• Communications for Statistical Applications and Methods
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• v.26 no.5
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• pp.507-517
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• 2019

Statistical matching is a method of combining multiple sources of data that are extracted or surveyed from the same population. It can be used in situation when variables of interest are not jointly observed. It is a low-cost way to expect high-effects in terms of being able to create synthetic data using existing sources. In this paper, we propose the several statistical micro matching methods using a multinomial logistic regression model when all variables of interest are categorical or categorized ones, which is common in sample survey. Under conditional independence assumption (CIA), a mixed statistical matching method, which is useful when auxiliary information is not available, is proposed. We also propose a statistical matching method with auxiliary information that reduces the bias of the conventional matching methods suggested under CIA. Through a simulation study, proposed micro matching methods and conventional ones are compared. Simulation study shows that suggested matching methods outperform the existing ones especially when CIA does not hold.

• Journal of KIISE:Software and Applications
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• v.31 no.7
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• pp.959-967
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• 2004

Jain et al. proposed the hybrid matching method which was combined the minutia-based matching method and the filter-bank based matching method. And, their experimental results proved the hybrid matching method was more effective than each of them. However, this hybrid method cannot utilize each peculiar advantage of two methods. The reason is that it gets the matching score by simply summing up each weighted matching score after executing two methods individually. In this paper, we propose new hybrid matching method. It mixes two matching methods during the feature extraction process. This new hybrid method has lower ERR than the filter-bank based method and higher ERR than the minutia-based method. So, we propose the adaptive hybrid scoring method, which selects the matching score in order to preserve the characteristics of two matching methods. Using this method, we can get lower ERR than the hybrid matcher by Jain et al. Experimental results indicate that the proposed methods can improve the matching performance up to about 1% in ERR.

• The KIPS Transactions:PartD
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• v.12D no.3 s.99
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• pp.355-364
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• 2005

Previous researches on subsequence matching have been focused on how to make indexes in order to speed up the matching time, and do not take into account the effectiveness issues of subsequence matching methods. This paper considers the effectiveness of subsequence matching methods and proposes two metrics for effectiveness evaluations of subsequence matching algorithms. We have applied the proposed metrics to Korean stock data and five known matching algorithms. The analysis on the empirical data shows that two methods (i.e., the method supporting normalization, and the method supporting scaling and shifting) outperform the others in terms of the effectiveness of subsequence matching.

• Korean Journal of Remote Sensing
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• v.8 no.1
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• pp.15-26
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• 1992

This paper describes an implementation of adaptive stereo matching for DBM generation. The matching method of two stereo satellite images to find corresponding points used in this paper is area-based matching, which is usually used in the field of making DBM. Same window size and search area used as in the conventional matching methods and we propose adaptive stereo matching algorithm in this paper. We cluster three areas which are consist of mountainous areas, cultivated areas and cities, and rivers and lakes by using proposed linear feature extracting method. These classified areas are matched by adaptive window size and search area, but rivers and lakes is excluded in this experiment. The matching time is three times faster than conventional methods.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.11
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• pp.1027-1033
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• 2012

Semantic-based information retrieval techniques understand the meanings of the concepts that users specify in their queries, but the traditional semantic matching methods based on the ontology tree have three weaknesses which may lead to many false matches, causing the falling precision. In order to improve the matching precision and the recall of the information retrieval, this paper proposes a multi-agent improved semantic similarity matching algorithm based on the ontology tree, which can avoid the considerable computation redundancies and mismatching during the entire matching process. The results of the experiments performed on our algorithm show improvements in precision and recall compared with the information retrieval techniques based on the traditional semantic similarity matching methods.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.4
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• pp.667-674
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• 1987

In this paper an effective block matching algorithm is proposed to find the motion vector. There are two approaches to the estimation of the motion vector in MCC (motion compensated coding), i.e., pel(pixel element) recursive algorithm and block matching algorithm. The search algorithm in this paper is based on the block matching method. The advantage of a proposed algorithm using integral projections is the reduction of the computation time. While the conventional block matching methods have to be computed in 2-dimensional arrays, the proposed algorithm using integral projections can be computed in 1-dimensional arrays. In comparison with conventional block matching methods, a computer simulation shows that though the prediction error increases 0.23 db, it is not detectable for human eyes and the average reduction ratio of computation time obtained from the proposed algorithm is about 3-4.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.414-417
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• 1993

The methods for stereo matching are divided into two techniques: area-based matching and feature-based matching. To find corresponding points by area-based method, it takes a lot of time because there are many points to be matched. Feature-based matching algorithm is often used because with this method it matches only some feature points so that the processing time is fast even though it requires interpolation after matching. In this paper, we propose the smart technique by which we makes features simpler than conventional methods to match an image pair by feature-based matching algorithm.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.06a
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• pp.171-176
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• 1999

Template matching is one of the most often used techniques for machine vision applications to find a template of size M$\times$M or subimage in a scene image of size N$\times$N. Most template matching methods, however, require pixel operations between the template and the image under analysis resulting in high computational cost of O(M2N2). So in this thesis, we present a two stage template matching method. In the first stage, we use a novel low cost feature whose complexity is approaching O(N2) to select matching candidates. In the second stage, we use conventional template matching method to find out the exact matching point. We compare the result with other methods in terms of complexity, efficiency and performance. Proposed method was proved to have constant time complexity and to be quite invariant to noise.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.373-380
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• 2022

This paper considers a method of fast correspondence matching for iterative closest point (ICP) algorithm. In robotics, the ICP algorithm and its variants have been widely used for pose estimation by finding the translation and rotation that best align two point clouds. In computational perspectives, the main difficulty is to find the correspondence point on the reference point cloud to each observed point. Jump-table-based correspondence matching is one of the methods for reducing computation time. This paper proposes a method that corrects errors in an existing jump-table-based correspondence matching algorithm. The criterion activating the use of jump-table is modified so that the correspondence matching can be applied to the situations, such as point-cloud registration problems with highly curved surfaces, for which the existing correspondence-matching method is non-applicable. For demonstration, both hardware and simulation experiments are performed. In a hardware experiment using Hokuyo-10LX LiDAR sensor, our new algorithm shows 100% correspondence matching accuracy and 88% decrease in computation time. Using the F1TENTH simulator, the proposed algorithm is tested for an autonomous driving scenario with 2D range-bearing point cloud data and also shows 100% correspondence matching accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1597-1610
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• 2022

At present, the main method of high-speed train chassis detection is using computer vision technology to extract keypoints from two related chassis images firstly, then matching these keypoints to find the pixel-level correspondence between these two images, finally, detection and other steps are performed. The quality and accuracy of image matching are very important for subsequent defect detection. Current traditional matching methods are difficult to meet the actual requirements for the generalization of complex scenes such as weather, illumination, and seasonal changes. Therefore, it is of great significance to study the high-speed train image matching method based on deep learning. This paper establishes a high-speed train chassis image matching dataset, including random perspective changes and optical distortion, to simulate the changes in the actual working environment of the high-speed rail system as much as possible. This work designs a convolutional neural network to intensively extract keypoints, so as to alleviate the problems of current methods. With multi-level features, on the one hand, the network restores low-level details, thereby improving the localization accuracy of keypoints, on the other hand, the network can generate robust keypoint descriptors. Detailed experiments show the huge improvement of the proposed network over traditional methods.