• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.709-712
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• 2005

Ground processing for geostationary weather satellite such as GOES, MTSAT includes the process called image navigation. Image navigation means the retrieval of satellite navigational parameters from images and requires landmark detection by matching satellite images against landmark chips. For an automated preprocessing, a matching must be performed automatically. However, if match results contain errors, the accuracy of image navigation deteriorates. To overcome this problem, we propose the use of a robust estimation technique, called Random Sample Consensus (RANSAC), to automatically detect mismatches. We tested GOES-9 satellite images with 30 landmark chips. Landmark chips were extracted from the world shoreline database. To them, matching was applied and mismatch results were detected automatically by RANSAC. Results showed that all mismatches were detected correctly by RANSAC with a threshold value of 2.5 pixels.

• Journal of Korea Multimedia Society
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• v.12 no.6
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• pp.785-793
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• 2009

This paper proposes a signature using dominant singular values for video sequence matching. By considering the input image as matrix A, a partition procedure is first performed to separate the matrix into non-overlapping sub-images of a fixed size. The SVD(Singular Value Decomposition) process decomposes matrix A into a singular value-singular vector factorization. As a result, singular values are obtained for each sub-image, then k dominant singular values which are sufficient to discriminate between different images and are robust to image size variation, are chosen and normalized as the signature for each block in an image frame for matching between the reference video clip and the query one. Experimental results show that the proposed video signature has a better performance than ordinal signature in ROC curve.

• The KIPS Transactions:PartB
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• v.12B no.3 s.99
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• pp.357-364
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• 2005

In this paper, we propose an accurate and fast keyword spotting system for searching user-specified keyword in Hangul document images by using two-level image-to-image matching. The system is composed of character segmentation, creating a query image, feature extraction, and matching procedure. Two different feature vectors are used in the matching procedure. An experiment using 1600 Hangul word images from 8 document images, downloaded from the website of Korea Information Science Society, demonstrates that the proposed system is superior to conventional image-based document retrieval systems.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.683-688
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• 2009

The purposes of this paper is to propose new techniques to reconstruct measured optical images by using the template matching and B-Spline interpolation method based on image processing technology. To do this, we detect the matching template and non-matching template from each optical image. And then, we match the overlaped images from base level by correcting roll, pitch, and yaw error of images. At last, the matching image is interpolated by B-Spline interpolation. Finally, we show the effectiveness and feasibility of the proposed method through some experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.2 no.6
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• pp.280-298
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• 2008

For efficient content-based image retrieval, diverse visual features such as color, texture, and shape have been widely used. In the case of leaf images, further improvement can be achieved based on the following observations. Most plants have unique shape of leaves that consist of one or more blades. Hence, blade-based matching can be more efficient than whole shape-based matching since the number and shape of blades are very effective to filtering out dissimilar leaves. Guaranteeing rotational invariance is critical for matching accuracy. In this paper, we propose a new shape representation, indexing and matching scheme for leaf image retrieval. For leaf shape representation, we generated a distance curve that is a sequence of distances between the leaf’s center and all the contour points. For matching, we developed a blade-based matching algorithm called rotation invariant - partial dynamic time warping (RI-PDTW). To speed up the matching, we suggest two additional techniques: i) priority queue-based pruning of unnecessary blade sequences for rotational invariance, and ii) lower bound-based pruning of unnecessary partial dynamic time warping (PDTW) calculations. We implemented a prototype system on the GEMINI framework [1][2]. Using experimental results, we showed that our scheme achieves excellent performance compared to competitive schemes.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.04a
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• pp.209-214
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• 2004

This paper presents a RFM-based image matching algorithm which put constraints on the search space through the object-space approach. Also, the detail procedure of generating 3-D surface models from the RFM is introduced as an end-user point of view. The proposed algorithm provides the PML (Piecewise Matching Line) for image matching and reduces the search space to within the confined line-shape area.

• Progress in Medical Physics
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• v.14 no.3
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• pp.189-195
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• 2003

The comparative analysis of a portal image and a simulation image is a very important process in radiotherapy for verifying the accuracy of an actual treatment field. In this study, we applied a chamfer-matching algorithm to compare a portal image with a simulation image and verified the accuracy of the algorithm to analyze the field matching error in the portal image. We also developed an analysis program that could analyze the two images more effectively with a chamfer-matching method and demonstrated its efficacy through a feasibility study. With virtual portal images, the accuracy of the analysis algorithm were acceptable considering the average error of shift (0.64 mm), rotation (0.32$^{\circ}$), and scale (1.61%). When the portal images of a head and neck phantom were analyzed, the accuracy and suitability of the developed analysis program was proven considering the acceptable average error of shift (1.55 mm), rotation (0.80$^{\circ}$), and scale (1.72%). We verified the applicability of a chamfer-matching algorithm to the comparative analysis of a portal image with a simulation image. The analysis program developed in this study was a practical tool to calculate the quantitative error of the treatment field in a portal image.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.182-187
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• 1999

Stereo matching is one of the most crucial parts in DEM generation. Naive stereo matching algorithms often create many holes and blunders in a DEM and therefore a carefully designed strategy must be employed to guide stereo matching algorithms to produce “good” 3D information. In this paper, we describe one such a strategy designed by the use of scene geometry, in particular, the epipolarity for generation of a DEM from linear pushbroom images. The epipolarity for perspective images is a well-known property, i.e., in a stereo image pair, a point in the reference image will map to a line in the search image uniquely defined by sensor models of the image pair. This concept has been utilized in stereo matching by applying epipolar resampling prior to matching. However, the epipolar matching for linear pushbroom images is rather complicated. It was found that the epipolarity can only be described by a Hyperbola- shaped curve and that epipolar resampling cannot be applied to linear pushbroom images. Instead, we have developed an algorithm of incorporating such epipolarity directly in least squares correlation matching. Experiments showed that this approach could improve the quality of a DEM.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1257-1271
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• 2022

Recently, many Earth observation optical satellites have been developed, as their demands were increasing. Therefore, a rapid preprocessing of satellites became one of the most important problem for an active utilization of satellite images. Satellite image matching is a technique in which two images are transformed and represented in one specific coordinate system. This technique is used for aligning different bands or correcting of relative positions error between two satellite images. In this paper, we propose an automatic image matching method among satellite images with different ground sampling distances (GSDs). Our method is based on improved feature matching and robust estimation of transformation between satellite images. The proposed method consists of five processes: calculation of overlapping area, improved feature detection, feature matching, robust estimation of transformation, and image resampling. For feature detection, we extract overlapping areas and resample them to equalize their GSDs. For feature matching, we used Oriented FAST and rotated BRIEF (ORB) to improve matching performance. We performed image registration experiments with images KOMPSAT-3A and RapidEye. The performance verification of the proposed method was checked in qualitative and quantitative methods. The reprojection errors of image matching were in the range of 1.277 to 1.608 pixels accuracy with respect to the GSD of RapidEye images. Finally, we confirmed the possibility of satellite image matching with heterogeneous GSDs through the proposed method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.4
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• pp.407-414
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• 2008

This study introduces an automatic image matching algorithm that can be applied for the scale different image pairs consisting of the satellite pushbroom images and the aerial frame images. The proposed method is based on several image processing techniques such as pre-processing, filtering, edge thinning, interest point extraction, and key-descriptor matching, in order to enhance the matching accuracy and the processing speed. The proposed method utilizes various characteristics, such as the different geometry of image acquisition and the different radiometric characteristics, of the multi-sensor images. In addition, the suggested method uses the sensor model to minimize search area and eliminate false-matching points automatically.