• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.4
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• pp.539-547
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• 2011

In this paper, a method of generating path planning map is developed using digitalized geographic information such as FDB(Feature DataBase). FDB is widely used by the Army and needs to be applied to all weapon systems of newly developed. For the autonomous navigation of a robot, it is necessary to generate a path planning map by which a global path can be optimized. First, data included in FDB is analyzed in order to identify meaningful layers and attributes of which information can be used to generate the path planning map. Then for each of meaningful layers identified, a set of values of attributes in the layer is converted into the traverse cost using a matching table in which any combination of attribute values are matched into the corresponding traverse cost. For a certain region that is gridded, i.e., represented by a grid map, the traverse cost is extracted in a automatic manner for each gird of the region to generate the path planning map. Since multiple layers may be included in a single grid, an algorithm is developed to fusion several traverse costs. The proposed method is tested using a experimental program. Test results show that it can be a viable tool for generating the path planning map in real-time. The method can be used to generate other kinds of path planning maps using the digitalized geographic information as well.

• Proceedings of the KAIS Fall Conference
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• 2006.11a
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• pp.220-224
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• 2006

The text to be included in the natural images has many important information in the natural image. Therefore, if we can extract the text in natural images, It can be applied to many important applications. In this paper, we propose a text region extraction method using pattern histogram of character-edge map. We extract the edges with the Canny edge detector and creates 16 kind of edge map from an extracted edges. And then we make a character-edge map of 8 kinds that have a character feature with a combination of an edge map. We extract text region using 8 kinds of character-edge map and 16 kind of edge map. Verification of text candidate region uses analysis of a character-edge map pattern histogram and structural feature of text region. The method to propose experimented with various kind of the natural images. The proposed approach extracted text region from a natural images to have been composed of a complex background, various letters, various text colors effectively.

• Journal of the Korea Society of Computer and Information
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• v.17 no.7
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• pp.33-43
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• 2012

In With increase of a demand on the spatial data, the need of spatial data model which can effectively store and manege spatial objects becomes more important in many GIS applications. There are many researches on the spatial data model. Several data models were proposed for some special functions, however, there are still many problems in the management and applications. Digital Map is one of spatial data model which is being used in Korea. The existing Digital Map is based on the Tiles. This approach needs more cost in its construction and management. Therefore, in this paper, we propose a feature-based seamless data model with Digital map 2.0 which is based on Tiles. This model can be easily constructed and managed in the large databases so that it is able to apply to any systems. The proposed model uses the relationships between features to correct updated data and the Unique Feature IDentifier(UFID) also makes system to search and manage the feature data more easily and efficiently.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.11a
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• pp.179-182
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• 2003

기존의 단일영상을 통한 얼굴인식기술이 갖는 단점을 극복하고자 본 논문에서는 스테레오 영상을 사용하여 단일영상의 제약조건 약화와 스테레오 영상의 깊이 정보를 이용한 보다 강건한 얼굴정보의 추출을 통한 다양한 특징 정보를 이용함으로써 얼굴인식의 인식률을 향상 시키고자 하였다.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.37.4-37
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• 2002

$\textbullet$ Feature extraction method for robot application $\textbullet$ Using ultrasonic sensor arrays $\textbullet$ Differentiate the target as plane, corner and edge $\textbullet$ Neural network approach

• The KIPS Transactions:PartD
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• v.8D no.5
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• pp.507-512
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• 2001

Feature-based similarity has become an important research issue in multimedia database systems. The features of multimedia data are useful for discriminating between multimedia objects. the performance of conventional multidimensional data structures tends to deteriorate as the number of dimensions of feature vectors increase. 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-dimensionalties. Self-Organizingf Maps (SOMs) provide mapping from high-dimensional feature vectors onto a two-dimensional space. The map is called a topological feature map, and preserves the mutual relationships (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. We experimentally compare the retrieval time cost of a SOM-based $R^{*}-Tree$ with of an SOM and $R^{*}-Tree$ using color feature vectors extracted from 40,000 images. The results show that the SOM-based $R^{*}-Tree$ outperform both the SOM and $R^{*}-Tree$ due to reduction of the number of nodes to build $R^{*}-Tree$ and retrieval time cost.

• 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.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.43-54
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• 2005

This paper proposes a novel texture segmentation method using Bayesian image segmentation method and SOM(Self Organization feature Map). Multi-scale wavelet coefficients are used as the input of SOM, and likelihood and a posterior probability for observations are obtained from trained SOMs. Texture segmentation is performed by a posterior probability from trained SOMs and MAP(Maximum A Posterior) classification. And the result of texture segmentation is improved by context information. This proposed segmentation method shows better performance than segmentation method by HMT(Hidden Markov Tree) model. The texture segmentation results by SOM and multi-sclae Bayesian image segmentation technique called HMTseg also show better performance than by HMT and HMTseg.

• The Journal of the Acoustical Society of Korea
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• v.14 no.2
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• pp.101-112
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• 1995

In order to construct a feature map-based phoneme classification system for speech recognition, two procedures are usually required. One is clustering and the other is labeling. In this paper, we present a phoneme classification system based on the Kohonen's Self-Organizing Feature Map (SOFM) for clusterer and labeler. It is known that the SOFM performs self-organizing process by which optimal local topographical mapping of the signal space and yields a reasonably high accuracy in recognition tasks. Consequently, SOFM can effectively be applied to the recognition of phonemes. Besides to improve the performance of the phoneme classification system, we propose the learning algorithm combined with the classical K-mans clustering algorithm in fine-tuning stage. In order to evaluate the performance of the proposed phoneme classification algorithm, we first use totaly 43 phonemes which construct six intra-class feature maps for six different phoneme classes. From the speaker-dependent phoneme classification tests using these six feature maps, we obtain recognition rate of $87.2\%$ and confirm that the proposed algorithm is an efficient method for improvement of recognition performance and convergence speed.

• Journal of Korea Spatial Information System Society
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• v.7 no.3 s.15
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• pp.107-118
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• 2005

Various spatial data are utilized, through geographic information system, for the process to make a decision related to space. Generally, spatial data is large in quantity and it costs high and takes quite a long time for producing and maintaining it. An existing spatial data management system, tile-based one, for digital map manages spatial data being separated by a uniform data unit called tile. These systems can be implemented easily but have many problems such as they can directly store and manage feature included in tile. Therefore, in this paper, we suggest a feature-based spatial data management system for digital map. The proposed system is able to store and manage spatial data in the unit of feature directly. Hence this system is able to immediately update any change in the data and to supply users with the updated data without any delay. The proposed system can not only support a function of data input, management, supplying and update but also support unity origin coordinate conversion, UFID creation, feature unifying, feature dividing and metadata input which is not supported by the existing tile-based system. The proposed system can easily manage spatial data and can increase efficiency in processing and application.