• Journal of Korea Multimedia Society
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• v.5 no.4
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• pp.415-425
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• 2002

In this paper, we present a content-based image retrieval method which is based on the feature extraction in the wavelet transform domain. In order to overcome the drawbacks of the feature vector making up methods which use the global wavelet coefficients in subbands, we utilize the energy value of wavelet coefficients, and the shape-based retrieval of objects is processed by moment which is invariant in translation, scaling, rotation of the objects The proposed methods reduce feature vector size, and make progress performance of classification retrieval which provides fast retrievals times. To offer the abilities of region-based image retrieval, we discussed the image segmentation method which can reduce the effect of an irregular light sources. The image segmentation method uses a region-merging, and candidate regions which are merged were selected by the energy values of high frequency bands in discrete wavelet transform. The region-based image retrieval is executed by using the segmented region information, and the images are retrieved by a color, texture, shape feature vector.

• Korean Journal of Remote Sensing
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• v.15 no.4
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• pp.357-365
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• 1999

The optical sensors of Electro-Optical Camera (EOC) and Ocean Scanning Multi-spectral Imager (OSMI) aboard the KOrea Multi-Purpose SATellite (KOMPSAT) will be placed in a sun synchronous orbit in late 1999. The EOC and OSMI sensors are expected to produce the land mapping imagery of Korean territory and the ocean color imagery of world oceans, respectively. Utilization of the EOC and OSMI data would encompass the various fields of science and technology such as land mapping, land use and development, flood monitoring, biological oceanography, fishery, and environmental monitoring. Readiness of data support for user community is thus essential to the success of the KOMPSAT program. As a part of testing such readiness prior to the KOMPSAT launch, we have performed the preliminary acceptance test for the KOMPSAT data processing system using the simulated EOC and OSMI data sets. The purpose of this paper is to demonstrate the readiness of the KOMPSAT data processing system, and to help data users understand how the KOMPSAT EOC and OSMI data are processed, archived, and provided. Test results demonstrate that all requirements described in the data processing specification have been met, and that the image integrity is maintained for all products. It is however noted that since the product accuracy is limited by the simulated sensor data, any quantitative assessment of image products can not be made until actual KOMPSAT images will be acquired.

• Journal of Digital Convergence
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• v.17 no.11
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• pp.475-482
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• 2019

Analyzing the inner meaning and expression of philosophy by analyzing the composition, symbolic expression, and style of the film with high artistic perfection, which contains the spirit of the times, considers human beings through society and history, and raises awareness of life and the present generation. It will be a very meaningful and valuable study in film as art. The movie 'Rome' was cut into the rest of the public's mind by being tempered, hidden and omitted, and the color was black and white. Many aesthetic attempts can be found through symbolic images expressing the ironic message of maid's daily life as a race, capital, socially oppressed history. It can be seen that he expresses his own authorism visual language by drawing symbolic expressions through many contrasts and symbolic expressions through objects. The analysis of commercial films containing these artistic values is expected to help in the future production as a measure of the progress of art films and precedents of authorism expression techniques.

• The Journal of Advanced Prosthodontics
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• v.13 no.5
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• pp.333-342
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• 2021

PURPOSE. To evaluate the impact of five different tooth preparation designs on the marginal and internal fit discrepancies of cobalt-chromium (CoCr) crowns produced by computer-aided designing (CAD) and selective laser melting (SLM) processes. MATERIALS AND METHODS. Five preparation data were constructed, after which design crowns were obtained. Actual crowns were fabricated using an SLM process. After the data of actual crowns were obtained with structural light scanning, intaglio surfaces of the design crown and actual crown were virtually superimposed on the preparation. The fit-discrepancies were displayed with colors, while the root means square was calculated and analyzed with one-way analysis of variance (ANOVA), Tukey's test or Kruskal-Wallis test (α = .05). RESULTS. The marginal or internal color-coded images in the five design groups were not identical. The shoulder-lip and sharp line angle groups in the CAD or SLM process had larger marginal or internal fit discrepancies compared to other groups (P < .05). In the CAD process, the mean marginal and internal fit discrepancies were 10.0 to 24.2 ㎛ and 29.6 to 31.4 ㎛, respectively. After the CAD and SLM processes, the mean marginal and internal fit discrepancies were 18.4 to 40.9 ㎛ and 39.1 to 47.1 ㎛, respectively. The SLM process itself resulted in a positive increase of the marginal (6.0 - 16.7 ㎛) and internal (9.0 - 15.7 ㎛) fit discrepancies. CONCLUSION. The CAD and SLM processes affected the fit of CoCr crowns and varied based on the preparation designs. Typically, the shoulder-lip and sharp line angle designs had a more significant effect on crown fit. However, the differences between the design groups were relatively small, especially when compared to fit discrepancies observed clinically.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1353-1364
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• 2018

This study proposed a method to detect Cochlodinium polykrikoides red tide pixels in satellite images using a logistic regression model of machine learning technique under Imbalanced data. The spectral profiles extracted from red tide, clear water, and turbid water were used as training dataset. 70% of the entire data set was extracted and used for as model training, and the classification accuracy of the model was evaluated using the remaining 30%. At this time, the white noise was added to the spectral profile of the red tide, which has a relatively small number of data compared to the clear water and the turbid water, and over-sampling was performed to solve the unbalanced data problem. As a result of the accuracy evaluation, the proposed algorithm showed about 94% classification accuracy.

