• 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.

• 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 the Korean Institute of Telematics and Electronics A
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• v.28A no.9
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• pp.662-670
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• 1991

Fast analysis method for calculating the radiation pattern of offset dual reflector antenna are presented. The validity of proposed method was verified by comparing with results of PO/PO, GO/PO, and the equivalent paraboloid method. Proposed method is the series expansion method using the Zernike polynomials. The calculated results by using the Zernike polynomials are in good agreement with obtained results by GO/PO and equivalent paraboloid method except PO/PO.

• Current Optics and Photonics
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• v.8 no.2
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• pp.151-155
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• 2024

Optical systems often suffer from optical aberrations caused by imperfect hardware, which places significant constraints on their utility and performance. To reduce these undesirable effects, a comprehensive understanding of the aberrations inherent to optical systems is needed. This article presents an effective method for aberration detection using Zernike polynomials. The process involves scanning the object plane to identify the optimal focus and subsequently fitting the acquired focus data to Zernike polynomials. This fitting procedure facilitates the analysis of various aberrations in the optical system.

• ETRI Journal
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• v.30 no.2
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• pp.335-337
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• 2008

An eye detection method for facial images using Zernike moments with a support vector machine (SVM) is proposed. Eye/non-eye patterns are represented in terms of the magnitude of Zernike moments and then classified by the SVM. Due to the rotation-invariant characteristics of the magnitude of Zernike moments, the method is robust against rotation, which is demonstrated using rotated images from the ORL database. Experiments with TV drama videos showed that the proposed method achieved a 94.6% detection rate, which is a higher performance level than that achievable by the method that uses gray values with an SVM.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.285-286
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• 2007

This paper presents a method to detect eyes in the facial image using Zernike moments and SVM After detecting eye candidate regions from the facial image, Zernike moments are computed on those regions with moving a $15{\times}15$ window. Then, SVM that uses Zernike moments as an input vector detects eyes. In the experimental results, the proposed method shows the eye detection rate of about 90%.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.208-211
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• 2005

The volumetric interferometer, which uses the interference of wavefronts emitted from two single mode fibers, measures the target position in 3-D. In this paper, we suggest a new calculation method which doesn't need a non-linear optimization and an initial guess. We find the relationship between the coefficients of the Zernike polynomials for a spherical wavefront and its center and reconstruct a spherical wavefront by using the Zernike polynomials from two interference fringes like a lateral shearing interferometer. The target position can be obtained from the coefficients of the Zernike polynomials of the reconstructed wavefront. We can get the target position in 3-D with $sub-{\mu}m$ errors in a simulation.

• Current Optics and Photonics
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• v.7 no.6
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• pp.692-700
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• 2023

We present a method for improving the accuracy of the modal wavefront reconstruction in the radial shearing interferometers (RSIs). Our approach involves expanding the reduced radial terms of Zernike polynomials to high-order, which enables more precise reconstruction of the wavefront aberrations with high-spatial frequency. We expanded the reduced polynomials up to infinite order with symbolic variables of the radius, shearing amount, and transformation matrix elements. For the simulation of the modal wavefront reconstruction, we generated a target wavefront subsequently, magnified and measured wavefronts were generated. To validate the effectiveness of the high-order Zernike polynomials, we applied both low- and high-order polynomials to the wavefront reconstruction process. Consequently, the peak-to-valley (PV) and RMS errors notably decreased with values of 0.011λ and 0.001λ, respectively, as the order of the radial Zernike polynomial increased.

• 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.