• Proceedings of the Korean Information Science Society Conference
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• 2010.06c
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• pp.49-52
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• 2010

본 논문에서는 윤곽선 이미지 매칭에서 회전-불변 거리를 계산하는 효율적 방법을 제안한다. 회전-불변 거리 계산은 이미지 시계열을 한 칸씩 회전하면서 매번 유클리디안 거리를 계산해야 하는 고비용의 연산이다. 본 논문에서는 엔빌로프 기반 하한을 사용하여 불필요한 회전-불변 거리 계산을 크게 줄이는 효율적인 해결책을 제시하다. 이를 위해, 먼저 질의 시퀀스 대상의 엔빌로프 작성과 이의 하한 개념을 제시한다.다음으로, 엔빌로프 기반 하한을 회전-불변 거리 계산에 사용하면 많은 수의 회전-불변 거리계산을 줄일 수 있음을 보인다. 실험 결과, 제안한 엔빌로프 기반 매칭 기법은 기존 기법에 비해 최대 수배에서 수십배까지 매칭 성능을 향상시킨 것으로 나타났다.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.10
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• pp.949-954
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• 2010

Computing the rotation-invariant distance between image time-series is a time-consuming process that incurs a lot of Euclidean distances for all possible rotations. In this paper we propose an innovative solution that significantly reduces the number of Euclidean distances using the triangular inequality. To this end, we first present the notion of self rotation distance and show that, by using the self rotation distance with the triangular inequality, we can prune many unnecessary distance computations. We next present that only one self-rotation is enough for all self-rotation distances required. Experimental results show that our self rotation distance-based methods outperform the existing methods by up to an order of magnitude.

• The KIPS Transactions:PartD
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• v.18D no.1
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• pp.9-22
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• 2011

In this paper we present an efficient solution to rotation?invariant boundary image matching. Computing the rotation-invariant distance between image time-series is a time-consuming process since it requires a lot of Euclidean distance computations for all possible rotations. In this paper we propose a novel solution that significantly reduces the number of distance computations using the envelope-based lower bound. To this end, we first present how to construct a single envelope from a query sequence and how to obtain a lower bound of the rotation-invariant distance using the envelope. We then show that the single envelope-based lower bound can reduce a number of distance computations. This approach, however, may cause bad performance since it may incur a larger lower bound by considering all possible rotated sequences in a single envelope. To solve this problem, we present a concept of rotation interval, and using the rotation interval we generalize the envelope-based lower bound by exploiting multiple envelopes rather than a single envelope. We also propose equi-width and envelope minimization divisions as the method of determining rotation intervals in the multiple envelope approach. Experimental results show that our envelope-based solutions outperform existing solutions by one or two orders of magnitude.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.3
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• pp.21-26
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• 2003

A new method to realize 3-dimensional object pattern recognition system using Fourier-based feature extractor has been proposed. The procedure to obtain the invariant feature vector is as follows ; A closed surface is generated by tracing the surface of object using the 3-dimensional polar coordinate. The centroidal distances between object's geometrical center and each closed surface points are calculated. The distance vector is translation invariant. The distance vector is normalized, so the result is scale invariant. The Fourier spectrum of each normalized distance vector is calculated, and the spectrum is rotation invariant. The Fourier-based feature generating from above procedure completely eliminates the effect of variations in translation, scale, and rotation of 3-dimensional object with closed-surface. The experimental results show that the proposed method has a high accuracy.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.473-475
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• 1999

본 논문은 구조적 매칭 접근 방법이 회전과 변이에 얼마나 효과적인가를 보여준다. 이는 지문에서 보여주는 특징적 요소들 (코아, 델타 그리고 분기점) 사이의 거리와 각도들을 이용한다. 실제로 이 접근 방법은 회전과 변이가 허용된 한 입력 지문에 대해서 짧은 시간 내에 간단한 연산만으로도 높은 매칭 성공률을 보여준다. 또한 현 자동화된 지문인식 시스템에서처럼 한 입력지문에 대해서 데이터베이스에서 최종 유력한 지문 10개를 검색하는 것을 목적으로 한다. 표본은 600명의 서로 다른 사람으로부터 채취된 지문을 4가지로 (궁상문, 우제상문, 좌제상문, 와상문) 분류한 각각에 대해서 약 98%의 매칭 성공률을 가진다. 실험은 150MHz, 586 퍼스널 컴퓨터에서 실행되었다.

