• Journal of Korea Multimedia Society
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• v.16 no.4
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• pp.418-429
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• 2013

This paper presents a GIS vector map data compression scheme based on hybrid polyline simplification method and SEC(spatial energy compaction). The proposed method extracts all layers which contain polylines in the GIS vector map and compress all polylines in extracted layers by the hybrid polyline simplification and SEC based on MAE(minimum area error) for each segment in the line. The proposed simplification and SEC increase the compression ratio while preserving the shape quality. We analyze the visual aspects and compression efficiency between the original GIS vector map and the compressed map. From experimental results, we verify that our method has the higher compression efficiency and visual quality than conventional methods.

• Journal of KIISE:Information Networking
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• v.36 no.6
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• pp.536-544
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• 2009

This paper proposed a novel vector digital watermarking method to protect copyright. The proposed method embeds watermarks after finding the closest pair of points and calculating the distance of the points. We tested the robustness of the method through several attacks on watermarked data. The experimental results show that the proposed method has more robustness than previous methods. And the new method doesn't change the topology of the vector data. Therefore, this method can be 'the vector digital watermarking for GIS.

• Journal of KIISE:Databases
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• v.29 no.5
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• pp.358-366
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• 2002

Feature-based similarity retrieval become an important research issue in image database systems. The features of image data are useful to discrimination of images. In this paper, we propose the high speed k-Nearest Neighbor search algorithm based on Self-Organizing Maps. Self-Organizing Maps(SOM) provides a mapping from high dimensional feature vectors onto a two-dimensional space and generates a topological feature map. A topological feature map preserves the mutual relations (similarities) in feature spaces of input data, and clusters mutually similar feature vectors in a neighboring nodes. Therefore each node of the topological feature map holds a node vector and similar images that is closest to each node vector. We implemented a k-NN search for similar image classification as to (1) access to topological feature map, and (2) apply to pruning strategy of high speed search. We experiment on the performance of our algorithm using color feature vectors extracted from images. Promising results have been obtained in experiments.

• Spatial Information Research
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• v.17 no.1
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• pp.51-66
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• 2009

There have been concerned about the copyright as numerous data are digitalized because of the growth of performance of the computer and Internet. Digital watermarking is one of strong methods to protect copyright. We proposed a novel digital watermarking for vector map data. Although vector map data are used widely in GIS, there is little interest in copyright. The proposed method is to embed and extract watermarks using CRC principle. The experimental results show that this method can protect the copyright of the vector map by extracting embedded watermarks. Therefore, the proposed method can be utilized as the technique to protect vector map data.

• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.515-522
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• 2001

Feature-based similarity retrieval become an important research issue in image database systems. The features of image data are useful to discrimination of images. In this paper, we propose the highspeed k-Nearest Neighbor search algorithm based on Self-Organizing Maps. Self-Organizing Map(SOM) provides a mapping from high dimensional feature vectors onto a two-dimensional space. A topological feature map preserves the mutual relations (similarity) in feature spaces of input data, and clusters mutually similar feature vectors in a neighboring nodes. Each node of the topological feature map holds a node vector and similar images that is closest to each node vector. We implemented about k-NN search for similar image classification as to (1) access to topological feature map, and (2) apply to pruning strategy of high speed search. We experiment on the performance of our algorithm using color feature vectors extracted from images. Promising results have been obtained in experiments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.579-582
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• 2001

Vector Transformation is a new method unified vector quantization and coding. So far, codebook generation applied to coding was LBG algorithm. But using the advantage of SOFM(Self-Organizing Feature Map) based on neural network can improve a system's performance. In this paper, we generated VTC(Vector Transformation Coding) codebook applied with SOFM algorithm and compare the result for several coding rates with LBG algorithm. The problem of Vector quantization is complicated calculation and codebook generation. So, to solve this problem, we used neural network approach method.

