• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.9
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• pp.736-743
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• 2013

In IRST(infrared search and track) system, the small target detection is very difficult because the IR(infrared) image have various clutter and sensor noise. The noise and clutter similar to the target intensity value produce many false alarms. In this paper. We propose the adaptive detection method which obtains optimal target detection using the image intensity information and the prior information of target. In order to enhance the target, we apply the human visual system. we determine the adaptive threshold value using image intensity and distance measure in target enhancement image. The experimental results indicate that the proposed method can efficiently extract target region in various IR images.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.1
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• pp.667-675
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• 2009

Image matching is widely applied in image application areas, such as remote sensing and GIS. In general, the initial set of matching points always includes outlier which affect the accuracy of image matching. The purpose of this paper is to develop a robust approach for outlier detection and removal in order to keep accuracy in image matching applications. In this paper we use three automatic outlier detection techniques of backward matching and affine transformation, and RANSAC(RANdom SAmple Consensus) algorithm. Moreover, we calculate overlapping apply and steps block-based processing for fast and efficient image matching in pre-processing steps. The suggested approach in this paper has been applied to real frame image pairs and the results have been analyzed in terms of the robustness and the efficiency.

• The Journal of the Korea Contents Association
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• v.14 no.2
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• pp.1-9
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• 2014

Image inpainting is the widely used technique to restore a damaged region or to fill a hole in an image. The exemplar-based technique effectively generates new texture by copying colour values of the most correlated patch in the source into the empty region of the current patch. In traditional exemplar-based synthesis, the patch correlation is computed using only the already filled pixels of the current patch. Thus, by ignoring the correlation between the hole regions of the two patches, an undesirable patch which is highly correlated with the current patch in the already filled area but considerably dissimilar in the area to be filled can be selected, which results in bad texture propagation. To avoid such problems, a new exemplar-based inpainting method using patch extrapolation is proposed. The empty part of the current patch is extrapolated beforehand, and then the complete patch is used for finding its exemplar. Experimental results show that the proposed method provides more natural synthesis results than the conventional ones.

• The Transactions of the Korea Information Processing Society
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• v.4 no.1
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• pp.11-22
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• 1997

The fractal image compression partitions an original image into blocks of equal size and searches a do-main block having self-similarity. This method of compression achieves high compression ratio because it is unnecessary to transmit the additional codebook to receiver and it provides good quality of reconstructed images. In spite of these advantages, this method has a drawback in which encoding time increase due to a complicated linear transformation for determining a similar-domain block. In this paper, a fast fractal image compression method is proposed by decreasing the number of transformation usings AC(alternating current) coefficients of block. The proposed method also has a good quality as compared with the well-known fractal codings. Furthermore, method also has a good quality as apply the video steganography that can conceal an important secret data.

• 대학교육
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• s.90
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• pp.47-60
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• 1997

대학은 획일화되어 있는 대학입학 사정 방법에 준하여 우수 학생을 유치하려는 지엽적.한시적인 정책 수립 안목에서 벗어나 21세기를 대비한 우수한 교육과정과 프로그램 개발 그리고 그에 부합하는 신입생을 선발하는 장기적이며 미래지향적인 계획을 수립하여야 한다. 이러할 때 대학이나 학과의 특성을 드러낼 수 있으며, 타대학 혹은 타학과와 경쟁하지 않고 자체가 경쟁의 대상이 되어 대학이나 학과의 발전을 가져온다. 여기에서 미국 대학입학제도의 다양성을 참고함은 유사한 대학입학제도에 따른 우리나라 교육의 문제점을 해결할 수 있는 시사점을 제공받을 수 있기 때문이다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.06a
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• pp.280-283
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• 2016

본 논문에서는 DIBR 3 차원 영상을 위한 블라인드 워터마크 방법을 제안한다. DIBR 을 이용한 3 차원 영상은 낮은 Bitrate 와 시점조정 가능성으로 인해 주목받고 있지만, DIBR 과정 중 워터마크 손상이 발생해 워터마크에 어려움이 있다. 이를 극복하기 위해 DIBR 의 특성을 이용한 깊이영상 기반으로 워터마크 삽입 영역을 탐색하는 방법을 사용하며, 이를 통해 시점이동에 민감하지 않은 영역에 워터마크를 삽입하는 것이 효과적임을 실험적으로 증명하였다. 또한 시점이동 후 사라질 가능성이 큰 불필요한 워터마크 삽입을 최소화하여 워터마크 삽입 후 영상의 화질저하를 줄였다. 이에 본 논문에서는 깊이영상을 이용해 변화가 적은 영역을 탐색하기 위한 연구를 진행하였다. 이는 후에 가상 시점 영상에 추가적인 공격이 가해지더라도 더 강인성을 가질 수 있는 기초가 되었다. 또한 블라인드 워터마크 방법을 적용하기 위해 기존 워터마크 데이터와 유사도를 측정하는 방식으로 워터마크 삽입 위치를 파악하였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.123-126
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• 2020

