2DIS$ aims to seek a manifold discriminant intact space for data of different views by making use of both the discriminant information of labeled data and the manifold structure of both labeled and unlabeled data. Experimental results on MNIST, COIL-20, Multi-PIE, and Caltech-101 databases demonstrate the effectiveness and robustness of our proposed approach.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.51.2-51.2
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• 2019

Multi-wavelength observations become very popular in astronomy. Even though there are some correlations among different sensor images, it is not easy to translate from one to the other one. In this study, we apply a deep learning method for image-to-image translation, based on conditional generative adversarial networks (cGANs), to solar images. To examine the validity of the method for scientific data, we consider several different types of pairs: (1) Generation of SDO/EUV images from SDO/HMI magnetograms, (2) Generation of backside magnetograms from STEREO/EUVI images, (3) Generation of EUV & X-ray images from Carrington sunspot drawing, and (4) Generation of solar magnetograms from Ca II images. It is very impressive that AI-generated ones are quite consistent with actual ones. In addition, we apply the convolution neural network to the forecast of solar flares and find that our method is better than the conventional method. Our study also shows that the forecast of solar proton flux profiles using Long and Short Term Memory method is better than the autoregressive method. We will discuss several applications of these methodologies for scientific research.

• Journal of the Korea Computer Graphics Society
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• v.18 no.2
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• pp.9-15
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• 2012

In this paper, we propose a method to segment a color image into several meaningful regions. We suppose that the meaningful region has a set of colors with high frequency in the color image. To find these colors, the color image is represented as several sets of color points in RGB space. And when we use the density of points defined in this method, color belonging to a dense region of color points in RGB space refers to the color that appeared frequently in the image. Eventually, we can find meaningful regions by looking for regions with high density of color points using our level set function in RGB space. However, if a meaningful region does not have a contiguous region of the sufficient size in the image, this is not a meaningful region but meaningless region. Thus, the pixels in the meaningless region are assigned to the biggest meaningful region belonging to its neighboring pixels in the color image. Our method divides the color image into meaningful regions by applying the density of color points to level set function in RGB space. This is different from the existing level set method that is defined only in 2D image.

• Journal of Korea Multimedia Society
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• v.9 no.8
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• pp.971-981
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• 2006

As shear-warp volume rendering is the fastest rendering method among the software based approaches, image quality is not good as that of other high-quality rendering methods. In this paper, we propose two methods to improve the image quality of shear-warp volume rendering without sacrificing computational efficiency. First, supersampling is performed in intermediate image space. We propose an efficient method to transform between volume and image coordinates at the arbitrary ratio. Second, we utilize pre-integrated rendering technique for shear-warp rendering. We propose new data structure called overlapped min-max map. Using this structure, empty space leaping can be performed so that we can maintain the rendering speed even though pre-integrated rendering is applied. Consequently, shear-warp rendering can generate high-qualify images comparable to those generated by the ray-casting without degrading speed.

• The Journal of the Korea Contents Association
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• v.6 no.9
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• pp.19-27
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• 2006

Mean shift procedure is applied for the data points in the joint spatial-range domain and achieves a high quality. However, a color image is segmented differently according to the inputted spatial parameter or range parameter and the demerit is that the image is broken into many small regions in case of the small parameter. In this paper, to improve this demerit, we propose the method that groups similar regions using region merging method for over-segmented images. The proposed method converts a over-segmented image in RGB color space into in HSI color space and merges similar regions by hue information. Here, to preserve edge information, the region merge constraints are used to decide whether regions are merged or not. After then, we merge the regions in RGB color space for non-processed regions in HSI color space. Experimental results show the superiority in region's segmentation results.