• Journal of Korea Multimedia Society
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• v.16 no.9
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• pp.1057-1066
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• 2013

Personal verification using finger-knuckle-print(FKP) uses lines and creases at the finger-knuckle area, so the orientation information of texture is an important feature. In this paper, we propose an effective FKP verification method which extracts keypoints using SIFT algorithm and matches the keypoints by vector similarity. The vector is defined as a direction vector which connects a keypoint extracted from a query image and a corresponding keypoint extracted from a reference image. Since the direction vector is created by a pair of local keypoints, the direction vector itself represents only a local feature. However, it has an advantage of expanding a local feature to a global feature by comparing the vector similarity among vectors in two images. The experimental results show that the proposed method is superior to the previous methods based on orientation codes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.4
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• pp.854-860
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• 2004

The result of fingerprint matching is different as the quality or the state of input fingerprint image. We can extract the false direction information when the quality of fingerprint is degraded by the noise or the scars of the ridges. The information of the direction is very important for the elimination of the false minutia, the measurement of the ridge distance, matching, finding of cores and deltas. We need the method which can compensate or correct the false information of directions because the false directions include serious errors in the fingerprint recognition. We propose a method which can compensate or correct the false direction of fingerprint with a \alpha$-trimmed mean filter followed by LPF to reconstruct the false direction information when fingerprints are corrupted by scars.

• Asia pacific journal of information systems
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• v.7 no.1
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• pp.49-66
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• 1997

Matching individual career desire and career path is desirable for individual motivation and organizational effectiveness. Some studies reported that major career orientations of MIS personnel are technical and managerial orientation. Furthermore, dual ladder system is based on the same notion above. However, subsequent research conclusions of this issues have been contradictory. Mnwhile, past studies on the work value of MIS personnel are mainly concentrated on the differences between MIS personnel and other occupations. To date, the study on the differences in the values of job factors between major career orientations is rare. The purpose of this study is to find out major career orientation of Korean MIS personnel and the differences of values of job factors between major orientations. The research result is two conclusions. First, major career orientations of Korean MIS personnel are technical and managerial orientation. Second, both group do not have different values about job property related factors, but managerial orientation group gives high values to the some organization related factors such as company policy, responsibility, authority, salary level and opportunity of promotion.

• Journal of Biomedical Engineering Research
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• v.29 no.6
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• pp.431-435
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• 2008

Quantitative information of a three dimensional(3D) kinematics of joint is very useful in knee joint surgery, understanding how knee kinematics related to joint injury, impairment, surgical treatment, and rehabilitation. In this paper, an automated 2D/3D image matching technique was developed to estimate the 3D in vivo knee kinematics using dual X-ray images. First, a 3D geometric model of the knee was reconstructed from CT scan data. The 3D in vivo position and orientation of femoral and tibial components of the knee joint could be estimated by minimizing the pixel by pixel difference between the projection images from the developed 3D model and the given X-ray images. The accuracy of the developed technique was validated by an experiment with a cubic phantom. The present 2D/3D image matching technique for the estimation of in vivo joint kinematics could be useful for pre-operative planning as well as post-operative evaluation of knee surgery.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.166-169
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• 2017

Currently, studies using Deep Learning are actively carried out showing good results in many fields. A template matching method is mainly used to recognize parts mounted on PCB(Printed Circuit Board). However, template matching should have multiple templates depending on the shape, orientation and brightness. And it takes long time to perform matching because it searches for the entire image. And there is also a disadvantage that the recognition rate is considerably low. In this paper, we use the Faster-RCNN method for recognizing PCB components as machine learning for classifying several objects in one image. This method performs better than the template matching method, execution time and recognition.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.5
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• pp.331-341
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• 2019

With the popularity of sensor-rich environments, smartphones have become one of the major platforms for obtaining and sharing information. Since it is difficult to utilize GNSS (Global Navigation Satellite System) inside the area with many buildings, the localization of smartphone in this case is considered as a challenging task. To resolve problem of localization using smartphone a four step image-based localization method and procedure is proposed. To improve the localization accuracy of smartphone datasets, MMS (Mobile Mapping System) and Google Street View were utilized. In our approach first, the searching for candidate matching image is performed by the query image of smartphone's using GNSS observation. Second, the SURF (Speed-Up Robust Features) image matching between the smartphone image and reference dataset is done and the wrong matching points are eliminated. Third, the geometric transformation is performed using the matching points with 2D affine transformation. Finally, the smartphone location and attitude estimation are done by PnP (Perspective-n-Point) algorithm. The location of smartphone GNSS observation is improved from the original 10.204m to a mean error of 3.575m. The attitude estimation is lower than 25 degrees from the 92.4% of the adjsuted images with an average of 5.1973 degrees.

