• Journal of the Institute of Convergence Signal Processing
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• v.4 no.1
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• pp.1-8
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• 2003

Various methods have been studying to maintain and apply the multimedia inform abruptly increasing over all social fields, in recent years. For retrieval of still images, we is implemented content-based image retrieval system in this paper that make possible to retrieve similar objects from image database after segmenting query object from background if user request query. Query image is processed median filtering to remove noise first and then object edge is detected it by canny edge detection. And query object is segmented from background by using convex hull. Similarity value can be obtained by means of histogram intersection with database image after securing color histogram from segmented image. Also segmented image is processed gray convert and wavelet transform to extract spacial gray distribution and texture feature. After that, Similarity value can be obtained by means of banded autocorrelogram and energy. Final similar image can be retrieved by adding upper similarity values that it make possible to not only robust in background but also better correct object retrieval by using object segmentation method.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.3
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• pp.42-52
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• 2010

The purpose of this study is to produce digital elevation model (DEM) in the Jebu tidal flat, one of the west coast of the Korean Peninsula, by means of photogrammetric techniques from aerial digital stereo-images. Produced DEM would be become the fundamental data for change detection of the sediment and erosion. To do so, epipolar line is established by relative orientation. Area-based matching is then carried out based on this line and matching size according to surface property of tidal flat after a classified image is produced from reflectance and texture of the surface. As the results, DEM generated by the proposed method showed subtle changes in height more precisely than DEM using the fixed matching size and DEM by the commercial S/W in the region, such as tidal flats having few level differences.

• Journal of KIISE:Software and Applications
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• v.34 no.1
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• pp.18-29
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• 2007

In this paper we present a system that classifies brain MR images by using 2 level decision tree learning. There are two kinds of information that can be obtained from images. One is the low-level features such as size, color, texture, and contour that can be acquired directly from the raw images, and the other is the high-level features such as existence of certain object, spatial relations between different parts that must be obtained through the interpretation of segmented images. Learning and classification should be performed based on the high-level features to classify images according to their semantic meaning. The proposed system applies decision tree learning to each level separately, and the high-level features are synthesized from the results of low-level classification. The experimental results with a set of brain MR images with tumor are discussed. Several experimental results that show the effectiveness of the proposed system are also presented.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.7C
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• pp.719-725
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• 2003

The content-based image retrieval system extracts features of color, shape and texture from raw images, and builds the database with those features in the indexing process. The search in the whole retrieval system is defined as a process which finds images that have large similarity to query image using the feature database. This paper proposes a new two-stage search method in the content-based image retrieval system. The method is that the features are condensed and stored by the property of Cauchy-Schwartz inequality in order to reduce the similarity computation time which takes a mostly response time from entering a query to getting retrieval results. By the extensive computer simulations, we have observed that the proposed two-stage search method successfully reduces the similarity computation time while maintaining the same retrieval relevance as the conventional exhaustive search method. We also have observed that the method is more effective as the number of images and dimensions of the feature space increase.

• Journal of Korean Society for Geospatial Information Science
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• v.8 no.1 s.15
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• pp.121-130
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• 2000

For the land-cover classification we have usually used imagery of the optical sensors only. But currently a number of the satellite with various sensors are operating and the availability of using the data acquired from them are increasing. SAR sensors, in particular, can produce additional informations on the land-cover which has not been available from optical sensors. On this study, I have applied the SAR Image to the SPOT XS image in the classification procedures, and analysed the classified results. In this procedure I have extracted texture informations from SAR intensity images, then applied both intensity and texture informations. From the accuracy analysis, overall accuracy are increased slightly when the SAR texture was applied. In case of the Built-up class the results showed higher accuracy than those of when only the SPOT XS image was used. From this result I can show that overall accuracy was increased slightly but the spatial distribution of classes was visibly improved.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.10
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• pp.113-122
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• 1999

When we want to retrieve the most similar image from the image database, the color histogram intersection, shape feature and texture feature comparing method are used as a metric to measure the similarity. In order to increase the accuracy of retrievals, we need to integrate two different features. In this paper, the histogram intersection and shape based block histogram intersection method are used. This method results in a high efficient algorithm that meets a similar accuracy and a relatively fast retrieval speed compared to the method of integration of two different features. The Proposed algorithm is tested on retrievals of image database consisting of various 600 images and we implemented that the proposed algorithm gives fast, high efficiency and reliability compared to others.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.12
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• pp.2836-2842
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• 2010

An NPC grouping method is proposed for various 3D games depending on their characteristics. Immovable objects tend to have particular orientation features in their Gabor filtering results whereas the movable objects controlled by AI appearing as a human or an animal do not. First of all, We analyzed directional and frequency domain features in the NPC object and configured them as 24 Gabor filter banks. Then, 24-dimensional feature vectors according to the scale and direction of the filter are calculated. Each extracted vector represents the energy of a certain direction. This energy indicates the particular direction strength of the object texture. Thus, using this property, NPCs could be grouped as artificial objects and natural objects effectively and it draws the game more speed and strategic actions as a result.

• Journal of Service Research and Studies
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• v.5 no.2
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• pp.103-110
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• 2015

Recently, Classification of characters in a 3D FPS game has emerged as a very significant issue. In this study, We propose the game character Classification method using Image Region Segmentation of the extracting meaningful object in a simple operation. In this method, first used a non-linear RGB color model and octree color quantization scheme. The input image represented a less than 20 quantized color and uses a small number of meaningful color histogram. And then, the image divided into small blocks, calculate the degree of similarity between the color histogram intersection and adjacent block in block units. Because, except for the block boundary according to the texture and to extract only the boundaries of the object block. Set a region by these boundary blocks as a game object and can be used for FPS game play. Through experiment, we obtain accuracy of more than 80% for Classification method using each feature. Thus, using this property, characters could be classified effectively and it draws the game more speed and strategic actions as a result.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.1
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• pp.111-116
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• 2019

The fire detectors used in the engine rooms of ships offer only a slow response to emergencies because smoke or heat must reach detectors installed on ceilings, but the air flow in engine rooms can be very fluid depending on the use of equipment. In order to overcome these disadvantages, much research on video-based fire detection has been conducted in recent years. Video-based fire detection is effective for initial detection of fire because it is not affected by air flow and transmission speed is fast. In this paper, experiments were performed using images of smoke from a smoke generator in an engine room. Data generated using LBP and GLCM operators that extract the textural features of smoke was classified using SVM, which is a machine learning classifier. Even if smoke did not rise to the ceiling, where detectors were installed, smoke detection was confirmed using the image-based technique.

• Journal of the Korea Convergence Society
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• v.11 no.12
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• pp.15-22
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• 2020

In this paper, we proposed a deep learning based super-resolution method that combines Channel Attention and Spatial Attention feature enhancement methods. It is important to restore high-frequency components, such as texture and features, that have large changes in surrounding pixels during super-resolution processing. We proposed a super-resolution method using feature enhancement that combines Channel Attention and Spatial Attention. The existing CNN (Convolutional Neural Network) based super-resolution method has difficulty in deep network learning and lacks emphasis on high frequency components, resulting in blurry contours and distortion. In order to solve the problem, we used an emphasis block that combines Channel Attention and Spatial Attention to which Skip Connection was applied, and a Residual Block. The emphasized feature map extracted by the method was extended through Sub-pixel Convolution to obtain the super resolution. As a result, about PSNR improved by 5%, SSIM improved by 3% compared with the conventional SRCNN, and by comparison with VDSR, about PSNR improved by 2% and SSIM improved by 1%.