• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.3
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• pp.61-67
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• 2001

In this paper, a method that apply LP algorithm to image for speech recognition is suggested, using both speech and image information for recogniton of korean numeral speech. The input speech signal is pre-emphasized with parameter value 0.95, analyzed for B th LP coefficients using Hamming window, autocorrelation and Levinson-Durbin algorithm. Also, a gray image signal is analyzed for 2-dimensional LP coefficients using autocorrelation and Levinson-Durbin algorithm like speech. These parameters are used for input parameters of neural network using back-propagation algorithm. The recognition experiment was carried out at each noise level, three numeral speechs, '3','5', and '9' were enhanced. Thus, in case of recognizing speech with 2-dimensional LP parameters, it results in a high recognition rate, a low parameter size, and a simple algorithm with no additional feature extraction algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.5
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• pp.744-752
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• 2012

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as an one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problems. In data preprocessing part, Principal Component Analysis(PCA) which is generally used in face recognition, which is useful to express some classes using reduction, since it is effective to maintain the rate of recognition and to reduce the amount of data at the same time. However, because of there of the whole face image, it can not guarantee the detection rate about the change of viewpoint and whole image. Thus, to compensate for the defects, Linear Discriminant Analysis(LDA) is used to enhance the separation of different classes. In this paper, we combine the PCA&LDA algorithm and design the optimized pRBFNNs for recognition module. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as two kinds of polynomials such as constant, and linear. The coefficients of connection weight identified with back-propagation using gradient descent method. The output of the pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. The proposed pRBFNNs are applied to face image(ex Yale, AT&T) datasets and then demonstrated from the viewpoint of the output performance and recognition rate.

• The Journal of the Korea Contents Association
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• v.9 no.8
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• pp.57-63
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• 2009

Object recognition technologies using PCA(principal component analysis) recognize objects by deciding representative features of objects in the model image, extracting feature vectors from objects in a image and measuring the distance between them and object representation. Given frequent recognition problems associated with the use of point-to-point distance approach, this study adopted the k-nearest neighbor technique(class-to-class) in which a group of object models of the same class is used as recognition unit for the images in-putted on a continual input image. However, the robustness of recognition strategies using PCA depends on several factors, including illumination. When scene constancy is not secured due to varying illumination conditions, the learning performance the feature detector can be compromised, undermining the recognition quality. This paper proposes a new PCA recognition in which database of objects can be detected under different illuminations between input images and the model images.

• Journal of Information Processing Systems
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• v.12 no.3
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• pp.392-409
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• 2016

The aim of this paper is to examine the effectiveness of combining three popular tools used in pattern recognition, which are the Active Appearance Model (AAM), the two-dimensional discrete cosine transform (2D-DCT), and Kernel Fisher Analysis (KFA), for face recognition across age variations. For this purpose, we first used AAM to generate an AAM-based face representation; then, we applied 2D-DCT to get the descriptor of the image; and finally, we used a multiclass KFA for dimension reduction. Classification was made through a K-nearest neighbor classifier, based on Euclidean distance. Our experimental results on face images, which were obtained from the publicly available FG-NET face database, showed that the proposed descriptor worked satisfactorily for both face identification and verification across age progression.

• Journal of Information Processing Systems
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• v.12 no.4
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• pp.591-611
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• 2016

High dimensional space is the biggest problem when classification process is carried out, because it takes longer time for computation, so that the costs involved are also expensive. In this research, the facial space generated from homogeneous and non-homogeneous polynomial was proposed to extract the facial image features. The homogeneous and non-homogeneous polynomial-based eigenspaces are the second opinion of the feature extraction of an appearance method to solve non-linear features. The kernel trick has been used to complete the matrix computation on the homogeneous and non-homogeneous polynomial. The weight and projection of the new feature space of the proposed method have been evaluated by using the three face image databases, i.e., the YALE, the ORL, and the UoB. The experimental results have produced the highest recognition rate 94.44%, 97.5%, and 94% for the YALE, ORL, and UoB, respectively. The results explain that the proposed method has produced the higher recognition than the other methods, such as the Eigenface, Fisherface, Laplacianfaces, and O-Laplacianfaces.

