• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.171-174
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• 2008

컴퓨터 시뮬레이션을 통한 자연스러운 가상 성형은 컴퓨터 그래픽스와 애니메이션 분야의 오래된 연구 주제이다. 본 논문은 2D 가상 성형 시스템 용도의 가변 워핑 마스크를 이용한 시그모이드 블렌딩 방법을 제안함으로써 가상 성형의 품질 만족도와 편의성 및 실용성을 제고함에 그 목적이 있다. 제안된 2D 가상 성형 시스템은 마우스로 스머지 필터를 조작하여 얼굴 구성 요소의 윤곽 형상을 원하는 형태로 변형할 수 있는 직관적인 사용자 인터페이스를 제공한다. 얼굴 구성 요소의 스머징 전후의 윤곽선을 대상으로 다각형 근사화에 기반한 계층적 제어선 매핑을 통해 획득한 제어선 쌍들을 이용하여 반자동 필드 워핑을 수행함으로써 소스 제어선으로부터 목표 제어선까지 점진적으로 변해가는 다수의 중간 프레임들을 생성한다. 또한 이 반자동 필드 워핑을 수행할 시, 성형 부위의 변형을 따라 단계적으로 모양이 변하는 가변 워핑 마스크를 사용함으로써 변형 부위 이외의 얼굴 구성 요소들에 대해서는 왜곡을 최소화하는 지역적 변형 특성을 제고하고, 이렇게 생성된 성형 부위를 가변 모핑 마스크의 경계 영역에서 시그모이드 함수에 기반한 블렌딩을 수행하는 것이 특징이다. 제안된 2D 가상 성형 시스템은 직관적이고 편리한 사용자 인터페이스를 제공할 수 있기 때문에 시간이 적게 소요되고 작업 피로도가 낮아 실용성이 높다. 특히 짧은 시간 내에 성형의와 고객이 만족하는 직관적인 상담을 가능케 하는 것이 장점이다.

• Proceeding of EDISON Challenge
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• 2017.03a
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• pp.226-235
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• 2017

이 논문에서는 워핑 구속조건이 박벽 구조물의 비틀림 거동에 주는 효과를 확인하였다. Vlasov torsion theory를 통해 얻은 이론해 및 EDISON SW의 해석해를 St.Venent torsion theory를 통해 얻은 이론해와 비교하는 방법으로 그 영향을 확인하였다. 이를 통해 Clamped end조건이 단면의 형상 및 단면의 세부 파라미터에 따라 단면의 워핑발생을 제한하여 비틀림거동에 큰 영향을 미칠 수 있음을 확인했다.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.124-126
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• 2002

본 논문에서는 비디오 데이터가 지니는 이동 객체의 움직임 궤적(moving objects'trajectories)에 대해 유사 부분 움직임 궤적 검색을 효율적으로 지원하는 N-워핑(N-warping) 알고리즘을 제안한다. 제안하는 알고리즘은 기존의 시계열 데이터베이스에서 유사 서브시퀸스 검색을 위해 사용되었던 타임 워핑 변환 기법(time-warping transformation)을 변형란 알고리즘이다. 또한 제안하는 알고리즘은 움직임 궤적을 모델링하기 위해 사용되는 단일 속성(property)인 각도뿐만 아니라, 거리와 시간과 같은 다중 속성을 지원하며, 사용자 질의에 대해 유사 부분 움직임 궤적 검색을 가능하게 하는 근사 매칭(approximate matching)을 지원한다

• Proceedings of the Korean Information Science Society Conference
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• 2001.10a
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• pp.181-183
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• 2001

본 논문에서는 대용량 시퀸스 데이터베이스에서 타임 워핑을 지원하는 인텍스 기반 서브시퀸스 매칭에 관하여 논의한다. 타임 워핑은 시퀸스의 길이가 서로 다른 경우에도 유사한 패턴을 갖는 시퀸스들을 찾을 수 있도록 해 준다. 최근의 연구에서 타임 워핑을 지원하는 효과적인 전체 매칭 기법이 제안된 바 있다. 본 연구에서는 이 기존의 연구에 슬라이딩 윈도우 개념을 결합하는 새로운 기법을 제안한다. 인덱싱을 위하여, 각 슬라이딩 윈도우와 대응되는 서브시퀸스로부터 특징 벡터를 추출하고, 이 특징 벡터를 인덱싱 애트리뷰트로 사용하는 다차원 인덱스를 구성한다. 질의 처리를 위하여, 조건을 만족하는 질의 접두어들에 대한 특징 벡터들을 이용하여 인덱스 검색을 수행한다. 제안된 기법은 대용량의 데이터베이스에서도 효과적인 서브시퀸스 매칭을 지원한다. 본 연구에서는 제안된 기법이 착오 기각을 유발시키지 않음을 증명하고, 실험을 통하여 제안된 기법의 우수성을 규명한다.

