• Journal of KIISE:Computer Systems and Theory
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• v.27 no.2
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• pp.117-122
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• 2000

The fractal image compression is based on the self-similarity that some area in an image is very similar to others. This compression method offers high compression ratio and fast decompression, but it has very long encoding time. To cut-off the encoding time, most researches give a restriction on domain blocks to be compared with a range block or make an effective search sequence of the domain blocks for a range block. However, most of them take much encoding time yet. In this research, we propose an algorithm that greatly reduces the encoding time by considering the coherence between range blocks. This algorithm first classifies all range blocks into some groups using the coherence between range blocks, and then searches corresponding domain blocks only for the key block of each group. If this scheme is joined in a prior work of the other fractal compression algorithm, it will give a great effectiveness to encoding time.

• Journal of the Korean Society of Radiology
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• v.8 no.7
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• pp.435-441
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• 2014

This paper suggests techniques to enhance coding time which is a problem in traditional fractal compression and to improve fidelity of reconstructed images by determining fractal coefficient through adaptive selection of block approximation formula. First, to reduce coding time, we construct a linear list of domain blocks of which characteristics is given by their luminance and variance and then we control block searching time according to the first permissible threshold value. Next, when employing three-level block partition, if a range block of minimum partition level cannot find a domain block which has a satisfying approximation error, There applied to 24-bpp color image compression and image techniques. The result did not occur a loss in the image quality of the image when using the encoding method, such as almost to the color in the RGB image compression rate and image quality, such as gray-level images and showed good.

• Journal of the Korean Society of Radiology
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• v.10 no.4
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• pp.263-270
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• 2016

This paper suggests techniques to enhance coding time which is a problem in traditional fractal compression and to improve fidelity of reconstructed images by determining fractal coefficient through adaptive selection of block approximation formula. First, to reduce coding time, we construct a linear list of domain blocks of which characteristics is given by their luminance and variance and then we control block searching time according to the first permissible threshold value. Next, when employing three-level block partition, if a range block of minimum partition level cannot find a domain block which has a satisfying approximation error, There applied to 24-bpp color image compression and image techniques. The result did not occur a loss in the image quality of the image when using the encoding method, such as almost to the color in the YIQ image compression rate and image quality, such as RGB images and showed good.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.450-452
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• 2012

본 논문에서는 CCD 카메라에 입력된 동영상에서 Random Forest를 이용하여 휴먼 행동을 인식하는 알고리즘을 제안한다. 행동 인식을 위한 특징 벡터 추출을 위해 가장 최근의 N개의 비디오 프레임들을 하나의 액션 볼륨으로 생성하고, 액션 볼륨 내에서 객체 트랙킹 된 영역을 서브 볼륨으로 생성한다. 이후 서브불륨을 $N{\times}N$개의 블록으로 나누고 각 블록에서 HOOF (Histogram of oriented optical flow)를 특징 벡터로 추출한다. 각 휴먼의 행동인식을 위해 사용된 Random Forest 분류기는 걷기, 뛰기, 발차기, 주먹질, 앉기, 쓰러지기, 넘어지기 7개의 행동을 나타내는 클래스로 분류하도록 학습되었으며 Random Forest에 의한 분류결과에 따라 어떤 행동을 취하는지 최종 판단한다.

• Journal of Broadcast Engineering
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• v.2 no.1
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• pp.45-55
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• 1997

In this paper, we propose a fast algorithm with adaptive thresholding for the wavelet transform (WT) based blocking artifact reduction. In the fast algorithm, all processings that are equivalent to the processing in WT domain of the first and second scale are performed in spatial domain. In the adaptive thresholding, the threshold values used to classify the block boundary are selected adaptively according to each input image by using the statistical properties of the WT of the coded signal at block boundary and at block center, which can be obtained in spatial domain. Experimental results showed that the proposed fast algorithm is about 10 times faster than the WT-based algorithm. It also was found that the postprocessing with proposed adaptive thresholding yields some PSNR improvement and better subjective quality over that with nonadaptive thresholding which has best performance at high compression ratios of a certain .image, even at low compression ratios.

