• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2021.06a
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• pp.89-90
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• 2021

본 논문에서는 3D(dimensional) 스켈레톤을 이용하여 멀티 뷰 RGB-D 카메라를 캘리브레이션 하는 새로운 기법을 제안하고자 한다. 멀티 뷰 카메라를 캘리브레이션 하기 위해서는 일관성 있는 특징점이 필요하다. 우리는 다시점 카메라를 캘리브레이션 하기 위한 특징점으로 사람의 스켈레톤을 사용한다. 사람의 스켈레톤은 최신의 자세 추정(pose estimation) 알고리즘들을 이용하여 쉽게 구할 수 있게 되었다. 우리는 자세 추정 알고리즘을 통해서 획득된 3D 스켈레톤의 관절 좌표를 특징점으로 사용하는 RGB-D 기반의 캘리브레이션 알고리즘을 제안한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.08a
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• pp.73-76
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• 2008

We present a area-based method for stereo matching using varying weights. A central problem in a area-based stereo matching is different result from selecting a window size. Most of the previous window-based methods iteratively update windows. However, the iterative methods very sensitive the initial disparity estimation and are computationally expensive. To resolve this problem, we proposed a new function to assign weights to pixels using features. To begin with, we extract features in a given stereo images based on edge. We adjust the weights of the pixels in a given window based on correlation of the stereo images. Then, we match pixels in a given window between the reference and target images of a stereo pair. The proposed method is compared to existing matching strategies using both synthetic and real images. The experimental results show the improved accuracy of the proposed method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.2
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• pp.89-98
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• 2009

In the field of computer vision, image mosaicking is a common method for effectively increasing restricted the field of view of a camera by combining a set of separate images into a single seamless image. Image mosaicking based on feature points has recently been a focus of research because of simple estimation for geometric transformation regardless distortions and differences of intensity generating by motion of a camera in consecutive images. Yet, since most feature-point matching algorithms extract feature points using gray values, identifying corresponding points becomes difficult in the case of changing illumination and images with a similar intensity. Accordingly, to solve these problems, this paper proposes a method of image mosaicking based on feature points using color information of images. Essentially, the digital values acquired from a digital color camera are converted to values of a virtual camera with distinct narrow bands. Values based on the surface reflectance and invariant to the chromaticity of various illuminations are then derived from the virtual camera values and defined as color-invariant values invariant to changing illuminations. The validity of these color-invariant values is verified in a test using a Macbeth Color-Checker under simulated illuminations. The test also compares the proposed method using the color-invariant values with the conventional SIFT algorithm. The accuracy of the matching between the feature points extracted using the proposed method is increased, while image mosaicking using color information is also achieved.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1257-1271
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• 2022

Recently, many Earth observation optical satellites have been developed, as their demands were increasing. Therefore, a rapid preprocessing of satellites became one of the most important problem for an active utilization of satellite images. Satellite image matching is a technique in which two images are transformed and represented in one specific coordinate system. This technique is used for aligning different bands or correcting of relative positions error between two satellite images. In this paper, we propose an automatic image matching method among satellite images with different ground sampling distances (GSDs). Our method is based on improved feature matching and robust estimation of transformation between satellite images. The proposed method consists of five processes: calculation of overlapping area, improved feature detection, feature matching, robust estimation of transformation, and image resampling. For feature detection, we extract overlapping areas and resample them to equalize their GSDs. For feature matching, we used Oriented FAST and rotated BRIEF (ORB) to improve matching performance. We performed image registration experiments with images KOMPSAT-3A and RapidEye. The performance verification of the proposed method was checked in qualitative and quantitative methods. The reprojection errors of image matching were in the range of 1.277 to 1.608 pixels accuracy with respect to the GSD of RapidEye images. Finally, we confirmed the possibility of satellite image matching with heterogeneous GSDs through the proposed method.

• Korean Journal of Cognitive Science
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• v.14 no.1
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• pp.10-10
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• 2003

This paper extracts the edge of main components of face with Gabor wavelets transformation in facial expression images. FCM(Fuzzy C-Means) clustering algorithm then extracts the representative feature points of low dimensionality from the edge extracted in neutral face. The feature-points of the neutral face is used as a template to extract the feature-points of facial expression images. To match point to Point feature points on an expression face against each feature point on a neutral face, it consists of two steps using a dynamic linking model, which are called the coarse mapping and the fine mapping. This paper presents an automatic extraction of feature-points by dynamic linking model based on Gabor wavelets and fuzzy C-means(FCM) algorithm. The result of this study was applied to extract features automatically in facial expression recognition based on dimension[1].

