• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.375-377
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• 2003

In this study, we employ the control patches, which have been created in previous photogrammetric projects, serving for the candidates of the control points that are likely to be found in the newly taken photos by utilizing image matching technique. Among others, the successful implementation of the above idea lies in the underlying factors: (1). Predicting the control patches and projecting them onto new photos; (2). Alignment of control patches with respect to the new photos; (3). Generating the equivalent ground elements of control patches versus the new photos for the purpose of correlation; (4). Developing effective matching methods and matching strategy; (5). Refining the exterior orientation parameters. What may show significance in this work comparing to traditional aerial-triangulation chain is that whenever at least three matched control patches succeed in a single photo, it follows that single photo orientation is applicable. The experiments suggest the potential efficiency of automatic control point measurement from control patch database and photo orientation by the proposed workflow.

• Journal of Information Processing Systems
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• v.12 no.1
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• pp.83-99
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• 2016

The latest research on the image-based fingerprint matching approaches indicates that they are less complex than the minutiae-based approaches when it comes to dealing with low quality images. Most of the approaches in the literature are not robust to fingerprint rotation and translation. In this paper, we develop a robust fingerprint matching system by extracting the circular region of interest (ROI) of a radius of 50 pixels centered at the core point. Maximizing their orientation correlation aligns two fingerprints that are to be matched. The modified Euclidean distance computed between the extracted orientation features of the sample and query images is used for matching. Extensive experiments were conducted over four benchmark fingerprint datasets of FVC2002 and two other proprietary databases of RFVC 2002 and the AITDB. The experimental results show the superiority of our proposed method over the well-known image-based approaches in the literature.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.74-74
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• 2002

This paper describes a corner matching technique for automatic relative orientation. Automatically matched corner points from stereo aerial images are used to a data set and help to improve automation of relative orientation process. A general corner matching process of overall image to image has very heavy operation and repetitive computation, so very time-consuming. But aerial stereo images are approximately seventy percent overlapped and little rotated. Based this hypothesis, we designed a sectional corner matching technique calculating correlation section by section between stereo images. Although the overlap information is not accurate, if we know it approximately, the matching process can be lighter. Since the size of aerial image is very large, corner extraction process is performed hierarchically by creating image pyramid, and corners extracted are refined at the higher level image. Extracted corners at the final step are matched section by section. Matched corners are filtered using positional information and their relation and distribution. Filtering process is applied over several steps because the thing affecting to get good result-good relative orientation parameter- is not the number of matched corner points but the accuracy of them. Filtered data is filtered one more during the process calculating relative orientation parameters. When the process is finished, we can get the well matched corner points and the refined Von-Gruber areas besides relative orientation parameters. This sectional corner matching technique is efficient by decreasing unnecessarily repetitive operations and contributes to improve automation for relative orientation.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.517-521
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• 2008

Hand motion classification problem is considered as basis for sign or gesture recognition. We promote optical flow as main feature extracted from images sequences to simultaneously segment the motion's area by its magnitude and characterize the motion' s directions by its orientation. We manage the flow orientation histogram as motion descriptor. A motion is encoded by concatenating the flow orientation histogram from several frames. We utilize simple histogram matching to classify the motion sequences. Attempted experiments show the feasibility of our method for hand motion localization and classification.

• Korean Journal of Remote Sensing
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• v.32 no.6
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• pp.647-656
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• 2016

Growth of UAV technology leads to expansion of UAV image applications. Many UAV image-based applications use a method called incremental bundle adjustment. However, incremental bundle adjustment produces large computation overhead because it attempts feature matching from all image pairs. For efficient feature matching process we have to confine matching only for overlapping pairs using exterior orientation parameters. When exterior orientation parameters are not available, we cannot determine overlapping pairs. We need another methods for feature matching candidate constitution. In this paper we compare matching candidate constitution methods without exterior orientation parameters, including partial feature matching, Bag-of-keypoints, image intensity method. We use the overlapping pair determination method based on exterior orientation parameter as reference. Experiment results showed the partial feature matching method in the one with best efficiency.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.4
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• pp.91-103
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• 2006

This paper proposes a methodology to evaluate the quality and level of difficulty of fingerprint image databases, which leads to objective evaluation for the performance of fingerprint recognition system. Influencing factors to fingerprint matching are defined and the matching performance between two fingerprint images is evaluated using segmentation and orientation filed. In this study, a hierarchical processing method is proposed to measure an orientation field, which is able to improve the matching speed and accuracy. The results of experiments demonstrate that the defined influencing factors can describe the characteristics of fingerprint databases. Level of difficulty for fingerprint databases enables the performance of fingerprint recognition algorithms to be evaluated and compared even with different databases.

