• Journal of the Korea Computer Graphics Society
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• v.11 no.1
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• pp.40-49
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• 2005

Several optimization techniques have been proposed for volume ray casting, but these cannot achieve real-time frame rates. In addition, it is difficult to apply them to some applications that require perspective projection. Recently, hardware-based methods using 3D texture mapping are being used for real-time volume rendering. Although rendering speed approaches real time, the larger volumes require more swapping of volume bricks for the limited texture memory. Also, image quality deteriorates compared with that of conventional volume ray casting. In this paper, we propose a data structure for real-time volume ray casting named PERM (Precomputed dEnsity and gRadient Map). The PERM stores interpolated density and gradient vector for quantized cells. Since the information requiring time-consuming computations is stored in the PERM, our method can ensure interactive frame rates on a consumer PC platform. Our method normally produces high-quality images because it is based on conventional volume ray casting.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.1
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• pp.45-50
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• 2003

Texture analysis has been efficiently utilized in the area of terrain classification. In this application features have been obtained in the 2D image domain. This paper suggests 3D co-occurrence texture features by extending the concept of co-occurrence to 3D world. The suggested 3D features are described using co-occurrence histogram of digital elevations at two contiguous position as co-occurrence matrix. The practical construction of co-occurrence matrix limits the number of levels of digital elevation. If the digital elevation is quantized into the number of levels over the whole DEM(Digital Elevation Map), the distinctive features can not be obtained. To resolve the quantization problem, we employ local quantization technique which preserves the variation of elevations. Experiments has been carried out to verify the proposed 3D co-occurrence features, and the addition of the suggested features significantly improves the classification accuracy.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.223-230
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• 2003

Feature-based similarity retrieval has become an important research issue in multimedia database systems. The features of multimedia data are useful for discriminating between multimedia objects (e 'g', documents, images, video, music score, etc.). For example, images are represented by their color histograms, texture vectors, and shape descriptors, and are usually high-dimensional data. The performance of conventional multidimensional data structures(e'g', R- Tree family, K-D-B tree, grid file, TV-tree) tends to deteriorate as the number of dimensions of feature vectors increases. 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 dimensionalities. Self-Organizing Maps (SOMs) provide mapping from high-dimensional feature vectors onto a two dimensional space. The mapping preserves the topology of the feature vectors. The map is called a topological of the feature map, and preserves the mutual relationship (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. A best-matching-image-list. (BMIL) holds similar images that are closest to each codebook vector. In a topological feature map, there are empty nodes in which no image is classified. When we build an R＊-tree, we use codebook vectors of topological feature map which eliminates the empty nodes that cause unnecessary disk access and degrade retrieval performance. We experimentally compare the retrieval time cost of a SOM-based R＊-tree with that of an SOM and an R＊-tree using color feature vectors extracted from 40, 000 images. The result show that the SOM-based R＊-tree outperforms both the SOM and R＊-tree due to the reduction of the number of nodes required to build R＊-tree and retrieval time cost.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1430-1434
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• 2004

Recently, Virtual reality parts is applied in various fields of industry. In this paper we developed basic components for virtual robot control system interfaced with real environment. For this, a real robot with virtual interface module is developed and virtual robot of similar image with real robot is created by putting on 3D graphic texture to the real robot. To build an unknown environment to be linked with virtual environment, we proposed a hough transformation based algorithm. Our proposed algorithm consists of navigation module by using fuzzy engine and map building module. Experiments using a developed robot illustrate the method.

• Proceedings of the KSEG Conference
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• 2004.03a
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• pp.13001-13021
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• 2004

공학지질도(Engineering geology map)는 토목공사의 예비단계에서 필요한 지질자료를 도면에 표시하여 지질공학 계획(Engineering geological plan)의 수립과 토목설계 정보를 제공 위하여 작성하는 특수지질도이다. (Report by the geological society engineering group working party, 1972). 공학지질도에는 지질공학, 토목 또는 건축 기술자들이 필요로 하는 지질정보를 수록하기 때문에 기존의 지질도(Geological map)와는 다르게 작성한다. 즉, 지질도는 암석의 광물조성, 석기 (texture), 층서, 지질구조 등의 지질학적 분석을 통하여 지각의 생성 메카니즘과 지구의 역사를 규명하는 것이 궁극적 작성 목표이다. 따라서 지질도에는 공학기술자들이 필요로 하는 암석과 토층의 물리적/공학적 특성이나 지하수에 대한 정량적 정보 등이 수록되지 않을 뿐 아니라 공학기술자들에게 필요한 인간 생활권 부분인 지표와 천부의 지질학적 특성이 제외되는 경우가 많다. (중략)