• International Journal of Internet, Broadcasting and Communication
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• v.10 no.3
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• pp.73-80
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• 2018

This study introduces an interactive drawing system, a tool that allows user to sketch and draw with his own intentions. The proposed system enables the user to express more creatively through a tool that allows the user to reproduce his original idea as a drawing and transform it using his body. The user can actively participate in the production of the artwork by studying the unique formative language of the spectator. In addition, the user is given an opportunity to experience a creative process by transforming arbitrary drawing into various shapes according to his gestures. Interactive drawing systems use the segmentation of the drawing image as a way to extend the user's initial drawing idea. The system includes transforming a two-dimensional drawing into a volume-like form such as a three-dimensional drawing using image segmentation. In this process, a psychological space is created that can stimulate the imagination of the user and project the object of desire. This process of drawing personification plays a role of giving the user familiarity with the artwork and indirectly expressing his her emotions to others. This means that the interactive drawing, which has changed to the emotional concept of interaction beyond the concept of information transfer, can create a cooperative sensation image between user's time and space and occupy an important position in multimedia society.

• Korean Journal of Cognitive Science
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• v.11 no.2
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• pp.79-87
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• 2000

In this paper we present a face recognition and face detection algorithm in MPEG compressed video. The proposed method consists three stage of processing steps. The first step is to produce a spatially reduced DC image form MPEG compressed video for processing. And the second step is face detection on reduced DC image. Finally, the last step is face recognition on partially extracted compressed frames which contain the detected faces. The spatially reduced DC image is produced from two dimensional inverse DCT of the DC coefficient and the first two AC coefficients. The face detection is performed on DC image and face recognition is performed on one extracted frame per GOP by using the K-L transform. In order to evaluate the proposed method, we carried out experiments on video database. The experiment results show the proposed method is very efficient and helpful for target tasks.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.638-643
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• 2009

A computer-generated hologram(CGH) is made for three-dimensional image reconstruction of a virtual object which is a difficult to irradiate the laser light directly. One of the adverse effect factors is quantization of wave front computed by program when a computer-generated hologram is made. Amplitude element is not considered in Kinoform, it needs processing to reduce noise or false image. So several investigation was reported that the improvement of reconstructed image of Kinoform. Means to calculate the most suitable complex amplitude distribution are iterative algorithm, simulated annealing algorithm and genetic Algorithm. Error diffusion method reconstructed to separate the object as for the noise that originated in the quantization error. So it is efficient method to obtain high quality image with not many processing.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.2A
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• pp.128-134
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• 2012

In this paper, we propose a novel approach to enhance the recognition performance of a far and partially occluded three-dimensional (3-D) target in computational curving-effective integral imaging (CEII) by using the direct pixel-mapping (DPM) method. With this scheme, the elemental image array (EIA) originally picked up from a far and partially occluded 3-D target can be converted into a new EIA just like the one virtually picked up from a target located close to the lenslet array. Due to this characteristic of DPM, resolution and quality of the reconstructed target image can be highly enhanced, which results in a significant improvement of recognition performance of a far 3-D object. Experimental results reveal that image quality of the reconstructed target image and object recognition performance of the proposed system have been improved by 1.75 dB and 4.56% on the average in PSNR (peak-to-peak signal-to-noise ratio) and NCC (normalized correlation coefficient), respectively, compared to the conventional system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.6
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• pp.900-911
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• 2015

In this paper, 3D face recognition model is designed by using Polynomial based RBFNN(Radial Basis Function Neural Network) and PNN(Polynomial Neural Network). Also recognition rate is performed by this model. In existing 2D face recognition model, the degradation of recognition rate may occur in external environments such as face features using a brightness of the video. So 3D face recognition is performed by using 3D scanner for improving disadvantage of 2D face recognition. In the preprocessing part, obtained 3D face images for the variation of each pose are changed as front image by using pose compensation. The depth data of face image shape is extracted by using Multiple point signature. And whole area of face depth information is obtained by using the tip of a nose as a reference point. Parameter optimization is carried out with the aid of both ABC(Artificial Bee Colony) and PSO(Particle Swarm Optimization) for effective training and recognition. Experimental data for face recognition is built up by the face images of students and researchers in IC&CI Lab of Suwon University. By using the images of 3D face extracted in IC&CI Lab. the performance of 3D face recognition is evaluated and compared according to two types of models as well as point signature method based on two kinds of depth data information.