• Korean Journal of Remote Sensing
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• v.12 no.1
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• pp.43-59
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• 1996

The objective of this research is to investigate the potential of relational matching in one of the fundamental photogrammetric processes, that is initial approximation problem. The automatic relative orientation procedures of aerial stereopairs have been investigated. The fact that the existing methods suffer from approximations, distortions (geometric and radiometric), occlusions, and breaklines is the motivation to investigate relational matching which appears to be a much more general solution. An elegant way of solving the initial approximation problem by using distinct(special) relationship from relational description is suggested and experimented. As for evaluation function, the cost function was implemented. The detection of erroneous matching is incorporated as a part of proposed relational matching scheme. Experiments with real urban area images where large numbers of repetitive patterns, breaklines, and occluded areas are present prove the feasibility of implementation of the proposed relational matching scheme. The investigation of relational matching in the domain of image matching problem provides advantages and disadvantages over the existing image matching methods and shows the future area of development and implementation of relational matching in the field of digital photogrammetry.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.34-39
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• 2002

Road information is very important for topographic mapping, transportation application, urban planning and other related application fields. Therefore, automatic detection of road networks from spatial imagery, such as aerial photos and satellite imagery can play a central role in road information acquisition. In this paper, we use least squares correlation matching alone for road center tracking and show that it works. We assumed that (bright) road centerlines would be visible in the image. We further assumed that within a same road segment, there would be only small differences in brightness values. This algorithm works by defining a template around a user-given input point, which shall lie on a road centerline, and then by matching the template against the image along the orientation of the road under consideration. Once matching succeeds, new match proceeds by shifting a matched target window further along road orientation at the target window. By repeating the process above, we obtain a series of points, which lie on a road centerline successively. A 1m resolution IKONOS images over Seoul and Daejeon were used for tests. The results showed that this algorithm could extract road centerlines in any orientation and help in fast and exact he ad-up digitization/vectorization of cartographic images.

• Journal of Information Processing Systems
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• v.8 no.3
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• pp.389-398
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• 2012

Texture is an important visual feature for image analysis. Many approaches have been proposed to model and analyze texture features. Although these approaches significantly contribute to various image-based applications, most of these methods are sensitive to the changes in the scale and orientation of the texture pattern. Because textures vary in scale and orientations frequently, this easily leads to pattern mismatching if the features are compared to each other without considering the scale and/or orientation of textures. This paper suggests an Orientation Matching Scheme (OMS) to ease the problem of mismatching rotated patterns. In OMS, a pair of texture features will be compared to each other at various orientations to identify the best matched direction for comparison. A database including rotated texture images was generated for experiments. A synthetic retrieving experiment was conducted on the generated database to examine the performance of the proposed scheme. We also applied OMS to the similarity computation in a K-means clustering algorithm. The purpose of using K-means is to examine the scheme exhaustively in unpromising conditions, where initialized seeds are randomly selected and algorithms work heuristically. Results from both types of experiments show that the proposed OMS can help improve the performance when dealing with rotated patterns.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.195-198
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• 2001

We proposes a new fingerprint minutia matching algorithm which matches the fingerprint minutiae by using local alignment. In general, fingerprint is deformed by Pressure and orientation when a user presses his fingerprint to the sensor. These nonlinear deformations change the position and the orientation of minutiae which decrease reliability of minutiae. Matching by using global alignment uses one alignment point. But, the problem with this method is that, due to the deformation, matching reliability of a minutia decreases as the distance from the alignment minutia increases. Matching by using local alignment overcomes this problem by considering minutiae which are located in a short distance boundary. Experimental results show that the performance of the proposed algorithm is superior to that of using global alignment.