• Korean Journal of Remote Sensing
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• v.36 no.2_2
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• pp.337-349
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• 2020

In this study, Helikite, Low Altitude Remote Sensing (LARS) platform, was used to acquire coastal images. In the obtained image, the land and water masses were divided using four types of region clustering algorithms, and then waterline was extracted using edge detection. Quantitative comparisons were not possible due to the lack of in-situ waterline data. But, based on the image of the infrared band where water masses and land are relatively clear, the waterlines extracted by each algorithm were compared. As a result, it was found that each algorithm differed significantly in the part where the distinction between water masses and land was ambiguous. This is considered to be a difference in the process of selecting the threshold value of the digital number that each algorithm uses to distinguish the regions. The extraction of waterlines through various algorithms is expected to be used in conjunction with a Low Altitude Remote Sensing system that can be continuously monitored in the future to explain the rapid changes in coastal shape through several years of long-term data from fixed areas.

• Tunnel and Underground Space
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• v.13 no.1
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• pp.33-43
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• 2003

Field investigations of the orientations of discontinuity planes inside the borehole for designing the underground rock structures have been depend solely on the borehole image-taking techniques. But, borehole image-taking has to be processed after the completion of drilling operation and also requires the handling of highly expensive apparatus so that practical application is very restricted. In this study Discontinuity Orientation Measurement (DOM) drilling system and discontinuity analysis model RoSA-DOM are developed to acquire the reliable information of rock structure by analyzing the characteristics of joint distribution. DOM drilling system retrieves the rock core on which the reference line of pre-fixed drilling orientation is engraved. Coordinates of three arbitrary points on the joint surface relative to the position of reference line are assessed to determine the orientation of joint plane. The position of joint plane is also allocated by calculating the location of core axis at which joint plane is intersected. Then, the formation of joint set is analyzed by utilizing the clustering algorithm. Total and set spacings are calculated by considering the borehole axis as the scanline. Engineering applicability of in-situ rock mass around the borehole is also estimated by calculating the total and regional RQDs along the borehole axis.

• Journal of Industrial Convergence
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• v.20 no.12
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• pp.195-202
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• 2022

Underground Geospatial Information Map Management System(UGIMMS) integrates various underground facilities in the underground space into 3D mesh data, and supports to check the 3D image and location of the underground facilities in the mobile app. However, there is a problem that it takes a long time to run in the app because various underground facilities can exist in some areas executed by the app and can be seen layer by layer. In this paper, we propose a deep learning-based K-means vertex clustering algorithm as a method to reduce the execution time in the app by reducing the size of the data by reducing the number of vertices in the 3D mesh data within the range that does not cause a problem in visibility. First, our proposed method obtains refined vertex feature information through a deep learning encoder-decoder based model. And second, the method was simplified by grouping similar vertices through K-means vertex clustering using feature information. As a result of the experiment, when the vertices of various underground facilities were reduced by 30% with the proposed method, the 3D image model was slightly deformed, but there was no missing part, so there was no problem in checking it in the app.

• Science of Emotion and Sensibility
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• v.7 no.4
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• pp.57-65
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• 2004

In this paper, we present a hybrid approach which incorporates color, texture and shape in content-based image retrieval. Colors in each image are clustered into a small number of representative colors. The feature descriptor consists of the representative colors and their percentages in the image. A similarity measure similar to the cumulative color histogram distance measure is defined for this descriptor. The co-occurrence matrix as a statistical method is used for texture analysis. An optimal set of five statistical functions are extracted from the co-occurrence matrix of each image, in order to render the feature vector for eachimage maximally informative. The edge information captured within edge histograms is extracted after a pre-processing phase that performs color transformation, quantization, and filtering. The features where thus extracted and stored within feature vectors and were later compared with an intersection-based method. The content-based retrieval system is tested to be effective in terms of retrieval and scalability through experimental results and precision-recall analysis.

• Journal of Digital Contents Society
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• v.19 no.8
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• pp.1463-1469
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• 2018

The development of multimedia devices with built-in cameras such as smart devices and various studies using video-related multimedia data such as images and video obtained from the devices have been actively conducted. These studies deal with image data. An image can be defined as a set of color information obtained from a digital sensor called a pixel. Images contain various cognitive information such as color, lighting, objects and so on. In order to extract or process such information, it is necessary to clearly understand the composition of colors. In this paper, we introduce 3-dimensional information visualization method which can effectively express the results of image processing together with color distribution. This study visualizes the characteristics of image related multimedia data as well as the characteristics of various analytical data derived from it, so that researchers can transmit the image information more clearly and effectively.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.3
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• pp.310-315
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• 2004

Image taken in the night can be low-contrast images because of poor environment and image transmission. We propose an algorithm that improves the acquired low-contrast image. MPEG-2 separates chrominance and illuminance, and compresses respectively because human vision is more sensitive to luminance. We extracted illumination and used K-means algorithm to find a proper crossover point automatically. We used K-means algorithm in the viewpoint that the problem of crossover point selection can be considered as the two-category classification problem. We divided an image into two subimages using the crossover point, and applied the histogram equalization method respectively. We used the index of fuzziness to evaluate the degree of improvement. We compare the results of the proposed method with those of other methods.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.223-230
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• 2003

