• Journal of the Institute of Convergence Signal Processing
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• v.11 no.4
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• pp.320-325
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• 2010

This paper is about the method and procedure to register the candidate sequentially in the face recognition system using the PCA(Principal Components Analysis). We use the method to update the principal components sequentially with the SKL algorithm which is improved R-SVD algorithm. This algorithm enable us to solve the re-training problem of the increase the candidates number sequentially in the face recognition using the PCA. Also this algorithm can use in robust tracking system with the bright change based to the principal components. This paper proposes the procedure in the face recognition system which sequentially updates the principal components using the SKL algorithm. Then we compared the face recognition performance with the batch procedure for calculating the principal components using the standard KL algorithm and confirms the effects of the forgetting factor in the SKL algorithm experimentally.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.583-586
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• 1999

In this paper, the wavelet transform is performed in the input 256$\times$256 color image and decomposes a image into low-pass and high-pass components. Since the high-pass band contains the components of three directions, edges are detected by combining three parts. After finding the position of face using the histogram of the edge component, a face region in low-pass band is cut off. Since RGB color image is sensitively affected by luminances, the image of low pass component is normalized, and a facial region is detected using face color informations. As the wavelet transform decomposes the detected face region into three layer, the dimension of input image is reduced. In this paper, we use the 3000 images of 10 persons, and KL transform is applied in order to classify face vectors effectively. FCM(Fuzzy C-Means) algorithm classifies face vectors with similar features into the same cluster. In this case, the number of cluster is equal to that of person, and the mean vector of each cluster is used as a codebook. We verify the system performance of the proposed algorithm by the experiments. The recognition rates of learning images and testing image is computed using correlation coefficient and Euclidean distance.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.6
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• pp.88-98
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• 1998

Subspace classifier is a popular pattern recognition method based on Karhunen-Loeve transform. This classifier describes a high dimensional pattern by using a reduced dimensional subspace. Because of the loss of information induced by dimensionality reduction, however, a subspace classifier sometimes shows unsatisfactory recognition performance to the patterns having quite similar principal components each other. In this paper, we propose the use of multiple novelty neural network classifiers constructed on novelty vectors to adopt minor components usually ignored and present a method of improving recognition performance through combining those with the subspace classifier. We develop the proposed classifier on handwritten numeral database and analyze its properties. Our proposed classifier shows better recognition performance compared with other classifiers, though it requires more weight links.

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.1
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• pp.95-100
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• 2007

In the steel & iron processing line, some characters are marked for the material management in the surface of material. This paper describes about the developed results of vision system for the recognition of material management characters, which was included in the slab image. Our vision system for the character recognition includes that CCD camera system which acquire slab image, optical transmission system which transmit captured image to the long distance, input and output system for the interface with existing system and monitoring system for the checking of recognition results. We have installed our vision system at the continuous casting line and tested. Also, we have performed inspection of durability, reliability and recognition rate. Through the testing, we have confirmed that our system have high recognition rate, 97.4%.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.1
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• pp.96-107
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• 1996

A voice personality transformation algorithm using orthogonal vector space conversion is proposed in this paper. Voice personality transformation is the process of changing one person's acoustic features (source) to those of another person (target). In this paper, personality transformation is achieved by changing the LPC cepstrum coefficients, excitation spectrum and pitch contour. An orthogonal vector space conversion technique is proposed to transform the LPC cepstrum coefficients. The LPC cepstrum transformation is implemented by principle component decomposition by applying the Karhunen-Loeve transformation and minimum mean-square error coordinate transformation(MSECT). Additionally, we propose a pitch contour modification method to transform the prosodic characteristics of any speaker. To do this, reference pitch patterns for source and target speaker are firstly built up, and speaker's one. The experimental results show the effectiveness of the proposed algorithm in both subjective and objective evaluations.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.986-989
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• 2003

In the steel production line, the molten metal of a furnace is transformed into slab material and then move to the hot strip line, This paper describe about the real time recognition system of material management number, which is marked at the surface of a slab in the steel production line. This recognition processing should be performed before the slab is moved to the hot strip line. This system include following recognition steps. First, we remove noise from the captured slab image by use pre-filter. Second, we extract rough area, which is include slab number and then, we extract individual number area. Finally, we recognize material management number by use KLT(Karhunen-Loeve transform) algorithm. We applied our system to the real slave image, which was captured in the process line. In the results, we recognized slave number to the 94% accuracy.