• Journal of Broadcast Engineering
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• v.17 no.3
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• pp.437-446
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• 2012

A face detection algorithms using two-dimensional (2-D) intensity or color images have been studied for decades. Recently, with the development of low-cost range sensor, three-dimensional (3-D) information (i.e., depth image that represents the distance between a camera and objects) can be easily used to reliably extract facial features. Most people have a similar pattern of 3-D facial structure. This paper proposes a face detection method using intensity and depth images. At first, adaboost algorithm using intensity image classifies face and nonface candidate regions. Each candidate region is divided into $5{\times}5$ blocks and depth values are averaged in each block. Then, $5{\times}5$ block rank pattern is constructed by sorting block averages of depth values. Finally, candidate regions are classified as face and nonface regions by matching the constructed depth map based block rank patterns and a template pattern that is generated from training data set. For template matching, the $5{\times}5$ template block rank pattern is prior constructed by averaging block ranks using training data set. The proposed algorithm is tested on real images obtained by Kinect range sensor. Experimental results show that the proposed algorithm effectively eliminates most false positives with true positives well preserved.

• Journal of Intelligence and Information Systems
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• v.15 no.2
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• pp.49-59
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• 2009

Contents-based image retrieval methods are in general more objective and effective than text-based image retrieval algorithms since they use color and texture in search and avoid annotating all images for search. SIM(Self-organizing Image browsing Map) is one of contents-based image retrieval algorithms that uses only browsable mapping results obtained by SOM(Self Organizing Map). However, SOM may have an error in selecting the right BMU in learning phase if there are similar nodes with distorted color information due to the intensity of light or objects' movements in the image. Such images may be mapped into other grouping nodes thus the search rate could be decreased by this effect. In this paper, we propose an improved SIM that uses HSV color model in extracting image features with color quantization. In order to avoid unexpected learning error mentioned above, our SOM consists of two layers. In learning phase, SOM layer 1 has the color feature vectors as input. After learning SOM Layer 1, the connection weights of this layer become the input of SOM Layer 2 and re-learning occurs. With this multi-layered SOM learning, we can avoid mapping errors among similar nodes of different color information. In search, we put the query image vector into SOM layer 2 and select nodes of SOM layer 1 that connects with chosen BMU of SOM layer 2. In experiment, we verified that the proposed SIM was better than the original SIM and avoid mapping error effectively.

• Journal of the Korea Computer Graphics Society
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• v.24 no.5
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• pp.31-39
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• 2018

In this paper, we propose an automated method for the ground-glass nodule(GGN) classification using GGN-Net based on intensity, texture, and shape-enhanced images in chest CT images. First, we propose the utilization of image that enhances the intensity, texture, and shape information so that the input image includes the presence and size information of the solid component in GGN. Second, we propose GGN-Net which integrates and trains feature maps obtained from various input images through multiple convolution modules on the internal network. To evaluate the classification accuracy of the proposed method, we used 90 pure GGNs, 38 part-solid GGNs less than 5mm with solid component, and 23 part-solid GGNs larger than 5mm with solid component. To evaluate the effect of input image, various input image set is composed and classification results were compared. The results showed that the proposed method using the composition of intensity, texture and shape-enhanced images showed the best result with 82.75% accuracy.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.6
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• pp.267-274
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• 2023

In this paper, a method for estimating the illuminant chromaticity of a scene for an input image is proposed. The illuminant chromaticity is estimated using the illuminant reference region. The conventional method uses a certain number of reference lighting information. By comparing the chromaticity distribution of pixels from the input image with the chromaticity set prepared in advance for the reference illuminant, the reference illuminant with the largest overlapping area is regarded as the scene illuminant for the corresponding input image. In the process of calculating the overlapping area, the weights for each reference light were applied in the form of a Gaussian distribution, but a clear standard for the variance value could not be presented. The proposed method extracts an independent reference chromaticity region from a given reference illuminant, calculates the characteristic values in the r-g chromaticity plane of the RGB color coordinate system for all pixels of the input image, and then calculates the independent chromaticity region and features from the input image. The similarity is evaluated and the illuminant with the highest similarity was estimated as the illuminant chromaticity component of the image. The performance of the proposed method was evaluated using the database image and showed an average of about 60% improvement compared to the conventional basic method and showed an improvement performance of around 53% compared to the conventional Gaussian weight of 0.1.

