• Journal of Korea Multimedia Society
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• v.10 no.2
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• pp.179-186
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• 2007

This paper proposes a novel method for face detection method using Zernike moments. To detect the faces in an image, local regions in multiscale sliding windows are classified into face and non-face by a neural network, and input features of the neural network consist of Zernike moments. Feature dimension is reduced as the reconstruction capability of orthogonal moment. In addition, because the magnitude of Zernike moment is invariant to rotation, a tilted human face can be detected. Even so the detection rate of the proposed method about head on face is less than experiments using intensity features, the result of our method about rotated faces is more robust. If the additional compensation and features are utilized, the proposed scheme may be best suited for the later stage of classification.

• Journal of KIISE:Software and Applications
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• v.31 no.5
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• pp.563-569
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• 2004

A set of Zernike moments has been successfully used for object recognition or content-based image retrieval systems. Real time applications using Zernike moments, however, have been limited due to its complicated definition. Conventional methods to compute Zernike moments fast have focused mainly on the radial components of the moments. In this paper, utilizing symmetric/anti-symmetric properties of Zernike basis functions, we propose a fast and efficient method for Zernike moments. By reducing the number of operations to one quarter of the conventional methods in the proposed method, the computation time to generate Zernike basis functions was reduced to about 20% compared with conventional methods. In addition, the amount of memory required for efficient computation of the moments is also reduced to a quarter. We also showed that the algorithm can be extended to compute the similar classes of rotational moments, such as pseudo-Zernike moments, and ART descriptors in same manner.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.4
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• pp.116-123
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• 2009

Feature point matching, which is finding the corresponding points from two images with different viewpoint, has been used in various vision-based applications and the demand for the real-time operation of the matching is increasing these days. This paper presents a real-time feature point matching method by using a local descriptor derived by Zernike moments. From an input image, we find a set of feature points by using an existing fast corner detection algorithm and compute a local descriptor derived by Zernike moments at each feature point. The local descriptor based on Zernike moments represents the properties of the image patch around the feature points efficiently and is robust to rotation and illumination changes. In order to speed up the computation of Zernike moments, we compute the Zernike basis functions with fixed size in advance and store them in lookup tables. The initial matching results are acquired by an Approximate Nearest Neighbor (ANN) method and false matchings are eliminated by a RANSAC algorithm. In the experiments we confirmed that the proposed method matches the feature points in images with various transformations in real-time and outperforms existing methods.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.11b
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• pp.35-40
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• 1999

본 논문은 이산 푸리에 변환(DFT :Discrete Fourier Transform)과 Zernike 모멘트를 사용하여 텍스쳐 영상을 검색하는 방법에 관한 것이다. 본 논문은 DFT를 이용하여 입력 영상의 이동에 관계없이 텍스쳐 영상을 검색할 수 있으며, DFT의 정규화된 에너지 특성을 이용함으로써 스케일 변화에도 관계없는 텍스쳐 영상 검색을 가능하게 한다 또한 이러한 정규화된 에너지 평면에 회전에 불변한 특성을 가진 Zernike 모멘트를 사용함으로써 회전에도 관계없이 텍스쳐 영상을 검색할 수 있다. 결과적으로 제안한 알고리듬을 통하여 인간의 시각적 인식 능력과 같은 이동, 스케일, 회전에 관계없이 텍스쳐를 신속하고 정확하게 검색할 수 있다 본 논문에서는 여러가지 실험영상을 통하여 제안한 알고리듬이 효과적임을 보였다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.11
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• pp.4568-4575
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• 2010

Iris recognition is a biometric technology that uses iris pattern information, which has features of stability, security etc. Because of this reason, it is especially appropriate under certain circumstances of requiring a high security. Recently, using the iris information has a variety uses in the fields of access control and information security. In extracting the iris feature, it is desirable to extract the feature which is invariant to size, lights, rotation. We have easy solutions to the problem of iris size and lights by previous processing but there is still problem of iris feature extract invariant to rotation. In this paper, To improve an awareness ratio and decline in speed for a revision of rotation, it is proposed that the iris recognition method using Zernike Moment and Daubechies Wavelet. At first step, the proposed method groups rotated iris into similar things by statistical feature of Zernike Moment invariant to a rotation, which shortens processing time of iris recognition and looks equal to an established method in the performance of recognition too. therefore, proposed method could confirm the possibility of effective application for large scale iris recognition system.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.10
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• pp.171-180
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• 2013