• Korean Journal of Remote Sensing
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• v.19 no.6
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• pp.431-445
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• 2003

In this paper, we experimentally investigated the precision of 3D positioning using 4S-Van images in photograrmmetric perspective. The 3D calibration target was built over building facade outside and was captured separately by two CCD cameras installed in 4S-Van. After then, we determined the interior orientation parameter for each CCD camera through self-calibration technique. With the interior orientation parameter computed, the bundle adjustment was performed to obtain the exterior orientation parameters simultaneously for two CCD cameras using calibration target image and object coordinates. The reverse lens distortion coefficients were computed and acquired by least squares method so as to introduce lens distortion into epipolar line. It was shown that the reverse lens distortion coefficients could transform image coordinates into lens distorted image coordinates within about 0.5 pixel. The proposed semi-automatic matching scheme incorporated with lens distorted epipolar line was implemented with scene images captured by 4S-Van in moving. The experimental results showed that the precision of 3D positioning from 4S-Van images in photograrmmetric perspective is within 2cm in the range of 20m from the camera.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.555-562
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• 2019

This paper presents soil deformation measurement in model chamber based on photogrammetry. We created an aluminum framed acrylic model chamber with soil inside and applied photogrammetry to measure soil deformation caused by loading tests. The soil consists of 40% black and 60% regular sand to create image contrast in soil images. In preprocessing, the self camera calibration was carried out for IOPs (Interior Orientation Parameters), followed by the space resection to estimate EOPs (Exterior Orientation Parameters) using control points located along the aluminum frame. Image matching was applied to measure the soil displacement. We tested different matching window sizes and the effect of image smoothing. Experimental results showed that 65x65 pixels of window size produced better soil deformation map and the image smoothing was useful to suppress the matching outliers. In conclusion, photogrammetry was able to efficiently generated soil deformation map.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.9 no.2
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• pp.93-102
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• 1991

This study aims to develop techniques for generating Digital Elevation Model(DEM) from SPOT Computer Compatible Tape(CCT) data, so as to present an effective way of generation of DEM for large area. As the first phase of extracting ground heights from SPOT stereo digital data, the bundle adjustment technique was used to determine the satellite exterior orientation parameters. Because SPOT data has the characteristics of multiple perspective projection, exterior orientation Parameters were modelled as a function of scan lines. In the second phase, a normalized cross correlation matching technique was applied to search for the conjugate pixels ill stereo pairs. The preliminary study showed that the matching window size of 13$\times$13 was adequate. After image coordinates of the conjugate pixels were determined by the matching technique, the ground coordinates of the corresponding pixels were calculated by the space intersection method. Then DEM was generated by interpolations. In addtion an algorithm for the elimination of abnormal elevation was developed and applied. The algorithm was very effective to improve the accuracy of the generated DEM.

• Journal of the Korea Society of Computer and Information
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• v.14 no.12
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• pp.63-73
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• 2009

This paper presents a vehicle detection method that uses convolution matching method based on a simple color information. An input image is expressed as 8 oriented color expression(Red, Green, Blue, White, Black, Cyan, Yellow, Magenta) considering an orientation of a pixel color vector. It makes the image very reliable and strong against changes of illumination condition or environment. This paper divides the vehicle detection into a hypothesis generation step and a hypothesis verification step. In the hypothesis generation step, the vehicle candidate region is found by vertical edge and shadow. In the hypothesis verification step, the convolution matching and the complexity of image edge are used to detect real vehicles. It is proved that the proposed method has the fast and high detection rate on various experiments where the illumination source and environment are changed.