• 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.

• 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%.

• 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 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.

• 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.

• 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.

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.85-96
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• 2020

It is important to extract salient object image and to solve the invariance problem for image recognition. In this paper we propose a new hybrid algorithm for invariance and improved classification performance in image recognition, whose algorithm is combined by FT(Frequency-tuned Salient Region Detection) algorithm, Guided filter, Zernike moments, and a simple artificial neural network (Multi-layer Perceptron). The conventional FT algorithm is used to extract initial salient object image, the guided filtering to preserve edge details, Zernike moments to solve invariance problem, and a classification to recognize the extracted image. For guided filtering, guided filter is used, and Multi-layer Perceptron which is a simple artificial neural networks is introduced for classification. Experimental results show that this algorithm can achieve a superior performance in the process of extracting salient object image and invariant moment feature. And the results show that the algorithm can also classifies the extracted object image with improved recognition rate.

• 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.