Generally, when we apply zernike moments value for matching, we can use those moments value obtained from projecting image information under circumscribed circle to zernike basis function. However, the problem is that the power of discrimination can be reduced because hand images include lots of overlapped information due to its special characteristic. On the other hand, when distinguishing hand poses, information in specific area of image information except for overlapped information can increase the power of discrimination. In this paper, in order to solve problems like those, we design R3 ring mask by combining image obtained from R2 ring mask, which can weight information of the power of discrimination and image obtained from R1 ring mask, which eliminate the overlapped information. The moments which are obtained by R3 ring mask decrease operational time by reducing dimension through principle component analysis. In order to confirm the superiority of the suggested method, we conducted some experiments by comparing our method to other method using seven different hand poses.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.205-208
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• 2010

We introduce a novel feature extraction scheme for online handwritten digit based on utilizing Zernike moment and angulation feature. The time sequential signal from mouse movement on the writing pad is described as a sequence of consecutive points on the x-y plane. So, we can create data-set which are successive and time-sequential pixel position data by preprocessing. Data preprocessed is used for Zernike moment and angulation feature extraction. this feature is scale-, translation-, and rotation-invariant. The extracted specific feature is fed to a BP(backpropagation) neural network, which in turn classifies it as one of the nine digits. In this paper, proposed method not noly show high recognition rate but also need less learning data for 200 handwritten digit data.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.2
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• pp.153-162
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• 2012

In this paper, we propose a new shape similarity measure for M:N polygon pairs regardless of different object cardinalities in the pairs. The proposed method compares the projections of two shape functions onto Zernike polynomial basis functions, where the shape functions were obtained from each overall region of objects, thus not being affected by the cardinalities of object pairs. Moments with low-order basis functions describe global shape properties and those with high-order basis functions describe local shape properties. Therefore several moments up to a certain order where the original shapes were similarly reconstructed can efficiently describe the shape properties thus be used for shape comparison. The proposed method was applied for the building objects in the New address digital map and a car navigation map of Seoul area. Comparing to an overlapping ratio method, the proposed method's similarity is more robust to object cardinality.

• Journal of the Korea Society of Computer and Information
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• v.17 no.2
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• pp.31-40
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• 2012

Iris recognition is a biometric technology which can identify a person using the iris pattern. It is important for the iris recognition system to extract the feature which is invariant to changes in iris patterns. Those changes can be occurred by the influence of lights, changes in the size of the pupil, and head tilting. In this paper, we propose a novel method based on Zernike Moment which is robust to rotations of iris patterns. we utilized a selection of Zernike moments for the fast and effective recognition by selecting global optimum moments and local optimum moments for optimal matching of each iris class. The proposed method enables high-speed feature extraction and feature comparison because it requires no additional processing to obtain the rotation invariance, and shows comparable performance to the well-known previous methods.

• Proceedings of the KAIS Fall Conference
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• 2011.05a
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• pp.247-250
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• 2011

컴퓨터의 대중화와 각종 디지털 기기의 보급 및 네트워크의 발달로 거의 모든 정보가 디지털화되어 처리 및 저장되고 있는 추세이다. 하지만 이러한 편리한 측면에도 불구하고 개인 신상 정보 유출 및 피싱, 해킹 등으로 인해 아이디/비밀번호와 같은 기존 방식으로는 강화되고 있는 보안에 대한 요구를 충족시킬 수 없는 것이 현실이다. 홍채 인식은 최근들어 출입통제, 정보보안 등의 분야에 많이 활용되고 있다. 홍채 특징 추출 시 크기, 조명, 회전에 무관한 홍채 특징을 추출하는 것이 바람직하다. 홍채크기 및 조명 문제는 전처리를 통해 쉽게 해결할 수 있지만 회전에 무관한 홍채 특징 추출은 여전히 문제가 된다. 본 논문에서는 회전 보정으로 인한 인식률 및 속도 저하를 개선하기 위해 Zernike 모멘트를 통한 홍채특징 추출방법을 제안하였다.