• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.835-836
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• 2008

This paper presents a new method of segmenting characters in a car licence plate which is less influenced by illumination variation. It uses an anisotropic filter to reduce the lighting noise and a histogram specification scheme to obtain the binary image. Anisotropic smoothing filter process the input images, which are acquired under different lighting conditions, so that they may have similar image quality. The enhanced performance of the proposed algorithm has been proved by the experiment.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.275-276
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• 2007

In this paper, we propose an efficient illumination preprocessing algorithm for face recognition. One of the best known illumination preprocessing method, based on anisotropic smoothing, enhances the edge information, but instead deteriorates the contrast of the original image. Our proposed method reduces the deterioration of the contrast while enhancing the edge information, and thus the preprocessed image does not lose features like Gabor features of the original images much.. The effectiveness of the proposed illumination preprocessing method is verified through experiments of face recognition.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.1
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• pp.21-26
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• 2002

Video stream invoke a variety of traffic with the structure of compression algorithm and image complexity. Thus, it is difficult to allocate the resource on the both sides of sender and receiver, and playout on the Internet such as a packet switched network. Thus, in this paper we proposed video stream smoothing using multistream for the effective transmission of video stream. This method specifies the type of LDU(logical data unit) according to the type of original stream, and then makes a large number of streams as a fixed size, and transfers them. So, the proposed method can reduce the buffering time which occurs during the process of the smoothing and prefetch be robust to the jitter on network, as well. Consequently, it has the effective transmission characteristics of fully utilizing the clients bandwidth.

• Geophysics and Geophysical Exploration
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• v.16 no.4
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• pp.211-216
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• 2013

In general, smoothing filters regularize functions by reducing differences between adjacent values. The smoothing filters, therefore, can regularize inverse solutions and produce more accurate subsurface structure when we apply it to full waveform inversion. If we apply a smoothing filter with a constant coefficient to subsurface image or velocity model, it will make layer interfaces and fault structures vague because it does not consider any information of geologic structures and variations of velocity. In this study, we develop a selective smoothing regularization technique, which adapts smoothing coefficients according to inversion iteration, to solve the weakness of smoothing regularization with a constant coefficient. First, we determine appropriate frequencies and analyze the corresponding wavenumber coverage. Then, we define effective maximum wavenumber as 99 percentile of wavenumber spectrum in order to choose smoothing coefficients which can effectively limit the wavenumber coverage. By adapting the chosen smoothing coefficients according to the iteration, we can implement multi-scale full waveform inversion while inverting multi-frequency components simultaneously. Through the successful inversion example on a salt model with high-contrast velocity structures, we can note that our method effectively regularizes the inverse solution. We also verify that our scheme is applicable to field data through the numerical example to the synthetic data containing random noise.

• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.333-342
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• 2005

This paper proposes a method, which recognizes student's identification card by using image processing and recognition technology and can manage student information on the web. The presented scheme sets up an average brightness as a threshold, based on the brightest Pixel and the least bright one for the source image of the ID card. It is converting to binary image, applies a horizontal histogram, and extracts student number through its location. And, it removes the noise of the student number region by the mode smoothing with 3$\times$3 mask. After removing noise from the student number region, each number is extracted using vertical histogram and normalized. Using the enhanced ART1 algorithm recognized the extracted student number region. In this study, we propose the enhanced ART1 algorithm different from the conventional ART1 algorithm by the dynamical establishment of the vigilance parameter. which shows a tolerance limit of unbalance between voluntary and stored patterns for clustering. The Experiment results showed that the recognition rate of the proposed ART1 algorithm was improved much more than that of the conventional ART1 algorithm. So, we develop an educational matters administration system by using the proposed recognition method of the student's identification card.

• Structural Engineering and Mechanics
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• v.77 no.1
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• pp.47-56
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• 2021

This paper deals with damage detection using a Gapped Smoothing Method (GSM) combined with deep learning. Convolutional Neural Network (CNN) is a model of deep learning. CNN has an input layer, an output layer, and a number of hidden layers that consist of convolutional layers. The input layer is a tensor with shape (number of images) × (image width) × (image height) × (image depth). An activation function is applied each time to this tensor passing through a hidden layer and the last layer is the fully connected layer. After the fully connected layer, the output layer, which is the final layer, is predicted by CNN. In this paper, a complete machine learning system is introduced. The training data was taken from a Finite Element (FE) model. The input images are the contour plots of curvature gapped smooth damage index. A free-free beam is used as a case study. In the first step, the FE model of the beam was used to generate data. The collected data were then divided into two parts, i.e. 70% for training and 30% for validation. In the second step, the proposed CNN was trained using training data and then validated using available data. Furthermore, a vibration experiment on steel damaged beam in free-free support condition was carried out in the laboratory to test the method. A total number of 15 accelerometers were set up to measure the mode shapes and calculate the curvature gapped smooth of the damaged beam. Two scenarios were introduced with different severities of the damage. The results showed that the trained CNN was successful in detecting the location as well as the severity of the damage in the experimental damaged beam.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2004.07a
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• pp.220-220
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• 2004

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.133-137
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• 2000

In this paper, We study on converting data transfer using Image processing method. In the program's code consist of outline trace, noise filtering methode, pont data smoothing, algorithm. We use those Algorithm to create Vectorized data file format from image data. This result can be utilized as a base part for development of Automatic recognition for mechanical drawings.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.9
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• pp.59-67
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• 1992

Color image enhancement can be achieved easily by using linear form of coordinate system. But some popular color coordinate systems almost have nonlinear characteristics in the geometric form. In this paper, the proposed coordinate system has pseudo-linear form and based on IHS system which represents human color perception appropriately. And for the image intensity processing, an edge-preserving smoothing algorithm is presented.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.7
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• pp.685-691
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• 2020

In this study, an algorithm that recognizes numbers from 0 to 9 was developed using the data obtained after tracking hand movements using the echo signal of a millimeter-wave radar sensor at 77 GHz. The echo signals obtained from the radar sensor by detecting the motion of a hand gesture revealed a cluster of irregular dots due to the difference in scattering cross-sectional area. A valid center point was obtained from them by applying a K-Means algorithm using 3D coordinate values. In addition, the obtained center points were connected to produce a numeric image. The recognition rate was compared by inputting the obtained image and an image similar to human handwriting by applying the smoothing technique to a CNN (Convolutional Neural Network) model trained with MNIST (Modified National Institute of Standards and Technology database). The experiment was conducted in two ways. First, in the recognition experiments using images with and without smoothing, average recognition rates of 77.0% and 81.0% were obtained, respectively. In the experiment of the CNN model with augmentation of learning data, a recognition rate of 97.5% and 99.0% on average was obtained in the recognition experiment using the image with and without smoothing technique, respectively. This study can be applied to various non-contact recognition technologies using radar sensors.