• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권7호
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• pp.1690-1704
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• 2013

An affine invariant descriptor is proposed, which is able to well represent the affine covariant regions. Estimating main orientation is still problematic in many existing method, such as SIFT (scale invariant feature transform) and SURF (speeded up robust features). Instead of aligning the estimated main orientation, in this paper ellipse orientation is directly used. According to ellipse orientation, affine covariant regions are firstly divided into 4 sub-regions with equal angles. Since affine covariant regions are divided from the ellipse orientation, the divided sub-regions are rotation invariant regardless the rotation, if any, of ellipse. Meanwhile, the affine covariant regions are normalized into a circular region. In the end, the gradients of pixels in the circular region are calculated and the partition-based descriptor is created by using the gradients. Compared with the existing descriptors including MROGH, SIFT, GLOH, PCA-SIFT and spin images, the proposed descriptor demonstrates superior performance according to extensive experiments.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.358-360
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• 2004

The digital images contain a significant amount of redundancy and require a large amount of data for their storage and transmission. Therefore, the image compression is necessary to treat digital images efficiently. The goal of image compression is to reduce the number of bits required for their representation. The image compression can reduce the size of image data using contractive mapping of original image. Among the compression methods, the mapping is affine transformation to find the block(called range block) which is the most similar to the original image. In this paper, we applied the neural network(SOM) in encoding. In order to improve the performance of image compression, we intend to reduce the similarities and unnecesaries comparing with the originals in the codebook. In standard image coding, the affine transform is performed with eight isometries that used to approximate domain blocks to range blocks.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.517-519
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• 2004

This paper describes an approach to estimate a robot pose with an image. The algorithm of pose estimation with an image can be broken down into three stages : extracting scale-invariant features, matching these features and calculating affine invariant. In the first step, the robot mounted mono camera captures environment image. Then feature extraction is executed in a captured image. These extracted features are recorded in a database. In the matching stage, a Random Sample Consensus(RANSAC) method is employed to match these features. After matching these features, the robot pose is estimated with positions of features by calculating affine invariant. This algorithm is implemented and demonstrated by Matlab program.

• 전기학회논문지
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• 제56권3호
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• pp.615-622
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• 2007

In general, Harris detector is commonly used for finding salient points in watermarking systems using feature points. Harris detector is a kind of combined comer and edge detector which is based on neighboring image data distribution, therefore it has some limitation to find accurate salient points after watermark embedding or any kinds of digital attacks. In this paper, we have used cross reference points which use not data distribution but geometrical structure of a normalized image in order to avoid pointing error caused by the distortion of image data. After normalization, we find cross reference points and take inverse normalization of these points. Next, we construct a group of triangles using tessellation with inversely normalized cross reference points. The watermarks are affine transformed and transformed-watermarks are embedded into not normalized image but original one. Only locations of watermarks are determined on the normalized image. Therefore, we can reduce data loss of watermark which is caused by inverse normalization. As a result, we can detect watermarks with high correlation after several digital attacks.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.260-262
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• 2003

New Image registration techniques are developed for determining geometric distortions between two images of the same scene. First, the properties of the Fourier transform of a two dimensional function under the affine transformation are given. As a result, techniques for the estimation of the coefficients of the distortion model using the spectral frequency information are developed. Image registration can be achieved by applying the fast Fourier transform (FFT) technique for cross correlation of misregistered imagery to determine spatial distances. The correlation results may be rather broad, making detection of the peak difficult, what can be suppressed by enhancing cross-correlation technique. Yield greatly improves the delectability and high precision of image misregistration.

• 대한전자공학회논문지SP
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• 제40권1호
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• pp.124-129
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• 2003

주기적인 워터마크 패턴에 적용할 수 있는 새로운 워터마크의 동기 복원 방법이 제안되었다. 주기적인 워터마크 패턴을 이용해서 워터마크의 동기를 찾는 방법은 널리 알려져 있지만, 직접적으로 동기 복원을 위한 파라미터를 추정하지 않고 대부분이 피크를 검출하는 과정에 의지하고 있어 그 성능에 있어 한계가 있었다. 제안된 동기 복원을 위한 파라미터 추정 방법은 Radon 변환을 이용하여 손실 압축이나 의도적으로 동기 복원을 방해하려는 공격 하에서도 동기를 복원하는 능력이 탁월한 성능을 보였다.