• The Korean Journal of Archival Studies
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• no.15
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• pp.67-93
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• 2007

This article deals with the short history from 1999 to the present time, how the Korean record and archives management world had grown up, and what the development of the branch of records and archives studies resulted in. First of all, it is looked out upon the transition and feature of each initiative bodies of records management, containing the records producing body, records and archives management body, records and archives professional body, and civil society. As a result, this article points out the disequilibrium state of the records producing body and civil society, for all the growth of records and archives management institutions and records and archives professionals. During the time of establishing the law, the Korean records and archives management had been made a rapid progress by some part of the leading group being to Korean Records and Archives Service and the society of professionals. But it is estimated only the malformed development depending on the model of elites, although we could achieve the establishment of Korean Records and Archives Act. The condition of records and archives management of the Participation Government was distinguish from the state of former times, being driven up the renovation of records and archives management. The main power of the renovation was sought our by overcome of the elite model with the development of archival institutions and professionals extending wide range. Particularly professionals to accept the education of graduate school grew up in quantity and quality and then they let the pattern of the collaboration with archival institutions rake root in Korea. As The Road Map on the Renovation of National Records and Archives Management was made, the government put into practice, so the management of records and archives in Korea could take a step of steady and continuous growth. But the development of the records producing bodies and civil society is staying at the low level as yet. Accordingly it is expected to have the most important means that the professional instruction become to normalize and archivists who posted in public agencies after graduating professional education program discharge their duties. And each public agencies have to speed up to set up the institutions for records management including some archivists so that overcome the condition of underdevelopment as fast as possible.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.387-396
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• 2024

Most very high-resolution (VHR) satellite images provide rational polynomial coefficients (RPC) data to facilitate the transformation between ground coordinates and image coordinates. However, initial RPC often contains geometric errors, necessitating correction through matching with ground control points (GCPs). A GCP chip is a small image patch extracted from an orthorectified image together with height information of the center point, which can be directly used for geometric correction. Many studies have focused on area-based matching methods to accurately align GCP chips with VHR satellite images. In cases with seasonal differences or changed areas, edge-based algorithms are often used for matching due to the difficulty of relying solely on pixel values. However, traditional edge extraction algorithms,such as canny edge detectors, require appropriate threshold settings tailored to the spectral characteristics of satellite images. Therefore, this study utilizes deep learning-based edge information that is insensitive to the regional characteristics of satellite images for matching. Specifically,we use a pretrained pixel difference network (PiDiNet) to generate the edge maps for both satellite images and GCP chips. These edge maps are then used as input for normalized cross-correlation (NCC) and relative edge cross-correlation (RECC) to identify the peak points with the highest correlation between the two edge maps. To remove mismatched pairs and thus obtain the bias-compensated RPC, we iteratively apply the data snooping. Finally, we compare the results qualitatively and quantitatively with those obtained from traditional NCC and RECC methods. The PiDiNet network approach achieved high matching accuracy with root mean square error (RMSE) values ranging from 0.3 to 0.9 pixels. However, the PiDiNet-generated edges were thicker compared to those from the canny method, leading to slightly lower registration accuracy in some images. Nevertheless, PiDiNet consistently produced characteristic edge information, allowing for successful matching even in challenging regions. This study demonstrates that improving the robustness of edge-based registration methods can facilitate effective registration across diverse regions.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.1
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• pp.31-36
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• 2014