• Proceedings of the Korea Society for Simulation Conference
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• 2000.11a
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• pp.180-185
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• 2000

방사선 치료계획이나 사전수술계획 등에 컴퓨터 사용이 늘어남에 따라 의료영상별 특성에 따른 복합적 처리를 필요로 한다. 본 논문에서는 다중 모달리티 영상으로부터 의미 있는 정보를 제공하기 위하여 상호정보 최적화를 통한 영상정합 방법을 제안한다. 본 방법은 두 영상에서 대응되는 위치의 명암도간 통계적 의존관계와 정보중복성을 계산하는 상호정보(mutual information)를 통해 영상간 변형관계를 추정함으로써 영상을 정합한다. 실험결과로는 뇌 자기공명영상(MRI)과 컴퓨터단층촬영영상(CT)의 상호정보를 최적화하여 정합 결과를 제시한다. 본 방법은 기존 정합방법에서 사용하는 영상분할이나 특징점 추출 등의 전처리 과정 없이 영상 자체 정보를 기반으로 계산함으로써 정합의 정확도를 높일 수 있다.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.05c
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• pp.371-376
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• 2002

본 논문은 영역 분할에 기반한 스테레오 정합에 있어서, 분할영역 내부에 존재하는 단계적인 변위의 변화를 추정할 수 있는 스테레오 정합 알고리즘에 관한 것이다. 우선, 분할영역을 효과적으로 표현할 수 있는 복수의 샘플점들을 선정한 다음에 각 샘플점 주위에 인접한 영역 내부의 미소영역을 취하여 스테레오 정함을 수행한다. 이후, 획득된 각 샘플점들의 변위를 이용하여 평면의 방정식을 통해 내부 변위를 보간함으로써 연산 시간을 감축함과 동시에 영역 내부의 단계적인 변위의 변화를 추정할 순 있다. 제안된 방법에 따르면, 분할된 영역을 사용함으로써 분할영역 자체가 구속 조건이 되어 특징 기반 기법들의 단점인 변위 보간의 문제점을 해결할 수 있다. 특히, 복수의 샘플점들 간의 변위차를 이용함으로써 영상 평면에 대해 깊이 방향으로 기울어진 영역 평면에 대한 변위의 기울기를 보간할 수 있음에 기인하여 조밀한 변위 맵을 얻을 수 있었다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.4C
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• pp.215-222
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• 2005

In this paper, we propose a fingerprint verification algorithm for the embedded system based on the minutia extracted using the image quality, the minutia structure, and the Sequency and the orientation of ridges. After the pre- and the post-processing, the true minutia are selected, thus it shows high reliability in the fingerprint verification. In matching process, we consider the errors caused by shift, rotation, and pressure when acquiring the fingerprint image and reduce the matching time by applying a local matching instead of a full matching to select the reference pair. The proposed algorithm has been designed and verified in Arm920T environment and various techniques for the realtime process have been applied. Time taken from the fingerprint registration through out the matching is 0.541 second that is relevant for the realtime applications. The FRR (False Reject Rate) and FAR (False Accept Rate) show 0.079 and 0.00005 respectively.

• Journal of the Korea Society of Computer and Information
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• v.10 no.4 s.36
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• pp.203-211
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• 2005

In this paper, we propose a new automatic fingerprint identification system that identifies individuals in large databases. The algorithm consists of three steps; preprocessing, classification, and matching, in the classification. we present a new classification technique based on the statistical approach for directional image distribution. In matching, we also describe improved minutiae candidate pair extraction algorithm that is faster and more accurate than existing algorithm. In matching stage, we extract fingerprint minutiaes from its thinned image for accuracy, and introduce matching process using minutiae linking information. Introduction of linking information into the minutiae matching process is a simple but accurate way, which solves the problem of reference minutiae pair selection in comparison stage of two fingerprints quickly. This algorithm is invariant to translation and rotation of fingerprint. The proposed system was tested on 1000 fingerprint images from the semiconductor chip style scanner. Experimental results reveal false acceptance rate is decreased and genuine acceptance rate is increased than existing method.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2014.11a
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• pp.242-244
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• 2014

파노라마 영상은 하나의 영상이 가지는 제한된 시점의 한계를 극복하고 폭넓은 시야를 가질 수 있다는 점에서 최근 여러 분야에서 활용되고 있는 기술이다. 본 논문에서는 자연스러운 파노라마 영상 생성을 위해 SURF(speed up robust feature)를 이용한 특징점 기반의 파노라마 영상 생성 기법을 제안한다. SURF 알고리즘을 사용하면 정합할 두 영상에서 특징점들을 추출할 수 있다. 추출된 특징점들을 RANSAC(random sample consensus) 알고리즘을 통해 특징점 간 정합시 오차율을 최소화한다. 또한, 이미지 왜곡을 최소화하기 위해 실린더 투영을 이용하여 영상을 보정한다. 최종적으로, 서로 다른 두 영상을 합성할 때 발생하는 경계 주변의 이질감을 보완하기 위해 블렌딩 기법을 사용함으로써 자연스러운 파노라마 영상을 생성한다.