• Journal of Digital Convergence
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• v.16 no.12
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• pp.555-561
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• 2018

It is the Korean language textbook of elementary school 1~2 grade that forms the basis of the textbook to nurture the creative convergence talent with the core competence required by the future society. In order to increase the learning effect based on the textbooks, the interest inducing factors were derived on the basis of the learning interest, and the textbook design was analyzed by the in - depth interviews and discussions of the expert group. As a result, Graphic elements using bright and soft colors, illustrations of peer groups related to learning contents, and resilient use of sans serifs. However, issues such as lack of proper mixing of photos and illustrations, further development of learning helper characters, configuration of spare margins, graphic image design, and lack of a structured layout that utilizes color and visual images were cited as problems.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1445-1456
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• 2018

Environment perception and three-dimensional (3D) reconstruction tasks are used to provide unmanned ground vehicle (UGV) with driving awareness interfaces. The speed of obstacle segmentation and surrounding terrain reconstruction crucially influences decision making in UGVs. To increase the processing speed of environment information analysis, we develop a CPU-GPU hybrid system of automatic environment perception and 3D terrain reconstruction based on the integration of multiple sensors. The system consists of three functional modules, namely, multi-sensor data collection and pre-processing, environment perception, and 3D reconstruction. To integrate individual datasets collected from different sensors, the pre-processing function registers the sensed LiDAR (light detection and ranging) point clouds, video sequences, and motion information into a global terrain model after filtering redundant and noise data according to the redundancy removal principle. In the environment perception module, the registered discrete points are clustered into ground surface and individual objects by using a ground segmentation method and a connected component labeling algorithm. The estimated ground surface and non-ground objects indicate the terrain to be traversed and obstacles in the environment, thus creating driving awareness. The 3D reconstruction module calibrates the projection matrix between the mounted LiDAR and cameras to map the local point clouds onto the captured video images. Texture meshes and color particle models are used to reconstruct the ground surface and objects of the 3D terrain model, respectively. To accelerate the proposed system, we apply the GPU parallel computation method to implement the applied computer graphics and image processing algorithms in parallel.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.4
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• pp.1-8
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• 2019

Plant phenomics is a technique for observing and analyzing morphological features in order to select plant varieties of excellent traits. The conventional methods is difficult to apply to the phenomics system. because the color threshold value must be manually changed according to the detection target. In this paper, we propose the convolution neural network (CNN) structure that can automatically segment plants from the background for the phenomics system. The LeafNet consists of nine convolution layers and a sigmoid activation function for determining the presence of plants. As a result of the learning using the LeafNet, we obtained a precision of 98.0% and a recall rate of 90.3% for the plant seedlings images. This confirms the applicability of the phenomics system.

• Journal of the Korean Society of Safety
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• v.34 no.1
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• pp.82-89
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• 2019

The importance of aviation safety has been emphasized recently due to the development of aviation industry. Despite the efforts of each country and the improvement of screening equipment, screening tasks are still difficult and detection failures are frequent. The purpose of this study was to examine the effect of feedback on improving signal detection performance applying a Simulated Baggage Screening Program(SBSP) for improving aviation safety. SBSP consists of three parts: image combination, option setting and experiment. The experimental images were color-coded to reflect the items' transmittance of the x-rays and could be combined as researchers' need. In the option, the researcher could set up the information, incentive, and comments needed for training to be delivered on a number of tasks and times. Experiment was conducted using SBSP and participant's performance information (hit, missed, false alarms, correct rejection, reaction time, etc.) was automatically calculated and stored. A total of 50 participants participated and each participant was randomly assigned to feedback and non-feedback group. Participants performed a total of 200 tasks and 20(10%) contained target object(gun and knife). The results showed that when the feedback was provided, the hit, correct rejection ratio and d′ were increased, however, the false alarms and miss decreased. However, there was no significant difference in response criteria(${\beta}$). In addition, implications, limitations of this study and future research were discussed.

• Journal of Broadcast Engineering
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• v.24 no.1
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• pp.67-76
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• 2019

In this paper, we propose an efficient object classification method based on semantic segmentation for multi-labeled image data. In addition to various pixel unit information and processing techniques such as color information, contour, contrast, and saturation included in image data, a detailed region in which each object is located is extracted as a meaningful unit and the experiment is conducted to reflect the result in the inference. We use a neural network that has been proven to perform well in image classification to understand which object is located where image data containing various class objects are located. Based on these researches, we aim to provide artificial intelligence services that can classify real-time detailed areas of complex images containing various objects in the future.