Security of chaos communication system that has characteristic of sensitive initial conditions is superior to digital communication systems, but BER(Bit Error Rate) performance is evaluatied badly. So, studies in order to improve the BER performance is important. existing studies, BER performance of proposed chaos transceiver is possible to improve than the CDSK(Correlation Delay Shift Keying) system because it has characteristic that has very few addition elements like noise signal except for the desired signal. Chaos communication system has many symbols because it spreads according to characteristic of chaos map. Therefore, study that can have the good data rate in chaos communication system is required. Information bits of existing chaos modulation system are modulated as -1 and 1 on the basis of BPSK system. However, instead of BPSK system, if chaos communication system is applied high level modulation systems such as QPSK system and 16QAM system, it is possible to have good data rate because more data are transmitted at a time. In the paper, when QPSK system and 16QAM system are applied to proposed chaos transceiver in existing study, we evaluate the SER(Symbol Error Rate) performance and compare the each performance. Also, when QPSK system and 16QAM system are applied to proposed chaos transceiver, we evaluate the anti-jamming performance of proposed system.

• Journal of the Korea Society of Computer and Information
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• v.20 no.3
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• pp.81-88
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• 2015

Recently the Security fields is emerged as a important issue in the world, While a variety of techniques such as a Cloud Computing or a Internet Of Things appeared. In these circumstances, The domestic security fields are divided into the Information Security, the Physical Security and the Convergence Security. and among these security fields, Convergence security is attracted much attention from various industries. the classification systems of a new field Convergence Security has become a very important criteria such about the Statistics calculation, the Analysis of status industry sector and the Road maps. However, In the domestic, The related institutions classified each other differently the Convergence Security Classification. so it is urgently needed a domestic security fields systematic classification due to the problems such as lack of reliability of the accuracy, compatibility of a data. Therefore, this paper will be analyzed to the characteristics of the domestic security classification systems by the cases. and will be proposed the newly improved classification system, to be possible to addition or deletion of an classification entries, and to be easy expanded according to the new technology trends. this proposed to classification system is expected to be utilized as a basis for the construct of a domestic security classification system in a future.

• Journal of Korea Multimedia Society
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• v.12 no.5
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• pp.745-756
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• 2009

An autonomous virtual character can conduct itself like a human after recognizing and interpreting the virtual environment. Artificial vision is mainly used in the recognition of the environment for a virtual character. The present artificial vision that has been developed takes all the information at once from everything that comes into view. However, this can reduce the efficiency and reality of the system by saving too much information at once, and it also causes problems because the speed slows down in the dynamic environment of the game. Therefore, to construct a vision system similar to that of humans, a visual observation system which saves only the required information is needed. For that reason, this research focuses on the descriptive artificial intelligence engine which detects the most important information visually recognized by the character in the virtual world and saves it into the memory by degrees. In addition, a visual system is constructed in accordance with an image transaction theory to make it sense and recognize human feelings. This system finds the attention area of moving objects quickly and effectively through the experiment of the virtual environment with three dynamic dimensions. Also the experiment enhanced processing speed more than 1.6 times.

• Journal of Digital Convergence
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• v.16 no.6
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• pp.187-195
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• 2018

Recently, the 4th industrial revolution has emerged as the core for enhancing national competitiveness, the development of a technology roadmap to efficiently develop related technologies to realize super intelligence as a main feature of the 4th Industrial Revolution is a major task has been highlighted. The objective of this study is to analyze the domestic and foreign technology level in the artificial intelligence field which is the core technology of the 4th Industrial Revolution era and to present the direction of development based on this. The keyword network analysis and the blank technical analysis based on the IPC classification were performed on the data derived from the keyword search of 'AI (Artificial Intelligence)' among domestic and foreign patent data. As a result, the number of domestic artificial intelligence related technology development was 1.2% compared with developed countries such as USA and Europe. In the major development fields, data recognition technology and digital information transmission technology were relatively insufficient. Through this study, we obtained the blank technology as a result of comparative analysis of domestic artificial intelligence related technologies compared to advanced countries and suggested the direction of domestic artificial intelligence technology development in future.