• 전자공학회논문지CI
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• 제48권2호
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• pp.55-65
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• 2011

본 논문은 증강현실 시스템에서 마커가 가려진 상황에서도 강건한 마커 검출을 위하여 지역적인 특징 점들을 이용하는 방법을 제안한다. 가려진 마커를 효율적으로 검출하기 위하여, 첫 번째 단계로 등록된 마커와 가려진 마커가 포함된 입력 영상을 Affine-SIFT (ASIFT, Affine-Scale Invariant Features Transform) 방법을 이용해 정합된 특징 점들을 검출한다. 두 번째 단계로 정합된 특징 점들의 이상치(Outlier)를 제거하기 위하여, 등록된 마커의 특징 점들에 주성분 분석(Principal Component Analysis)을 적용하고 제 1 주축과 제 2 주축으로 사영한 후 중심으로 부터의 거리에 대한 평균값을 타원의 장축과 단축으로 지정한다. 세 번째 단계로 마커의 기하학적인 왜곡을 추정하기 위하여 특징 점들이 이루는 Convex-hull 지점들을 다각형의 꼭짓점으로 정한다. 마지막 단계로, 입력영상에 정합된 특징 점들의 기하적인 왜곡의 변화를 추정함으로써 마커의 가려진 환경에 서도 강건한 마커 검출 결과를 얻을 수 있다.

• 한국ITS학회 논문지
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• 제8권1호
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• pp.127-135
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• 2009

This paper presents a new technique for corner shape based object retrieval from a database. The proposed feature matrix consists of values obtained through a neighborhood operation of detected corners. This results in a significant small size feature matrix compared to the algorithms using color features and thus is computationally very efficient. The corners have been extracted by finding the intersections of the detected lines found using Hough transform. As the affine transformations preserve the co-linearity of points on a line and their intersection properties, the resulting corner features for image retrieval are robust to affine transformations. Furthermore, the corner features are invariant to noise. It is considered that the proposed algorithm will produce good results in combination with other algorithms in a way of incremental verification for similarity.

• 융합신호처리학회 학술대회논문집
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• 한국신호처리시스템학회 2006년도 하계 학술대회 논문집
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• pp.41-44
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• 2006

본 논문은 안면영상의 인증 요소로 사용되는 눈동자영역을 SIFT를 이용하여 추출해내는 방법을 제안하고 있다. 모델이 되는 눈동자영상과 추출 하고자 하는 입력영상의 SET 결과인 Keypoint descriptor를 이용하여 각각의 특징벡터를 구성하고 서로 정합한 후 두 특징 점들 사이에 affine transform이 존재하는지 판단하여 반수 이상에 대응하는 특징 점들에 대해 동일한 affine transform이 존재할 경우 눈동자영역이라고 판단하였다. 실험결과 학습과정이 없으므로 기존의 인식방법 보다 빠르게 영역을 추출하는 결과를 얻을 수 있었다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1998년도 제13차 학술회의논문집
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• pp.221-225
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• 1998

This paper is to present a method for recognizing an image of a tracking object by processing the image from a camera, whose attitude is controlled in inertial space with inertial co-ordinate system. In order to recognize an object, a pseudo-random M-array is attached on the object and it is observed by the camera which is controlled on inertial coordinate basis by inertial stabilization unit. When the attitude of the camera is changed, the observed image of M-array is transformed by use of affine transformation to the image in inertial coordinate system. Taking the cross-correlation function between the affine-transformed image and the original image, we can recognize the object. As parameters of the attitude of the camera, we used the azimuth angle of camera, which is de-fected by gyroscope of an inertial sensor, and elevation an91e of camera which is calculated from the gravitational acceleration detected by servo accelerometer.