딥러닝이 정답을 찾아가는 연구과정이라면 미술은 정답이나 오답의 단정적 결과보다는 미추(아름다움과 추함)를 포함하는 과정적, 창조적 행위에 가깝다고 할 수 있다. 다시 말하면 미술은 0과 1로만 환원할 수 없는 세계를 기술하여 감동을 주는 유기적 규칙이 내재되어 있고 때로는 과학이 만들어낸 결론을 뒤집는 반상식적 추론을 하기도 한다. 그러므로 딥러닝은 예술적 방식을 통하여 과학의 상식적 추론과의 좋은 거리(Fine distance)를 유지할 필요성이 있는데, 이를 위해서 기존 딥러닝의 이미지 생성과 관련하여 Distance, Classification, Optimization 등의 문제를 미술 표현 기법과 목적이 담겨있는 창작자의 Statement 키워드와의 유사성과 차이점을 비교 분석할 필요가 있다고 생각한다. 시각적 표현과 관련된 딥러닝의 성능은 아직 사람의 표현능력에 못 미치고 있어 본 논문에서는 콜라주 기법에 의한 비디오 생성을 위한 탐색적 실험 분석을 목적으로 GAN을 활용한 콜라주 비디오를 제작하고 그 문제점과 개선점을 제안하고자 한다.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.7
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• pp.925-934
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• 2017

R-tree is index structure which is frequently used for handling spatial data. However, if the number of dimensions increases, or if only partial dimensions are used for searching the certain data according to user preference, the time for indexing is greatly increased and the efficiency of the generated R-tree is greatly reduced. Hence, it is not suitable for the multidimensional data, where dimensions are continuously increasing. In this paper, we propose a multidimensional hash index, a new multidimensional index structure based on a hash index. The multidimensional hash index classifies data into buckets of euclidean space through a hash function, and then, when an actual search is requested, generates a hash search tree for effective searching. The generated hash search tree is able to handle user preferences in selected dimensional space. Experimental results show that the proposed method has better indexing performance than R-tree, while maintaining the similar search performance.

• Korean Journal of Cognitive Science
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• v.32 no.3
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• pp.117-139
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• 2021

The way of change detection in which presence of a different item is determined between memory and test arrays with a brief in-between time interval resembles how visual search is done considering that the different item is searched upon the onset of a test array being compared against the items in memory. According to the resemblance, the present study examined whether varying the probability of change occurrence in a visual short-term memory-based change detection task can influence the aspect of response-decision making (i.e., change prevalence effect). The simple-feature change detection task in the study consisted of a set of four colored boxes followed by another set of four colored boxes between which the participants determined presence or absence of a color change from one box to the other. The change prevalence was varied to 20, 50, or 80% in terms of change occurrences in total trials, and their change detection errors, detection sensitivity, and their subsequent RTs were analyzed. The analyses revealed that as the change prevalence increased, false alarms became more frequent while misses became less frequent, along with delayed correct-rejection responses. The observed change prevalence effect looks very similar to the target prevalence effect varying according to probability of target occurrence in visual search tasks, indicating that the background principles deriving these two effects may resemble each other.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.3
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• pp.171-180
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• 2003

A content-based image retrieval(CBIR) system builds the image database using low-level features such as color, shape and texture and provides similar images that user wants to retrieve when the retrieval request occurs. What the user is interest in is a response time in consideration of the building time to build the index database and the response time to obtain the retrieval results from the query image. In a content-based image retrieval system, the similarity computing time comparing a query with images in database takes the most time in whole response time. In this paper, we propose the two-phase search method with the clustering technique of feature vector in order to minimize the similarity computing time. Experimental results show that this two-phase search method is 2-times faster than the conventional full-search method using original features of ail images in image database, while maintaining the same retrieval relevance as the conventional full-search method. And the proposed method is more effective as the number of images increases.