• 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 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 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 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.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.11b
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• pp.53-57
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• 1999

모양정보는 사람이 물체를 구분하는 특징 중 하나이며 Zernike moments등의 방법으로 그 특징을 표현한다. 본 논문에서는 기존의 Zernike moment 방법을 수정하여 입력 모양정보를 내부 모양정보와 외부 모양정보로 분리하여 각각의 영역에 대해서 특징을 추출한다. 그리고 두 모양정보의 유사도를 계산하는 과정에서 내부 모양정보와 외부 모양정보의 특징에 각각 다른 가중치를 적용함으로써 사용자의 의도에 가장 적합한 질의 결과를 얻을 수 있는 새로운 기능성을 부여하여 검색의 효율성을 높였다. 실험 결과 기존의 Zernike moments 방법에 비해서 최대 12 %의 성능 향상이 있음을 보였다.

• 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 Korea Institute of Military Science and Technology
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• v.3 no.2
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• pp.88-100
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• 2000

The design of an automatic aircraft recognition system involves two parts. The first part is extraction of invariant features independent of scale, rotation and translation. The second part is determination of optimal decision procedures, which are needed in the classification process. In this research, we extracted invariant aircraft features regardless of size, rotation and translation using Fourier Descriptors and Zernike Moments and classified using neural networks.

• Journal of Electrical Engineering and information Science
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• v.2 no.6
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• pp.146-155
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• 1997

In order to achieve scale- and rotation-invariance in recognizing unoccluded objects in binary images using Zernike moment features, an image of an object has often been normalized first by its zeroth-order moment (ZOM) or area. With elongated objects such as characters, a stroke width varies with the threshold value used, it becomes one or two pixels wider or thinner. The variations of the total area of the character becomes significant when the character is relatively thin with respect to its overall size, and the resulting normalized moment features are no longer reliable. This dilation/erosion effect is more severe when the object is not focused precisely. In this paper, we analyze the ZOM method and propose as a normalization method, the maximum enclosing circle (MEC) centered at the centroid of the character. We compare both the ZOM and MEC methods in their performance through various experiments.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.93-94
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• 2013

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.11a
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• pp.87-89
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• 2018

We proposed an improved reliable SVD-based watermarking scheme resistant to geometric attacks while having high fidelity with no false-positive problem. Principal components of a watermark image are embedded into singular values of LL, LH, HL, and HH sub-bands of a transformed cover image by RDWT(redundant discrete wavelet transform) with optimal scale factors. Each scale factor is generated by trading-off fidelity and robustness using Differential Evolution (DE) algorithm. Zernike Moment (ZM) is used to estimate the geometric distortion and to correct the watermarked image before extracting watermark. The proposed scheme improves fidelity and robustness of existing reliable SVD based watermarking schemes while resisting to geometric attacks.