• The Journal of Information Technology and Database
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• v.3 no.1
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• pp.85-95
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• 1996

본 논문은 특정문서에서 문서가 갖고 있는 일반적인 지식을 이용하여 논리적 항목을 추출하는 방법에 관한 연구이다. 먼저 입력된 문서의 영역 분할, 분리자 추출, 그리고 문자와 비문자를 구별하였다. 논리구조 추출단계에서는 구별된 요소의 상대적 크기, 위치 및 전후 블록들의 연관성에 관한 지식을 이용하여 각 블록들을 레이블링 하였고, 레이블된 항목들의 위치정보값을 이용하여 각 항목들을 자료화하였다. 마지막으로, 오분류된 항목에 대해서는 배치기술자를 이용한 검증을 통해 정정하였다. 본 논문에서 구현한 방법으로 실험한 결과 96.5%의 논리항목 추출율을 획득함으로써 그 유효성을 입증하였다.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2009.04a
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• pp.259-261
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• 2009

최근에 기업들이 지역별 특성을 고려한 마케팅전략을 수립하기 위해 GIS를 활용하려는 시도가 이어지고 있다. 그러나 GIS 데이터의 특성상 일반 고객들이 손쉽게 접근하기 어려운 상황이다. 따라서 GIS 데이터의 한계를 극복하고 기업의 효율적인 타겟마케팅 업무지원을 위해 본 연구에서는 공간단위를 블록으로 구성하고 그 활용방안을 모색해보는 것을 목적으로 하였다. 블록의 영역을 구분한 후, 특성을 표현하기에 유의미한 인문사회적 요인으로 분류하였다. 이렇게 구성된 블록은 판매 상승 혹은 판매 하락 지역의 특성을 파악할 수 있음을 물론, 이와 유사한 블록을 추출함으로써 잠재적인 상승판매 지역을 설정하여 유형별 특성에 따른 차별화된 마케팅 전략을 수립하는데 활용 될 수 있을 것으로 판단된다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.2
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• pp.28-38
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• 2000

This paper proposes a fractal compression method using the domain classification and local searching, which utilize DCT coefficient characteristic Generally, the fractal Image encoding method has a time consuming process to search a domain to be matched with range block In order to reduce computation complexity, the domain and range regions are respectively classified into 4 category by using the characteristics of DCT coefficients and each range region is encoded by a method suitable for the property of its category Since the bit amount of the compressed image depends on the number of range blocks, the matching of domain block and range block is induced on the large range block by using local search, so that compression ratio is increased by reducing the number of range block In the local search, the searching complexity is reduced by determining the direction and distance of searching using the characteristics of DCT coefficients The experimental results shows that the proposed algorithm have 1 dB higher PSNR and 0 806 higher compression ratio than previous algorithm.

• Korean Journal of Remote Sensing
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• v.32 no.5
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• pp.471-482
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• 2016

This paper presents an extraction method for water body which uses block-based image partitioning and extension of water body boundaries to improve the performance of supervised classification for water body extraction. The Mahalanobis distance image is created by computing the spectral information of Normalized Difference Water Index (NDWI) and Near Infrared (NIR) band images over a training site within the water body in order to extract an initial water body area. To reduce the effect of noise contained in the Mahalanobis distance image, we apply mean curvature diffusion to the image, which controls diffusion coefficients based on connectivity strength between adjacent pixels and then extract the initial water body area. After partitioning the extracted water body image into the non-overlapping blocks of same size, we update the water body area using the information of water body belonging to water body boundaries. The update is performed repeatedly under the condition that the statistical distance between water body area belonging to water body boundaries and the training site is not greater than a threshold value. The accuracy assessment of the proposed algorithm was tested using KOMPSAT-2 images for the various block sizes between $11{\times}11$ and $19{\times}19$. The overall accuracy and Kappa coefficient of the algorithm varied from 99.47% to 99.53% and from 95.07% to 95.80%, respectively.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.4
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• pp.362-370
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• 2001

In this paper, we proposed multispectral image compression method using CIP (classified inter-channel prediction) and SVQ (selective vector quantization) in wavelet domain. First, multispectral image is wavelet transformed and classified into one of three classes considering reflection characteristics of the subband with the lowest resolution. Then, for a reference channel which has the highest correlation and the same resolution with other channels, the variable VQ is performed in the classified intra-channel to remove spatial redundancy. For other channels, the CIP is performed to remove spectral redundancy. Finally, the prediction error is reduced by performing SVQ. Experiments are carried out on a multispectral image. The results show that the proposed method reduce the bit rate at higher reconstructed image quality and improve the compression efficiency compared to conventional methods. Index Terms-Multispectral image compression, wavelet transform, classfied inter-channel prediction, selective vetor quantization, subband with lowest resolution.