• The Transactions of the Korea Information Processing Society
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• v.3 no.7
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• pp.1894-1905
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• 1996

Delauuany triangulation which is the dual of Dirichlet tessellation is not affine invariant. In other words, the triangulation is dependent upon the choice of the coordinate axes used to represent the vertices. In the same reason, Delahanty tetrahedrization does not have an affine iveariant transformation property. In this paper, we present a new type of tetrahedrization of spacial points sets which is unaffected by translations, scalings, shearings and rotations. An affine invariant tetrahedrization is discussed as a means of affine invariant 2 -D triangulation extended to three-dimensional tetrahedrization. A new associate norm between two points in 3-D space is defined. The visualization of the structure of tetrahedrization can discriminate between Delaunay tetrahedrization and affine invariant tetrahedrization.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.67-75
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• 2011

This paper presents a scale and rotation-invariant gait recognition method using R-transform, which is computed by projecting squared coefficients of Radon transform. Since R-transform is invariant to translation, rotation, and scaling, it particularly suitable for extracting object poses without camera calibration. Coefficients of R-transform are used to compute correlation, and the maximum correlation value determines the similarity between two gait images. The proposed method requires neither camera calibration nor geometric compensation, and as a result, it makes robust gait recognition possible without additional compensation for translation, rotation, and scaling.

• The KIPS Transactions:PartB
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• v.12B no.5 s.101
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• pp.527-534
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• 2005

Rotation and scale variations make it difficult to solve the problem of shape description and recognition because these variations change the location of points composing the shape. However, some geometric Invariant points and the relations among them are not changed by these variations. Therefore, if points in image space depicted with the r-y coordinates system can be transformed into a new coordinates system that are invariant to rotation and scale, the problem of shape description and recognition becomes easier. This paper presents a shape description method via transformation from the image space into the invariant feature space having two axes: representing relative distance from a centroid and contour segment curvature(CSC). The relative distance describes how far a point departs from the centroid, and the CSC represents the degree of fluctuation in a contour segment. After transformation, mesh features were used to describe the shape mapped onto the feature space. Experimental results show that the proposed method is robust to rotation and scale variations.

• Journal of Korea Multimedia Society
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• v.4 no.3
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• pp.215-221
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• 2001

In this paper, we propose a algorithm that apply different weight-sampling values according to the directions of the contour to reduce errors that can arise in extracting the feature of an contoured object. Especially, it 8is designed to have invariant property under the circumstances like the rotation, transition and scaling. The output matrix of feature set is made through the size function of the proposed algorithm, and the euclidean distance between the output matrix and that of the original image is calculated. Experimental result shows that the proposed algorithm reduces the euclidean distance between the original image and the changed image-by 57% in rotation and by 91% in scaling.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.06b
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• pp.97-102
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• 1999

멀티미디어 데이터의 증가로 사용자가 원하는 데이터의 신속하고 정확한 검색이 필요하게 되었다. 본 논문에서는 모양 정보를 기반으로 영상 데이터를 효과적이며 효율적으로 검색하기 위하여, 새로운 모양 정보 특징 및 검색 방법을 제안한다. 본 논문에서는 화소의 공간적분포로 나타나는 모양 정보를 covariance matrix의 eigenvector를 이용하여, 계층적으로 영역을 분할하고, 각 분할된 영역에서 크기 변화, 위치 이동, 회전에 불변하는 특징들을 추출한다. 영상 정보의 검색은 특징벡터 공간에서 질의 영상에서 추출된 특징과, 데이터베이스에 기록된 영상들의 특징 사이의 거리를 계산하여, 거리에 반비례하는 유사도가 높은 영상들을 출력한다. 제안된 모양 특징은 또한 계층수의 조정에 의해서 모양 정보를 표현할 수 있는 정도를 조절 할 수 있다는 장점이 있다.