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.673-675
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• 2003

제한된 성능의 그래픽처리기를 이용하여 대규모 지형 가시화를 수행할 때 지형데이터, 특히 텍스처 데이터의 효율적 관리는 성능향상에 영향을 끼치는 주요 요소 중 하나이다. 본 연구에서는 지형 셀의 쿼드 트리 구조를 기반으로 시점 매개변수와 그래픽 처리기의 성능을 고려하여 실시간 렌더링 시 효율적으로 텍스처를 관리할 수 있는 다단계의 시계범위 맵 (View Frustum map with classes) 방법을 제안하였고, 비행모의 시스템에 적용하여 기존의 텍스처 관리기법과 비교하였다. 실험결과를 통하여 본 연구에서 제안한 방법이 기존의 방법과 비교하여 영상품질은 유사하게 유지하면서 성능을 크게 향상시킨 방법임을 알 수 있었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.1
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• pp.228-246
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• 2011

A 3D model based approach for a face representation and recognition algorithm has been investigated as a robust solution for pose and illumination variation. Since a generative 3D face model consists of a large number of vertices, a 3D model based face recognition system is generally inefficient in computation time and complexity. In this paper, we propose a novel 3D face representation algorithm based on a pixel to vertex map (PVM) to optimize the number of vertices. We explore shape and texture coefficient vectors of the 3D model by fitting it to an input face using inverse compositional image alignment (ICIA) to evaluate face recognition performance. Experimental results show that the proposed face representation and recognition algorithm is efficient in computation time while maintaining reasonable accuracy.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.3
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• pp.129-135
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• 2011

In this paper, we propose the algorithm using optical flow and machine learning-based segmentation for the 3D conversion of 2D video. For the segmentation allowing the successful 3D conversion, we design a new energy function, where color/texture features are included through machine learning method and the optical flow is also introduced in order to focus on the regions with the motion. The depth map are then calculated according to the optical flow of segmented regions, and left/right images for the 3D conversion are produced. Experiment on various video shows that the proposed method yields the reliable segmentation result and depth map for the 3D conversion of 2D video.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.1
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• pp.26-34
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• 1996

One of the challenging tasks in visual inspection using CCD camera is to identify surface defects in an image with complex textured backgeound. In microscopic view, the surface of real objects shows regular or random textured patterns. In this paper, we present a visual inspection algorithm to extract abnormal surface defects in an image with textured background. The algorithm uses the space and frequency information at the same time by introducing the Dominant Frequency Map(DFM) which can describe the frequency characteristics of every small local region of an input image. We demonstrate the feasibility and effectiveness of the method through a series of real experiments for a 14" TV CRT mold. The method successfully identifies a variety of infinitesimal defects, whose size is larger than $50\mu\textrm{m}$, of the mold. The experimental results show that the DFM based method is less sensitive to the environmental changes, such as illumination and defocusing, than conventional vision techniques.ques.

• Korean Journal of Remote Sensing
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• v.23 no.1
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• pp.33-42
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• 2007

In this paper, an iterative MAP approach using a Bayesian model based on the lognormal distribution for image intensity and a GRF for image texture is proposed for despeckling the SAR images that are corrupted by multiplicative speckle noise. When the image intensity is logarithmically transformed, the speckle noise is approximately Gaussian additive noise, and it tends to a normal probability much faster than the intensity distribution. MRFs have been used to model spatially correlated and signal-dependent phenomena for SAR speckled images. The MRF is incorporated into digital image analysis by viewing pixel types as slates of molecules in a lattice-like physical system defined on a GRF Because of the MRF-SRF equivalence, the assignment of an energy function to the physical system determines its Gibbs measure, which is used to model molecular interactions. The proposed Point-Jacobian Iterative MAP estimation method was first evaluated using simulation data generated by the Monte Carlo method. The methodology was then applied to data acquired by the ESA's ERS satellite on Nonsan area of Korean Peninsula. In the extensive experiments of this study, The proposed method demonstrated the capability to relax speckle noise and estimate noise-free intensity.