• Journal of Institute of Control, Robotics and Systems
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• v.5 no.7
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• pp.821-826
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• 1999

This paper proposes a face recognition method using feature spaces and image vectors in the image plane. We obtain the 2-D feature space using the self-organizing map which has two inputs from the axis of the given image. The image vector consists of its weights and the average gray levels in the feature space. Also, we can reconstruct an normalized face by using the image vector having no connection with the size of the given face image. In the proposed method, each face is recognized with the best match of the feature spaces and the maximum match of the normally retrieval face images, respectively. For enhancing recognition rates, our method combines the two recognition methods by the feature spaces and the retrieval images. Simulations are conducted on the ORL(Olivetti Research laboratory) images of 40 persons, in which each person has 10 facial images, and the result shows 100% recognition and 14.5% rejection rates for the 20$\times$20 feature sizes and the 24$\times$28 retrieval image size.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.507-517
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• 2003

Self-organizing map (SOM) has been developed mainly by T. Kohonen and his colleagues as a unsupervised learning neural network. Because of its topological ordering property, SOM is known to be very useful in pattern recognition and text information retrieval areas. Recently, data miners use Kohonen´s mapping method frequently in exploratory analyses of large data sets. One problem facing SOM builder is that there exists no sensible criterion for evaluating goodness-of-fit of the map at hand. In this short communication, we propose valid evaluation procedures for the Kohonen SOM of any size. The methods can be used in selecting the best map among several candidates.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.4
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• pp.229-236
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• 2017

This paper presents an efficient object-based color image-retrieval algorithm that is suitable for the classification and retrieval of images from small to mid-scale datasets, such as images in PCs, tablets, phones, and cameras. The proposed method first finds salient regions by using regional feature vectors, and also finds several dominant colors in each region. Then, each salient region is partitioned into small sub-blocks, which are assigned 1 or 0 with respect to the number of pixels corresponding to a dominant color in the sub-block. This gives a binary map for the dominant color, and this process is repeated for the predefined number of dominant colors. Finally, we have several binary maps, each of which corresponds to a dominant color in a salient region. Hence, the binary maps represent the spatial distribution of the dominant colors in the salient region, and the union (OR operation) of the maps can describe the approximate shapes of salient objects. Also proposed in this paper is a matching method that uses these binary maps and which needs very few computations, because most operations are binary. Experiments on widely used color image databases show that the proposed method performs better than state-of-the-art and previous color-based methods.

• Convergence Security Journal
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• v.7 no.4
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• pp.29-34
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• 2007

This paper proposes RCB(Reduced Compact Binary) trie to correct faults of CB(Compact Binary) trie. First, in the case of CB trie, a compact structure was tried for the first time, but as the amount of data was increasing, that of inputted data gained and much difficulty was experienced in insertion due to the dummy nods used in balancing trees. On the other hand, if the HCB trie realized hierarchically, given certain depth to prevent the map from increasing on the right, reached the depth, the method for making new trees and connecting to them was used. Eventually, fast progress could be made in the inputting and searching speed, but this had a disadvantage of the storage space becoming bigger because of the use of dummy nods like CB trie and of many tree links. In the case of RCB trie in this thesis, a capacity is increased by about 60% by completely cutting down dummy nods.

• KIISE Transactions on Computing Practices
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• v.20 no.9
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• pp.498-506
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• 2014

With the development of smart transportation, people are likely to find their paths by using navigation and map application. However, the existing retrieval system cannot output the correct retrieval result due to the inaccurate query. In order to remedy this problem, set-based POI search algorithm was proposed. Subsequently, additionally a method for measuring POI name similarity and POI search algorithm supporting classifying duplicate characters were proposed. These algorithms tried to compensate the insufficient part of the compensate set-based POI search algorithm. In this paper, accuracy improvement methods for measuring string similarity in POI data retrieval system are proposed. By formulization, similarity measurement scheme is systematized and generalized with the development of transportation. As a result, it improves the accuracy of the retrieval result. From the experimental results, we can observe that our accuracy improvement methods show better performance than the previous algorithms.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.2
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• pp.57-68
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• 2004

