• Journal of the Korean Geophysical Society
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• v.9 no.2
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• pp.99-103
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• 2006

3D object recognition independent of translation and rotation using an ultrasonic sensor array, invariant moment vectors and SOFM(Self Organizing Feature Map) neural networks is presented. Using invariant moment vectors of the acquired 16×8 pixel data of square, rectangular, cylindric and regular triangular blocks, 3D objects could be classified by SOFM neural networks. Invariant moment vectors are constant independent of translation and rotation. The recognition rates for the training and testing data were 95.91% and 92.13%, respectively.

• 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.

• The Journal of Korean Institute of Information Technology
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• v.17 no.5
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• pp.13-20
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• 2019

Recently, object detection technology based on CNN has been actively studied. Object detection technology is used as an important technology in autonomous vehicles, intelligent image analysis, and so on. In this paper, we propose a rotation change robust object detection system by applying MI-FL (Moment Invariant-Feature Layer) to SSD (Single Shot Multibox Detector) which is one of CNN-based object detectors. First, the features of the input image are extracted based on the VGG network. Then, a total of six feature layers are applied to generate bounding boxes by predicting the location and type of object. We then use the NMS algorithm to get the bounding box that is the most likely object. Once an object bounding box has been determined, the invariant moment feature of the corresponding region is extracted using MI-FL, and stored and learned in advance. In the detection process, it is possible to detect the rotated image more robust than the conventional method by using the previously stored moment invariant feature information. The performance improvement of about 4 ~ 5% was confirmed by comparing SSD with existing SSD and MI-FL.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.5
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• pp.1240-1250
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• 2010

In this paper, an eye tracking method is presented by using on iterated spatial moment adapting weighted gray level that can accurately detect and track user's eyes under the complicated background. The region of face is detected by using Haar-like feature before extracting region of eyes to minimize an region of interest from the input picture of CCD camera. And the region of eyes is detected by using eigeneye based on the eigenface of Principal component analysis. Also, feature points of eyes are detected from darkest part in the region of eyes. The tracking of eyes is achieved correctly by using iterated spatial moment adapting weighted gray level.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.198-201
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• 2009

In this paper, an eye tracking method is presented by using on iterated spatial moment adapting weighted gray level that can accurately detect and track user's eyes under the complicated background. The region of face is detected by using Haar-like feature before extracting region of eyes to minimize an region of interest from the input picture of CCD camera. And the region of eyes is detected by using eigeneye based on the eigenface of Principal component analysis. And then feature points of eyes are detected from darkest part in the region of eyes. The tracking of eyes is achieved correctly by using iterated spatial moment adapting weighted gray level.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.2
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• pp.203-210
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• 2009

The problem of outdoor localization can be practically solved by GPS. However, GPS is not perfect and some areas of outdoor navigation should consider other solutions. This research deals with outdoor localization using an elevation map without GPS. This paper proposes a novel feature, elevation moment of inertia (EMOI), which represents the distribution of elevation as a function of distance from a robot in the elevation map. Each cell of an elevation map has its own EMOI, and outdoor localization can be performed by matching EMOIs obtained from the robot and the pre-given elevation map. The experiments and simulations show that the proposed EMOI can be usefully exploited for outdoor localization with an elevation map and this feature can be easily applied to other probabilistic approaches such as Markov localization method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.305-309
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• 2000

In this paper, we presents a new methodology for face recognition after analysing conventional ICA(Independent Component Analysis) based approach. In the literature we found that ICA based methods have followed the same procedure without any exception, first PCA(Principal Component Analysis) has been used for feature extraction, next ICA learning method has been applied for feature enhancement in the reduced dimension. However, it is contradiction that features are extracted using higher order moments depend on variance, the second order statistics. It is not considered that a necessary component can be located in the discarded feature space. In the new methodology, features are extracted using the magnitude of kurtosis(4-th order central moment or cumulant). This corresponds to the PCA based feature extraction using eigenvalue(2nd order central moment or variance). The synergy effect of PCA and ICA can be achieved if PCA is used for noise reduction filter. ICA methodology is analysed using SVD(Singular Value Decomposition). PCA does whitening and noise reduction. ICA performs the feature extraction. Simulation results show the effectiveness of the methodology compared to the conventional ICA approach.

• ETRI Journal
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• v.34 no.4
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• pp.572-582
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• 2012

The discrete Gaussian-Hermite moment (DGHM) is a global feature representation method that can be applied to square images. We propose a modified DGHM (MDGHM) method and an MDGHM-based scale-invariant feature transform (MDGHM-SIFT) descriptor. In the MDGHM, we devise a movable mask to represent the local features of a non-square image. The complete set of non-square image features are then represented by the summation of all MDGHMs. We also propose to apply an accumulated MDGHM using multi-order derivatives to obtain distinguishable feature information in the third stage of the SIFT. Finally, we calculate an MDGHM-based magnitude and an MDGHM-based orientation using the accumulated MDGHM. We carry out experiments using the proposed method with six kinds of deformations. The results show that the proposed method can be applied to non-square images without any image truncation and that it significantly outperforms the matching accuracy of other SIFT algorithms.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1057-1060
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• 2004

We propose new rotation moment invariants based on multiresolution filter bank techniques. The multiresolution pyramid motivates our simple but efficient feature selection procedure based on the fuzzy C-mean clustering, combined with the Mahalanobis distance. The procedure verifies an impact of random noise as well as an interesting and less known impact of noise due to spatial transformations. The recognition accuracy of the proposed techniques has been tested with the preceding moment invariants as well as with some wavelet based schemes. The numerical experiments, with more than 30,000 images, demonstrate a tangible accuracy increase of about 3% for low noise, 8% for the average noise and 15% for high level noise.