• Journal of KIISE:Software and Applications
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• v.28 no.2
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• pp.132-140
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• 2001

야외에서 인지되는 자연 경치는 다양한 개체, 빛의 산란, 또는 변화를 주는 많은 요소들 때문에 컴퓨터 영상처리에서 인식하기가 쉽지 않다. 본 논문에서는 다층 인지 신경망을 이용하여 도로가 포함된 야외영상에 나타나는 개체들을 인식하는 방법을 연구하였다. 자연 영상을 영역화한 후, 각각의 영역들에 대하여 색상과 기하학적인 특성에 근거하여 특성벡터를 추출하고 이를 신경망에 입력하여 각 영역을 구분하는 2단계의 알고리듬을 제안한다. 먼저 야외 영상들을 개선된 영역 확장법과 병합과정에 의하여 개체별로 영역화하였다. 영역화된 연상은 자연 영상과 함께 영상 데이타베이스에 저장되고, 이 자료들을 이용하여 각 영역의 특성벡터를 계산하였다. 이 특성 벡터를 구성된 신경망의 입력층에 전달하면, 각 영역은 27개의 개체 중의 하나로 출력층에서 인식된다. 제안된 방법은 학습에 사용된 데이타, 학스베 사용되지 않은 새로운 데이타, 그리고 모두 합하여 놓은 데이타의 세가지 데이타 군에서 무작위로 선별하여 인식률을 측정하였다. 학습된 데이타에서는 99.4%까지의 인식률을 보여주었고, 학습되지 않은 데이타에 대해서도 최고 89.1%까지의 인식률을 나타내었다. 제안된 방법은 평균적으로 88.1%~97.9%의 인식률을 보여주어 자연 경치의 인식에 신뢰성이 있는 방법으로 사용될 수 있음을 증명하였다.

• The Transactions of the Korea Information Processing Society
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• v.1 no.2
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• pp.163-171
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• 1994

The paper describes a design and implementation of a user interface system between a temporal database management system (DBMS) and a graphical display system. This interfacing system consists of a temporal DBMS, a graphical display system, and an interface control system. The temporal DBMS supports time and the graphical display system draws a graphic using an icon. The interfacing system shows the graphical animation with time factors by using a graphical knowledgeable. We describe how to interface them as well as the structure of temporal DBMS and graphical display system.

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.5
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• pp.510-521
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• 2001

Image retrieval system should be construct for searching fast, efficient image be extract the accurate feature information of image with more massive and more complex characteristics. Image retrieval system are essential differences between image databases and traditional databases. These differences lead to interesting new issues in searching of image, data modeling. So, cause us to consider new generation method of database, efficient retrieval method of image. In this paper, To extract feature information of edge using in searching from input image, we was performed to extract the edge by convolution Laplacian mask and input image, and we implemented the annotation-based and content-based image retrieval system for searching fast, efficient image by generation image database from extracting feature information of edge and metadata. We can improve the performance of the image contents retrieval, because the annotation-based and content-based image retrieval system is using image index which is made up of the content-based edge feature extract information represented in the low level of image and annotation-based edge feature information represented in the high level of image. As a conclusion, image retrieval system proposed in this paper is possible the accurate management of the accumulated information for the image contents and the information sharing and reuse of image because the proposed method do construct the image database by metadata.

• Journal of Korean Society for Geospatial Information Science
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• v.4 no.1 s.6
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• pp.43-55
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• 1996

The objective of this research is to design a digital map database structure supporting video images which is one of the fundamental elements of hypermap. In order to reach the research objective, the work includes the identification of the relationships between two dimensional digital map database and video elements. The proposed database model has functions for interactive browsing between video image frames and specific points on two dimensional digital map, fer connecting the map elements and features on video images. After that, the images and the database are transformed to the pilot system fer testing the map database structure. The pilot project results indicate that the map database structure can integrate functionally two dimensional digital map and video images.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.7
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• pp.748-752
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• 2008

Various services are developed from advancement of satellite imagery methodologies and internet infrastructure expansions. However, most of these services still rely upon low-resolution satellite images combined with DEM models. In this paper, we have implemented the raw data processing modules and other modules that transfer and render high-spatial resolution satellite images for efficient streaming services in web environments. By utilizing the Bukhan-mountain data as a pilot study, the paper has proposed the efficient approach to solve graphical problems in real time processing the large geographical area.

