• Journal of Korean Society for Geospatial Information Science
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• v.5 no.2 s.10
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• pp.99-110
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• 1997

Image matching to determine the conjugate points in stereo photos is the one of the most important subject in digital photogrammetry and many researches In digital photogrammetric field are on going to automate the image matching process. In this study, we analyzes the effect of allowable correlation coefficient, which controls the accuracy in areal based image matching, on the accuracy of digital photogrammetry. So, some areal based matching methods such as image correlation coefficient matching, image Pyramid matching and interest point matching, are implemented, and the effect of allowable correlation coefficient on accuracy of digital photogrammetry in each method is analyzed. As a result of this study, a method to determine the optimal correlation coefficient is presented.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.5
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• pp.443-451
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• 2015

In photogrammetry, GCPs (Ground Control Points) have traditionally been used to determine EOPs (Exterior Orientation Parameters) and to produce DEM (Digital Elevation Model). The existing DEM can be used as GCPs, where the observer’s approach is a difficult area, because it is very restrictive to survey in the field. For this, DEM matching should be performed. This study proposed the fusion method using ICP (Iterative Closest Point) and RT (proposed method by Rosenholm and Torlegard, 1988) in order to improve accuracy of the DEM matching. The proposed method was compared to the ICP method to evaluate its usefulness. Pseudo reference DEM with resolution 10m, and modified DEM (random-numbers are added from 0 to 2 at height; scale is 0.9; translation is 100 meters in 3-D axes; rotation is from 10° to 50° from the reference DEM) were used in the experiment. The results proposed accuracy was highest in the matching and absolute orientation. In the case of ICP, according to rotation of the modified DEM being increased, absolute orientation error is increased, while the proposed method generally showed consistent results without increasing the error. The proposed method would be applied to matching when the DEM is modified up to 30° rotation, compared to the reference DEM, based on the results of experiments. In addition when we use Drone, this method can be utilized to identify EOPs or detect 3-D surface deformation from the existing DEM of the inaccessible area.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.6
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• pp.4158-4163
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• 2015

In this paper, the solutions of TE(transverse electric) scattering problems by a condutive strip grating over a dielectric layer are analyzed by using the FGMM(fourier galerkin moment method) and PMM(point matching method) known as a numerical method of electromagnetic fileld. The scattered electromagnetic fields are expanded in a series of floguet mode functions, the boundary conditions are applied to obtain the unknown field coefficients, and the conductive boundary condition is used for the relationship between the tangential electric field and the induced surface current density on the strip. The numerical results for the reflected and transmitted power of zeroth mode analyzed by according as the width and spacing of conductive strip, the relative permittivity and thickness of dielectric layer, and incident angles. Generally, according to the relative permittivity of dielectric layer increased, also the normalized reflected power of zeroth mode increased. To examine the accruacy of this paper, the numerical results of FGMM shown in good agreement compared to those of PMM.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.505-510
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• 2016

This paper presents a searching method of corresponding points robust to rotational error for scan-matching used for SLAM(Simultaneous Localization and Mapping) in mobile robot. A differential driving mechanism is one of the most popular type for mobile robot. For driving curved path, this type controls the velocities of each two wheels independently. This case increases a wheel slip of the mobile robot more than the case of straight path driving. And this is the reason of a drifting problem. To handle this problem and improves the performance of scan-matching, this paper proposes a searching method of corresponding points using extraction of a closest point based on rotational radius of the mobile robot. To verify the proposed method, the experiment was conducted using LRF(Laser Range Finder). Then the proposed method is compared with an existing method, which is an existing method based on euclidian closest point. The result of our study reflects that the proposed method can improve the performance of searching corresponding points.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1025-1034
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• 2022

In this paper, we propose a post-processing method through interpolation of hole regions that occur when extracting point clouds. When image matching is performed on stereo image data, holes occur due to occlusion and building façade area. This area may become an obstacle to the creation of additional products based on the point cloud in the future, so an effective processing technique is required. First, an initial point cloud is extracted based on the disparity map generated by applying stereo image matching. We transform the point cloud into a grid. Then a hole area is extracted due to occlusion and building façade area. By repeating the process of creating Triangulated Irregular Network (TIN) triangle in the hall area and processing the inner value of the triangle as the minimum height value of the area, it is possible to perform interpolation without awkwardness between the building and the ground surface around the building. A new point cloud is created by adding the location information corresponding to the interpolated area from the grid data as a point. To minimize the addition of unnecessary points during the interpolation process, the interpolated data to an area outside the initial point cloud area was not processed. The RGB brightness value applied to the interpolated point cloud was processed by setting the image with the closest pixel distance to the shooting center among the stereo images used for matching. It was confirmed that the shielded area generated after generating the point cloud of the target area was effectively processed through the proposed technique.