Feature-based similarity retrieval has become an important research issue in multimedia database systems. The features of multimedia data are useful for discriminating between multimedia objects (e 'g', documents, images, video, music score, etc.). For example, images are represented by their color histograms, texture vectors, and shape descriptors, and are usually high-dimensional data. The performance of conventional multidimensional data structures(e'g', R- Tree family, K-D-B tree, grid file, TV-tree) tends to deteriorate as the number of dimensions of feature vectors increases. The R＊-tree is the most successful variant of the R-tree. In this paper, we propose a SOM-based R＊-tree as a new indexing method for high-dimensional feature vectors.The SOM-based R＊-tree combines SOM and R＊-tree to achieve search performance more scalable to high dimensionalities. Self-Organizing Maps (SOMs) provide mapping from high-dimensional feature vectors onto a two dimensional space. The mapping preserves the topology of the feature vectors. The map is called a topological of the feature map, and preserves the mutual relationship (similarity) in the feature spaces of input data, clustering mutually similar feature vectors in neighboring nodes. Each node of the topological feature map holds a codebook vector. A best-matching-image-list. (BMIL) holds similar images that are closest to each codebook vector. In a topological feature map, there are empty nodes in which no image is classified. When we build an R＊-tree, we use codebook vectors of topological feature map which eliminates the empty nodes that cause unnecessary disk access and degrade retrieval performance. We experimentally compare the retrieval time cost of a SOM-based R＊-tree with that of an SOM and an R＊-tree using color feature vectors extracted from 40, 000 images. The result show that the SOM-based R＊-tree outperforms both the SOM and R＊-tree due to the reduction of the number of nodes required to build R＊-tree and retrieval time cost.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.4
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• pp.373-379
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• 2019

In this study, image-based flock monitoring and density evaluation were conducted for broiler chicks welfare. Image data were captured by using a mono camera and region of broiler chicks in the image was detected using converting to HSV color model, thresholding, and clustering with filtering. The results show that region detection was performed with 5% relative error and 0.81 IoU on average. The detected region was corrected to the actual region by projection into ground using coordinate transformation between camera and real-world. The flock density of broiler chicks was estimated using the corrected actual region, and it was observed with an average of 80%. The developed algorithm can be applied to the broiler chicks house through enhancing accuracy of region detection and low-cost system configuration.

• Journal of the Korean Society of Radiology
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• v.85 no.2
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• pp.381-393
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• 2024

Purpose Metabolic abnormalities in hepatic encephalopathy (HE) cause brain edema or demyelinating disease, resulting in symmetric regional cerebral edema (SRCE) on MRI. This study aimed to investigate the usefulness of the clustering analysis of SRCE in predicting the development of brain failure. Materials and Methods MR findings and clinical data of 98 consecutive patients with HE were retrospectively analyzed. The correlation between the 12 regions of SRCE was calculated using the phi (φ) coefficient, and the pattern was classified using hierarchical clustering using the φ² distance measure and Ward's method. The classified patterns of SRCE were correlated with clinical parameters such as the model for end-stage liver disease (MELD) score and HE grade. Results Significant associations were found between 22 pairs of regions of interest, including the red nucleus and corpus callosum (φ = 0.81, p < 0.001), crus cerebri and red nucleus (φ = 0.72, p < 0.001), and red nucleus and dentate nucleus (φ = 0.66, p < 0.001). After hierarchical clustering, 24 cases were classified into Group I, 35 into Group II, and 39 into Group III. Group III had a higher MELD score (p = 0.04) and HE grade (p = 0.002) than Group I. Conclusion Our study demonstrates that the SRCE patterns can be useful in predicting hepatic preservation and the occurrence of cerebral failure in HE.

• Korean Journal of Remote Sensing
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• v.31 no.2
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• pp.111-125
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• 2015

In this study, the target detection using both high-resolution satellite SAR and Elecro-Optical (EO) images such as TerraSAR-X and WorldView-2 is performed, considering the characteristics of targets. The targets of our interest are featured by being stationary and appearing as cluster targets. After the target detection of SAR image by using Constant False Alarm Rate (CFAR) algorithm, a series of processes is performed in order to reduce false alarms, including pixel clustering, network clustering and coherence analysis. We extend further our algorithm by adopting the fast and effective ellipse detection in EO image using randomized hough transform, which is significantly reducing the number of false alarms. The performance of proposed algorithm has been tested and analyzed on TerraSAR-X SAR and WordView-2 EO images. As a result, the average false alarm for group of targets is 1.8 groups/$64km^2$ and the false alarms of single target range from 0.03 to 0.3 targets/$km^2$. The results show that groups of targets are successfully identified with very low false alarms.