Recently in order to more effectively and scientifically process forest official tasks, which have been focused on documents and inventories, they should be applied with the up-to-date spatial information technologies. Especially, the forest resource information management based on GIS(geographic information system) and aerial orthophoto is expected not only to play an important role as DSS(decision support system) for domestic forest conservation policy and forestry development industry but also to service forest resource information toward people such as the owners of a mountain rapidly. In this study, the important forest information such as digital topography map, digital forest type map, digital forest cadastral map, digital aerial photographs and attribute data were first reprocessed and constructed in DBMS(data base management system). In addition, forest officials could analyze and retrieve forest information by using detail sub-application systems such as forest cadastral retrieval, forest land development information management, reserved forest information management and forest resource information retrieval. For this, the user interface is developed by using Visual Basic 6.0 and MapObjects 2.1 of ESRI based on CBD(component based development) technology. The result of developing this system will not only perform constructing economical forest and better environment but also be the foundation of domestic spatial technology for forest resource management.

• Journal of Practical Engineering Education
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• v.14 no.2
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• pp.351-359
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• 2022

In this paper, this app is an online learning tool web application that helps learners learn efficiently. It discusses how learners can improve their learning efficiency in these three aspects: retrieval practice, systematization, metacognition. Through this web service, learners can proceed with learning with a flash card-based retrieval practice. In this case, a method of managing a flash card in a form similar to a directory-file system using a composite pattern is described. Learners can systematically organize their knowledge by converting flash cards into a mind map. The color of the mind map varies according to the learner's learning progress, and learners can easily recognize what they know and what they do not know through color. In this case, it is proposed to build a deep learning model to improve the accuracy of an algorithm for determining and predicting learning progress.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.316-318
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• 2012

본 논문의 목적은 이동 로봇이 일정한 작업 공간을 이동하며 정보를 수집한는 시스템을 구성하는 것이다. 작업 공간에는 초음파 발신장치가 있어 로봇이 이 초음파를 수신하여 자신의 위치를 확인한다. 로봇은 특정 위치의 정보를 획득하여 중앙 서버로 전송하고 서버는 이 정보를 바탕으로 3D map을 생성한다.

• Journal of the Korea Society of Computer and Information
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• v.12 no.4
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• pp.11-19
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• 2007

This paper proposes RCB(Reduced Compact Binary) tie to correct faults of both CB(Compact Binary) tie and HCB(Hierarchical Compact Binary) trie. First, in the case of CB trie, a compact structure was tried for the first time, but as the amount of data was increasing, that of inputted data gained and much difficulty was experienced in insertion due to the dummy nods used in balancing trees. On the other hand, if the HCB trie realized hierarchically, given certain depth to prevent the map from increasing on the right, reached the depth, the method for making new trees and connecting to them was used. Eventually, fast progress could be made in the inputting and searching speed, but this had a disadvantage of the storage space becoming bigger because of the use of dummy nods like CB trie and of many tree links. In the case of RCB trie in this thesis, the tree-map could be reduced by about 35% by completely cutting down dummy nods and the whole size by half, compared with the HCB trie.

• The Journal of the Korea Contents Association
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• v.11 no.6
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• pp.18-25
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• 2011

Salient object attracts more and more attention from researchers due to its important role in many fields of multimedia processing like tracking, segmentation, adaptive compression, and content-base image retrieval. Usually, a saliency map is binarized into black and white map, which is considered as the binary mask of the salient object in the image. Still, the threshold is heuristically chosen or parametrically controlled. This paper suggests using the global optimal threshold to perform saliency map thresholding. This work also considers the usage of multi-level optimal thresholds and the local adaptive thresholds in the experiments. These experimental results show that using global optimal threshold method is better than parametric controlled or local adaptive threshold method.