• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2007.04a
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• pp.187-190
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• 2007

This study is for analyzing best band in image matching using correlation coefficient of left and right images of stereo image pair, lot red, green, blue band images separated from color aerial photo and gray image converted from the same color aerial photo image. The image matching is applied to construct Digital Elevation Model(DEM) or terrain data. The correlation coefficients and variation by change of pixel patch size are computed from pixel patches of which sizes are $11{\times}11{\sim}101{\times}101$. Consequently, the correlation coefficient in red band image is highest. The lowest is in blue band. Therefore, to construct terrain data using image matching, the red band image is preferable. As the size of pixel patch is growing, the correlation coefficient is increasing. But increasing rate declines from $51{\times}51$ image patch size and above. It is proved that the smaller pixel patch size than $51{\times}51$ is applied to construct terrain data using image matching.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.24 no.4
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• pp.310-316
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• 2014

The location template matching(LTM) method is a technique of identifying an impact location on a structure, and requires a certain measure of similarity between two time signals. In general, the correlation coefficient is widely used as the measure of similarity, while the group delay based method is recently proposed to improve the accuracy of the impact localization. Another possible measure is the frequency response assurance criterion(FRAC), though this has not been applied yet. In this paper, these three different measures of similarity are examined comparatively by using experimental data in order to understand the properties of these measures of similarity. The comparative study shows that the correlation coefficient and the FRAC give almost the same information while the group delay based method gives the shape oriented information that is best suitable for the location template matching method.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.04a
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• pp.506-511
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• 2014

The location template matching (LTM) method is a technique of identifying an impact location on a structure, and requires a certain measure of similarity between two time signals. In general, the correlation coefficient is widely used as the measure of similarity, while the group delay based method is recently proposed to improve the accuracy of the impact localization. Another possible measure is the frequency response assurance criterion (FRAC), though this has not been applied yet. In this paper, these three different measures of similarity are examined comparatively by using experimental data in order to understand the properties of these measures of similarity. The comparative study shows that the correlation coefficient and the FRAC give almost the same information while the group delay based method gives the shape oriented information that is best suitable for the location template matching method.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.230-234
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• 1997

The matching pursuit (MP) algorithm developed by S. Mallat and Z. Zhang is applied to magnetic resonance (MR) imaging. Since matching pursuit is a greedy algorithm to find waveforms which are the best match for an object-signal, the signal can be decomposed with a few iterations. In this paper, we propose an application of the MP algorithm to the MR imaging to reduce imaging time. Inner products of residual signals and selected waveforms in the MP algorithm are derived from the MR signals by excitation of RF pulses which are fourier transforms of selected waveforms. Results from computer simulations demonstrate that the imaging time is reduced by using the MP algorithm and further a progressive reconstruction can be achieved.

• Journal of the Semiconductor & Display Technology
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• v.8 no.4
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• pp.95-100
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• 2009

This paper presents an efficient disparity matching, using sum of absolute difference (SAD) and dynamic programming (DP) algorithm. This algorithm makes use of one of area-based algorithm which is the absolute sum of the pixel difference corresponding to the window size. We use the information of the right eye brightness (B) and the left eye brightness to get an best matching results and apply the results to the left eye image using the window go by the brightness of the right eye image. This is that we can control the brightness. The major feature of this algorithm called SAD+DP+B is that although Root Mean Square (RMS) performance is slightly less than SAD+DP, due to comparing original image, its visual performance is increased drastically for matching the disparity map on account of its matching compared to SAD+DP. The simulation results demonstrate that the visual performance can be increased and the RMS is competitive with or slightly higher than SAD+DP.

• Journal of Korean Society for Quality Management
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• v.45 no.4
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• pp.829-846
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• 2017

Purpose: This paper compares the effectiveness of typical selective assembly criteria and suggests the most promising one. Based on the result of a computer simulation, the key issues of selective assembly are examined and the best criterion is recommended from the effectiveness perspective. Methods: Using JAVA program, production of ten thousand units for each pair of components are simulated for selective assembly of the two types. And the number of mismatching and the fraction nonconforming for each criterion are determined. Results: The best match criterion appeared to be most promising from the perspectives of both mismatching and nonconforming problems. Its effectiveness appears to be also good even when the precision of one component is different from that of the other. Conclusion: For designing an optimal method for selective assembly, the best match criterion is recommendable as the base criterion.

• Journal of the Korea society of information convergence
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• v.7 no.2
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• pp.59-65
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• 2014

Grassfire spot matching method is based on similarity comparison of topological patterns for neighbor spots. This is a method where spot matching is performed as if fire spreads all around on grass. Spot matching starts from a seed spot pair confirmed as a matched pair of spots and spot matching spreads to the direction where the best matching result is produced. In this paper, it is a bit complicated way of grassfire method where multi-seed matched spot pair are manually selected and spot matching is performed from each multi-seed matched spot pair. The proposed method shows better performance in detection rate and accuracy than that of the previous method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.7
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• pp.1667-1679
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• 2010

This thesis proposes an adaptive variable-sized matching window method using the characteristic points of the image and a method to increase the reliability of the cross-consistency check to raise the correctness of the final disparity image. The proposed adaptive variable-sized window method segments the image with the color information, finds the characteristic points in each segmented image, and varies the size of the matching window according to the existence of the characteristic points inside the window. Also the proposed cross-consistency check method processes the two cases with the cost values corresponding to the best disparity and the second-best disparity: when the cost values themselves are too large and when the difference between the two cost values are too small. The two proposed methods were experimented with the four test images provided by the Middleburry site. As the results from the experiments, the proposed adaptive variable-sized matching window method decreased up to 18.2% of error ratio and the proposed cross-consistency check method increased up to 7.4% of reliability.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.295-296
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• 2006

This paper implements a method that tracking the moving objects that detected by the motion detection function of the digital CCTV system. We simply implement the motion detection function of the digital CCTV system that use frame difference and thresholding. When motion is detected, the motion detection function generates two outputs. One output is the event that the motion is arised in input image frame. The other output is coordinate that motion is exists. Then, do the block matching algorithm[2] using coordinate, that motion is exists, as initial coordinate of the block matching algorithm. The best matched coordinate is new initial coordinate of the block matching algorithm for the next image frame. We simply use the block matching algorithm that implements tracking the moving objects. It is simple, but useful the actual digital CCTV system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.48
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• pp.213-222
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• 1998

The objective of this study is to develop a knowledge-based scheduler applying simulation and knowledge base. This study utilizes a machine induction to build knowledge base which enables knowledge acquisition without domain expert. In this study, the best job dispatching rule for each order is selected according to the specifications of the order information. And these results are built to the fact base and knowledge base using the attribute-oriented induction method and simulation. When a new order enters in the developed system, the scheduler retrieves the knowledge base in order to find a matching record. If there is a matching record, the scheduling will be carried out by using the job dispatching rule saved in the knowledge base. Otherwise the best rule will be added to the knowledge base as a new record after scheduling to all the rules. When all these above steps finished the system will furnish a learning function.