• International journal of advanced smart convergence
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• v.6 no.3
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• pp.17-21
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• 2017

In this paper, the service execution accuracy was compared by ontology based rule inference method and machine learning method, and the amount of data at the point when the service execution accuracy of the machine learning method becomes equal to the service execution accuracy of the rule inference was found. The rule inference, which measures service execution accuracy and service execution accuracy using accumulated data and pattern matching on service results. And then machine learning method measures service execution accuracy using cross validation data. After creating a confusion matrix and measuring the accuracy of each service execution, the inference algorithm can be selected from the results.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.1002-1005
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• 2003

This paper presents a new template matching method for finding facial feature points. Instead of matching each template to its corresponding feature point separately the present method matches a set of element templates simultaneously. The set of templates can be placed on the space predefined by the geometrical characteristics of human faces. As a result, the search area for template matching is very small compared with a conventional method. This makes the presented method very robust and accurate. Experiment results show that the presented method results in good performance In various illuminance environments and poses.

• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.800-808
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• 2008

A fingerprint verification system based on a set of invariant moment features and a nonlinear Back Propagation Neural Network(BPNN) verifier is proposed. An image-based method with invariant moment features for fingerprint verification is used to overcome the demerits of traditional minutiae-based methods and other image-based methods. The proposed system contains two stages: an off-line stage for template processing and an on-line stage for testing with input fingerprints. The system preprocesses fingerprints and reliably detects a unique reference point to determine a Region-of-Interest(ROI). A total of four sets of seven invariant moment features are extracted from four partitioned sub-images of an ROI. Matching between the feature vectors of a test fingerprint and those of a template fingerprint in the database is evaluated by a nonlinear BPNN and its performance is compared with other methods in terms of absolute distance as a similarity measure. The experimental results show that the proposed method with BPNN matching has a higher matching accuracy, while the method with absolute distance has a faster matching speed. Comparison results with other famous methods also show that the proposed method outperforms them in verification accuracy.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.8
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• pp.640-646
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• 2003

We presents an edge-based point correlation algorithm for fast and stable visual inspection system. Conventional algorithms based on NGC(normalized gray-level correlation) have to overcome some difficulties in applying automated inspection systems to real factory environment. First of all, NGC algorithms involve highly complex computation and thus require high performance hardware for realtime process. In addition, lighting condition in realistic factory environments is not stable and therefore intensity variation from uncontrolled lights gives many troubles for applying NGC directly as pattern matching algorithm. We propose an algorithm to solve these problems, using thinned and binarized edge data, which are obtained from the original image. A point correlation algorithm with the thinned edges is introduced with image pyramid technique to reduce the computational complexity. Matching edges instead of using original gray-level image pixels overcomes problems in NGC method and pyramid of edges also provides fast and stable processing. All proposed methods are proved by the experiments using real images.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1135-1147
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• 2021

In order to geometrically correct high-resolution satellite imagery, the sensor modeling process that restores the geometric relationship between the satellite sensor and the ground surface at the image acquisition time is required. In general, high-resolution satellites provide RPC (Rational Polynomial Coefficient) information, but the vendor-provided RPC includes geometric distortion caused by the position and orientation of the satellite sensor. GCP (Ground Control Point) is generally used to correct the RPC errors. The representative method of acquiring GCP is field survey to obtain accurate ground coordinates. However, it is difficult to find the GCP in the satellite image due to the quality of the image, land cover change, relief displacement, etc. By using image maps acquired from various sensors as reference data, it is possible to automate the collection of GCP through the image matching algorithm. In this study, the RPC of KOMPSAT-3A satellite image was corrected through the extracted matching point using the UAV (Unmanned Aerial Vehichle) imagery. We propose a pre-porocessing method for the extraction of matching points between the UAV imagery and KOMPSAT-3A satellite image. To this end, the characteristics of matching points extracted by independently applying the SURF (Speeded-Up Robust Features) and the phase correlation, which are representative feature-based matching method and area-based matching method, respectively, were compared. The RPC adjustment parameters were calculated using the matching points extracted through each algorithm. In order to verify the performance and usability of the proposed method, it was compared with the GCP-based RPC correction result. The GCP-based method showed an improvement of correction accuracy by 2.14 pixels for the sample and 5.43 pixelsfor the line compared to the vendor-provided RPC. In the proposed method using SURF and phase correlation methods, the accuracy of sample was improved by 0.83 pixels and 1.49 pixels, and that of line wasimproved by 4.81 pixels and 5.19 pixels, respectively, compared to the vendor-provided RPC. Through the experimental results, the proposed method using the UAV imagery presented the possibility as an alternative to the GCP-based method for the RPC correction.