• Korean Journal of Cognitive Science
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• v.7 no.4
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• pp.63-74
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• 1996

• Annual Conference on Human and Language Technology
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• 1995.10a
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• pp.131-136
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• 1995

최근들어 자연스럽게 필기된 한글을 인식함으로써 정보 입력 과정을 자동화하기 위한 오프라인 한글 글씨 인식에 관한 연구가 활발히 진행되고 있다. 오프라인 한글 글씨 인식에 관한 연구에 있어서 반드시 확보되어야 하는 연구 환경으로 대용량 오프라인 한글 글씨 데이타베이스의 구축을 들 수 있는데, 본 논문에서는 시스템공학연구소 국어공학센터의 국어 정보 베이스 개발사업의 일환으로 추진중인 오프라인 한글 글씨 데이타베이스의 구축현황에 대해 간략히 소개하고자 한다. 오프라인 한글 글씨 데이타베이스의 구축은 크게 글씨 데이타베이스 설계, 글씨 데이타 수집, 용지 스캔 및 문자 단위 분할, 데이타베이스 검증의 4 단계로 구성된다. 본 연구에서는 다양한 변형을 갖는 글씨체의 수집을 데이타베이스 구축시 가장 고려해야 할 요소로 삼았으며, 고품질의 일관성 있는 글씨 데이타베이스 구축을 위해 데이타베이스 설계 단계와 검증 단계에 많은 시간을 할애했다. 마지막으로 본 연구에서는 WWW(World Wide Web)의 HTML(Hyper Text Markup Language)을 이용하여 편리 한 사용자 인터페이스를 구현함으로써 사용자들이 쉽게 한글 글씨 영상을 검색 할 수 있음은 물론 인식 알고리즘의 개발에 사용 가능한 형태의 화일을 제공받을 수 있도록 구성하고 있다. 현재는 KS C 완성형 한글 2,350자 중에서 사용 빈도순 상위 520자에 대한 한글 글씨 1,000벌을 수집하여 명도영상 데이타베이스를 구축 중에 있으며, 향후 2년간 나머지 1,830자에 대한 한글 글씨 데이타를 수집하여 데이타베이스를 완성하고자 한다. 구축된 글씨 데이타베이스는 조만간 국내의 오프라인 한글 글씨 인식 연구자들에게 제공되어 우수한 인식 알고리즘의 개발을 위한 중요한 실험 데이타로서 사용될 예정이며, 개발된 인식 시스템에 대한 객관적인 성능 평가에 있어서도 크게 기여하여 국내의 오프라인 한글 글씨 인식에 관한 연구를 활성화시켜주는 계기가 될 것으로 기대된다.

• Journal of KIISE:Databases
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• v.33 no.4
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• pp.372-383
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• 2006

Among the approaches to image database management, content-based image retrieval (CBIR) is viewed as having the best support for effective searching and browsing of large digital image libraries. Typical CBIR systems allow a user to provide a query image, from which low-level features are extracted and used to find 'similar' images in a database. However, there exists the semantic gap between human visual perception and low-level representations. An effective methodology for overcoming this semantic gap involves relevance feedback to perform feature re-weighting. Current approaches to feature re-weighting require the number of components for a feature representation to be the same for every image in consideration. Following this assumption, they map each component to an axis in the n-dimensional space, which we call the metric space; likewise the feature representation is stored in a fixed-length vector. However, with the emergence of features that do not have a fixed number of components in their representation, existing feature re-weighting approaches are invalidated. In this paper we propose a feature re-weighting technique that supports features regardless of whether or not they can be mapped into a metric space. Our approach analyses the feature distances calculated between the query image and the images in the database. Two-sided confidence intervals are used with the distances to obtain the information for feature re-weighting. There is no restriction on how the distances are calculated for each feature. This provides freedom for how feature representations are structured, i.e. there is no requirement for features to be represented in fixed-length vectors or metric space. Our experimental results show the effectiveness of our approach and in a comparison with other work, we can see how it outperforms previous work.

• Journal of Astronomy and Space Sciences
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• v.13 no.2
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• pp.198-209
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• 1996

In this paper, we present an image compression algorithm that is capable of significantly reducing the vast mount of information contained in multispectral images. The developed algorithm exploits the spectral and spatial correlations found in multispectral images. The scheme encodes the difference between images after contrast/brightness equalization to remove the spectral redundancy, and utilizes a two-dimensional wavelet trans-form to remove the spatial redundancy. The transformed images are than encoded by hilbert-curve scanning and run-length-encoding, followed by huffman coding. We also present the performance of the proposed algorithm with KITSAT-1 image as well as the LANDSAT MultiSpectral Scanner data. The loss of information is evaluated by peak signal to noise ratio (PSNR) and classification capability.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.10
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• pp.2091-2101
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• 1997

Conventional method for fractal image decoding requires high-degree computational complexity in decoding propocess, because of iterated contractive transformations applied to whole range blocks. In this paper, we propose a fast decoding algorithm of fractal image using data depence in order to reduce computational complexity for iterated contractive transformations. Range of reconstruction image is divided into a region referenced with domain, called referenced range, and a region without reference to domain, called unreferenced range. The referenced range is converged with iterated contractive transformations, and the unreferenced range can be decoded by convergence of the referenced range. Thus the unreferenced range is called data dependence region. We show that the data dependence region can be deconded by one transformation when the referenced range is converged. Consequently, the proposed method reduces computational complexity in decoding process by executing iterated contractive transformations for the referenced range only.