• Spatial Information Research
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• v.21 no.3
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• pp.31-43
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• 2013

Although the vector map data possesses much higher values than other types of multimedia, the data copyright and the protection against illegal duplication are still far away from the attention. This paper proposes a novel watermarking technique which is both robust to diverse attacks and optimized to a vector map structure. Six approaches are proposed for the design of the watermarking algorithm: point-based approach, building a minimum perimeter triangle, watermark embedding in the length ratio, referencing to the pixel position of the watermark image, grouping, and using the one-way function. Our method preserves the characteristics of watermarking such as embedding effectiveness, fidelity, and false positive rate, while maintaining robustness to all types of attack except a noise attack. Furthermore, our method is a blind scheme in which robustness is independent of the map data. Finally, our method provides a solution to the challenging issue of degraded robustness under severe simplification attacks.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.11C
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• pp.940-947
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• 2010

Motion Compensated Frame Interpolation(MCFI) has been used to reduce motion jerkiness for dynamic scenes and motion blurriness for LCD-panel display as post processing for high quality display. However, MCFI that directly uses the motion information often suffers from annoying artifacts such as blockiness, ghost effects, and deformed structures. So in this paper, we propose a novel edge-based adaptively weighted vector median filter as post-processing. At first, the proposed method generates an edge direction map through a sobel mask and a weighted maximum frequent filter. And then, outlier MVs are removed by average of angle difference and replaced by a median MV of $3{\times}3$ window. Finally, weighted vector median filter adjusts the weighting values based on edge direction derived from spatial coherence between the edge direction continuity and motion vector. The results show that the performance of PSNR and SSIM are higher up to 0.5 ~ 1 dB and 0.4 ~ 0.8 %, respectively.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2021.06a
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• pp.180-182
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• 2021

본 논문은 눈 랜드마크 위치 검출과 시선 방향 벡터 추정이 하나의 딥러닝 네트워크로 통합된 시선 추정 네트워크를 제안한다. 제안하는 네트워크는 Stacked Hourglass Network[1]를 백본(Backbone) 구조로 이용하며, 크게 랜드마크 검출기, 특징 맵 추출기, 시선 방향 추정기라는 세 개의 부분으로 구성되어 있다. 랜드마크 검출기에서는 눈 랜드마크 50개 포인트의 좌표를 추정하며, 특징 맵 추출기에서는 시선 방향 추정을 위한 눈 이미지의 특징 맵을 생성한다. 그리고 시선 방향 추정기에서는 각 출력 결과를 조합하고 이를 통해 최종 시선 방향 벡터를 추정한다. 제안하는 네트워크는 UnityEyes[2] 데이터셋을 통해 생성된 가상의 합성 눈 이미지와 랜드마크 좌표 데이터를 이용하여 학습하였으며, 성능 평가는 실제 사람의 눈 이미지로 구성된 MPIIGaze[3] 데이터 셋을 이용하였다. 실험을 통해 시선 추정 오차는 0.0396 MSE(Mean Square Error)의 성능을 보였으며, 네트워크의 추정 속도는 42 FPS(Frame Per Second)를 나타내었다.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.1031-1036
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• 2007

최근 가상현실 기술의 주요 연구 동향으로 몰입감을 증가시키는 실감공간 구현구술이 주목 받고 있다. 실감공간 기술이란 서로 다른 공간에 떨어져 있는 사용자가 같은 공간에 있는 효과를 구현하는 기술이다. 본 논문에서는 특히 상호간의 주변 환경을 일치시키는 기술에 중점을 두고, 실시간으로 두 공간의 조명정보를 일치시키는 기술로서 2가지 핵심 내용을 소개한다. 첫째는 비주얼 헐 데이터를 기반으로 고속으로 노말벡터를 추출하는 방법이고, 둘째는 사용자 주변 조명 환경 정보를 반영하는 라이팅 방법이다. 본 논문에서 수행한 첫번째 방법은 비주얼 헐 데이터의 depth존재영역에서 노말맵을 계산하도록 하고, 노말맵을 계산할 때 주변 폴리곤들 기하학적 변화가 심할수록 노말맵 계산에 사용하는 주변 벡터의 선태을 늘리거나 줄이는 방식으로, 불필요한 계산량을 감소시켰다. 본 논문에서 수행한 두번째 방법에서는 주변 조명 정보에서 빛의 세기와 라이팅을 반영할 객체의 반사율의 특성을 고려하여 라이팅에 사용할 광원을 선택적으로 반영하여 불필요한 연산량을 감소시켰다. 종래의 영상기반 라이팅 기술이 사전에 촬영된 영상을 사용하거나 정지영상에 적용되는 연구를 한 반면에 본 논문은 실시간에서 라이팅을 구현하기 위한 시도로서 고속 라이팅 연산 기법을 제시하고 있다. 본 연구의 결과를 이용하면 영상기반 라이팅 연구의 실제적이고도 폭넓은 적용이 가능할 것으로 사료되며 고화질의 콘텐츠 양산에도 기여할 것으로 사료된다.