• Journal of KIISE:Software and Applications
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• v.28 no.10
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• pp.764-772
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• 2001

This paper describes fingerprint matching algorithm using connected information between minutiae. We regard minutiae as ridge bifurcation and ridge ending. Features are composed of minutia's position, type(ridge bifurcation or ridge ending) ridge direction and connected ridge information. While the minutiae are extracted, we store connected in information between minutiae on the same ridge. They are used to find corresponding point pairs. Minutiae are aligned completely by two corresponding point pairs and point pattern matching is achieved by counting the number of overlapping pairs. It is invariable t translation and rotation. We have tested proposed method on the 445 fingerprints from 89 persons. These experimental results show that proposed algorithm improve 33% in speed.

• The KIPS Transactions:PartB
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• v.17B no.5
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• pp.345-354
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• 2010

Visual tracking techniques for Augmented Reality are classified as either a marker tracking approach or a natural feature tracking approach. Marker-based tracking algorithms can be efficiently implemented sufficient to work in real-time on mobile devices. On the other hand, natural feature tracking methods require a lot of computationally expensive procedures. Most previous natural feature tracking methods include heavy feature extraction and pattern matching procedures for each of the input image frame. It is difficult to implement real-time augmented reality applications including the capability of natural feature tracking on low performance devices. The required computational time cost is also in proportion to the number of patterns to be matched. To speed up the natural feature tracking process, we propose a novel fast tracking method based on optical flow. We implemented the proposed method on mobile devices to run in real-time and be appropriately used with mobile augmented reality applications. Moreover, during tracking, we keep up the total number of feature points by inserting new feature points proportional to the number of vanished feature points. Experimental results showed that the proposed method reduces the computational cost and also stabilizes the camera pose estimation results.

• Journal of Advanced Navigation Technology
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• v.13 no.1
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• pp.126-133
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• 2009

In this paper, we propose an object recognition technique for implementing marker less augmented reality. Scale Invariant Feature Transform (SIFT) is used for finding the local features from object images. These features are invariant to scale, rotation, translation, and partially invariant to illumination changes. Extracted Features are distinct and have matched with different image features in the scene. If the trained image is properly matched, then it is expected to find object in scene. In this paper, an object is found from a scene by matching the template images that can be generated from the first frame of the scene. Experimental results of object recognition for 4 kinds of objects showed that the proposed technique has a good performance.

• Proceedings of the KIEE Conference
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• 2008.04a
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• pp.105-106
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• 2008

영상을 이용한 물체인식은 컴퓨터 비젼분야의 주요한 관심분야중 하나이다. 이중 특정기반 물체인식은 영상이 가지고 있는 특징점을 이용하는 방법으로 입력영상과 물체에 대한 질의 영상의 특징점을 검출하고 매칭을 수행하여 물체를 인식하게 된다. 특징점은 스케일, 회전, 어파인 변화 등에 변하지 않는 특징을 가지고 있는 점을 말한다. 이러한 특징점을 구하기 위하여 사용하는 방범으로는 SIFT(Scale Invariant Feature Transform)가 있다. SIFT는 스케일, 회선, 어파인 변화에 우수한 성능을 보여주기는 하나 많은 연산으로 인하여 처리속도가 느리다는 단점이 존재한다. 이에 본 논문에서는 SIFT를 사용한 특징기반 물체인식에서 속도 개선 방법에 대하여 제안하였다. 제안한 방법을 사용하였을 경우 물체인식을 위한 특징점을 검출하고 매칭을 수행하는데 소모된 시간이 줄어드는 것을 실험을 통하여 확인 하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.256-257
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• 2022

In this paper, we introduce a method of extracting three-dimensional feature points through the movement of a single mobile device. Using a monocular camera, a 2D image is acquired according to the camera movement and a baseline is estimated. Perform stereo matching based on feature points. A feature point and a descriptor are acquired, and the feature point is matched. Using the matched feature points, the disparity is calculated and a depth value is generated. The 3D feature point is updated according to the camera movement. Finally, the feature point is reset at the time of scene change by using scene change detection. Through the above process, an average of 73.5% of additional storage space can be secured in the key point database. By applying the algorithm proposed to the depth ground truth value of the TUM Dataset and the RGB image, it was confirmed that the\re was an average distance difference of 26.88mm compared with the 3D feature point result.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.169-172
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• 2003