• The Journal of the Acoustical Society of Korea
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• v.22 no.4
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• pp.299-307
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• 2003

There have been many studies on speaker normalization which aims to minimize the effects of speaker's vocal tract length on the recognition performance of the speaker independent speech recognition system. In this paper, we propose a simple vector quantizer based linear warping speaker normalization method based on the observation that the vector quantizer can be successfully used for speaker verification. For this purpose, we firstly generate an optimal codebook which will be used as the basis of the speaker normalization, and then the warping factor of the unknown speaker will be extracted by comparing the feature vectors and the codebook. Finally, the extracted warping factor is used to linearly warp the Mel scale filter bank adopted in the course of MFCC calculation. To test the performance of the proposed method, a series of recognition experiments are conducted on discrete HMM with thirteen mono-syllabic Korean number utterances. The results showed that about 29% of word error rate can be reduced, and that the proposed warping factor extraction method is useful due to its simplicity compared to other line search warping methods.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.5 no.6
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• pp.504-510
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• 2004

The objective of this paper is to propose the image interpolation method with pseudomedian filter for Field warping and morphing, and to evaluate and analyze its subjective image quality. The Field warping relatively gives rise to more computing overhead, but it can use the control line to control the warping result with more elaboration. Due to the working characteristics of the image warping and morphing process, various complex geometrical transformations occur and a image interpolation technique is needed to effectively process them. Of the various interpolation techniques, bilinear interpolation which shows above average performance is the most widely used. However, this technology has its limits in the reconstructivity of diagonal edges. The proposed interpolation method is to efficiently combine the bilinear interpolation and the pseudomedian filter-based interpolation which shows good performance in the reconstructivity of diagonal edges. According to the proposed interpolation method, we could get more natural warping and morphing results than other interpolation methods.

• Journal of the Korea Society of Computer and Information
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• v.15 no.7
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• pp.19-26
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• 2010

In this paper, we suggest a efficient handwritten character verification method in on-line environments which automatically analyses two input character string and computes their similarity degrees. The proposed algorithm first applies the circular projection method to input handwritten strings and extracts their representative features including shape, directions, etc. It then calculates the similarity between two character strings by using an improved dynamic time warping (DTW) algorithm. We improved the conventional DTW algorithm efficiently through adopting the branch-and-bound policy to the existing DTW algorithm which is well-known to produce good results in the various optimization problems. The experimental results to verify the performance of the proposed system show that the suggested handwritten character verification method operates more efficiently than the existing DTW and DDTW algorithms in terms of the speed.

• The KIPS Transactions:PartD
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• v.11D no.7 s.96
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• pp.1385-1398
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• 2004

This paper discusses an effective processing of subsequence matching under time warping in time-series databases. Time warping is a trans-formation that enables finding of sequences with similar patterns even when they are of different lengths. Through a preliminary experiment, we first point out that the performance bottleneck of Naive-Scan, a basic method for processing of subsequence matching under time warping, is on the CPU processing step. Then, we propose a novel method that optimizes the CPU processing step of Naive-Scan. The proposed method maximizes the CPU performance by eliminating all the redundant calculations occurring in computing the time warping distance between the query sequence and data subsequences. We formally prove the proposed method does not incur false dismissals and also is the optimal one for processing Naive-Scan. Also, we discuss the we discuss to apply the proposed method to the post-processing step of LB-Scan and ST-Filter, the previous methods for processing of subsequence matching under time warping. Then, we quantitatively verify the performance improvement ef-fects obtained by the proposed method via extensive experiments. The result shows that the performance of all the three previous methods im-proves by employing the proposed method. Especially, Naive-Scan, which is known to show the worst performance, performs much better than LB-Scan as well as ST-Filter in all cases when it employs the proposed method for CPU processing. This result is so meaningful in that the performance inversion among Nive- Scan, LB-Scan, and ST-Filter has occurred by optimizing the CPU processing step, which is their perform-ance bottleneck.

• The KIPS Transactions:PartD
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• v.9D no.2
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• pp.173-184
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• 2002

This paper discuss an index-based subsequence matching that supports time warping in large sequence databases. Time warping enables finding sequences with similar patterns even when they are of different lengths. In earlier work, Kim et al. suggested an efficient method for whole matching under time warping. This method constructs a multidimensional index on a set of feature vectors, which are invariant to time warping, from data sequences. For filtering at feature space, it also applies a lower-bound function, which consistently underestimates the time warping distance as well as satisfies the triangular inequality. In this paper, we incorporate the prefix-querying approach based on sliding windows into the earlier approach. For indexing, we extract a feature vector from every subsequence inside a sliding window and construct a multidimensional index using a feature vector as indexing attributes. For query processing, we perform a series of index searches using the feature vectors of qualifying query prefixes. Our approach provides effective and scalable subsequence matching even with a large volume of a database. We also prove that our approach does not incur false dismissal. To verify the superiority of our approach, we perform extensive experiments. The results reveal that our approach achieves significant speedup with real-world S&P 500 stock data and with very large synthetic data.

• Journal of KIISE:Databases
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• v.28 no.4
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• pp.643-654
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• 2001

This paper discusses an effective processing of similarity search that supports time warping in large sequence database. Time warping enables finding sequences with similar patterns even when they are of different length, Previous methods fail to employ multi-dimensional indexes without false dismissal since the time warping distance does not satisfy the triangular inequality. They have to scan all the database, thus suffer from serious performance degradation in large database. Another method that hires the suffix tree also shows poor performance due to the large tree size. In this paper we propose a new novel method for similarity search that supports time warping Our primary goal is to innovate on search performance in large database without false dismissal. to attain this goal ,we devise a new distance function $D_{tw-Ib}$ consistently underestimates the time warping distance and also satisfies the triangular inequality, $D_{tw-Ib}$ uses a 4-tuple feature vector extracted from each sequence and is invariant to time warping, For efficient processing, we employ a distance function, We prove that our method does not incur false dismissal. To verify the superiority of our method, we perform extensive experiments . The results reveal that our method achieves significant speedup up to 43 times with real-world S&P 500 stock data and up to 720 times with very large synthetic data.