본 논문에서는 기존의 분절 단위 비교방법에서 참조서명과 비교서명간에 분할점 개수와 위치에 대한 불일치 문제를 개선하기 위해 구간 분할 매칭 방법을 제안한다. 제안된 분할방법은 시간에 대한 x와 y좌표 상에서 서명마다 변하지 않는 특징점을 구간 분할점으로 선택하여 구간별로 매칭시킴으로서 분절의 안정적인 분할을 통해 인식률을 높이고자 한다. 실험 결과에서 진서명과 위조서명을 포함한 기타서명에 대해 FAR이 0.06%일 때 FRR 1.25%의 오류율을 확인하였으며 FAR이 0%일 때의 평균인식율이 98.7%를 보임으로써 제안한 서명 검증 기법이 우수함을 확인 할 수 있었다.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.168-173
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• 2012

This paper presents a new human identification algorithm using partial features of the uncovered portion of face when a person wears a mask. After the face area is detected, the feature is extracted from the eye area above the mask. The identification process is performed by comparing the acquired one with the registered features. For extracting features SIFT(scale invariant feature transform) algorithm is used. The extracted features are independent of brightness and size- and rotation-invariant for the image. The experiment results show the effectiveness of the suggested algorithm.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.322-324
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• 2020

본 논문에서는 스테레오 영상으로부터 얻은 특징점들을 활용하여 기초행렬(Fundamental matrix)을 추정하는 실험을 한다. 획득한 영상들은 보정이 되어 있으며, 특징점 추출 후 매칭은 RANSAC 등의 기존 알고리즘을 활용한다. 기초 행렬을 얻기 위해 스테레오 영상으로부터 정의되는 에피폴라 점, 에피폴라 선, 에피폴라 평면을 정의하고, 이들로부터 얻을 수 있는 기하학적 관계식을 활용하여 기초행렬을 수학적으로 추정해 보고, 실험으로 수학적 이론을 검증해 본다.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.488-490
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• 2011

본 논문에서는 문서 영상에서 글자 분할 및 인식이 필요 없는 단어 검색 알고리즘을 제안한다. 글자 분할을 하지 않고 검색하기 위해 영상 검색에 사용되는 SIFT특징을 이용하였다. 제안하는 알고리즘은 사용자가 입력한 질의어를 질의 영상으로 변환하고, 질의 영상에서 SIFT특징을 추출한다. 추출된 특징은 문서영상에서 추출한 특징과 매칭을 통해 매칭점 쌍을 생성한다. 생성된 매칭점 쌍들을 군집화 조건에 따라 군집화 한다. 군집화는 질의 영상과 지리적 분포가 유사하게 군집화 되도록 설계되었다. 생성된 군집은 군집에 포함된 특징점의 개수가 많을수록 질의 영상과 유사하다. 따라서 N개 이상의 원소를 가지는 군집을 결과로 출력한다. 실험한 결과 제안하는 알고리즘의 가능성을 확인할 수 있었다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.173-179
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• 2008

This paper proposes an efficient 1:N matching scheme for fingerprint identification. Usually, in the minutiae-based matching scheme, fingerprint matching score could be calculated by analyzing geometrical similarity between minutiae from two fingerprints. To calculate the geometrical similarity between them, it is necessary to fingerprint align a fingerprint data with the other one. The final matching score is obtained by bidirectional matching in the common fingerprint matching scheme, because the similarity between two fingerprints varies with the result of alignments. The reliability of matching score by the bidirectional matching is better than by the unidirectional matching, but it takes two times comparing with unidirectional matching. To solve the problem, this paper proposes an efficient 1:N fingerprint matching scheme based on the distribution of bidirectional matching scores for the large fingerprints database. The experimental result shows the usefulness of the proposed scheme.