• Korean Journal of Applied Biomechanics
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• v.25 no.1
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• pp.1-10
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• 2015

Objective : The purpose of this study was to find some kinetic variable's relationships between personal records and low records in female high jump. Methods : Collected data of the subjects(N=8, ages: $25.5{\pm}1.85$, height: $173{\pm}5.83$, mass: $54.75{\pm}6.36$ personal record: $1.71{\pm}0.04$, low record: $1.62{\pm}0.03$) were used for the last three strides and take-off phase. Five video cameras set in 30frames/s were used for recording. After digitizing motion, the Direct Linear Transformation(DLT) technique was employed to obtain 3-D position coordinates. The kinematic and kinetic factors of distance, velocity, angle, impulse, jerk variables were calculated. A paired t-test was applied for the difference of variables between personal records and lower records and for correlation with performances and variables. The significance level was accepted at p<.05. Results : There was no relationship between pattern of stride and performance. However, rate of change of velocity was related with cental of mass height(CMH) at peak point(PP). Knee, hip, backward lean, foot plant, approach and take off angle showed no difference between best record and low record. Vertical impulse momentum also showed no difference between performances. Conclusion : According to a t-test result, there were significant differences in CMH at PP and jerk at touch down between best record and low record.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.6
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• pp.625-635
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• 2014

Multiple strips are required for large area mapping using ALS (Airborne Laser Scanner) system. LiDAR (Light Detection And Ranging) data collected from the ALS system has discrepancies between strips due to systematic errors of on-board laser scanner and GPS/INS, inaccurate processing of the system calibration as well as boresight misalignments. Such discrepancies deteriorate the overall geometric quality of the end products such as DEM (Digital Elevation Model), building models, and digital maps. Therefore, strip adjustment for minimizing discrepancies between overlapping strips is one of the most essential tasks to create seamless point cloud data. This study implemented area-based matching (ABM) to determine conjugate features for computing 3D transformation parameters. ABM is a well-known method and easily implemented for this purpose. It is obvious that the exact same LiDAR points do not exist in the overlapping strips. Therefore, the term "conjugate point" means that the location of occurring maximum similarity within the overlapping strips. Coordinates of the conjugate locations were determined with sub-pixel accuracy. The major drawbacks of the ABM are sensitive to scale change and rotation. However, there is almost no scale change and the rotation angles are quite small between adjacent strips to apply AMB. Experimental results from this study using both simulated and real datasets demonstrate validity of the proposed scheme.

• Journal of Biosystems Engineering
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• v.28 no.1
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• pp.65-76
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• 2003

The analysis of cow body parameters is important to provide some useful information fur cow management and cow evaluation. Present methods give many stresses to cows because they are invasive and constrain cow postures during measurement of body parameters. This study was conducted to develop the stereo vision system fur non-invasive analysis of cow body features. Body feature parameters of 16 heads at two farms(A, B) were measured using scales and nineteen stereo images of them with walking postures were captured under outdoor illumination. In this study, the camera calibration and inverse perspective transformation technique was established fer the stereo vision system. Two calibration results were presented for farm A and fm B, respectively because setup distances from camera to cow were 510 cm at farm A and 630cm at farm B. Calibration error values fer the stereo vision system were within 2 cm for farm A and less than 4.9 cm for farm B. Eleven feature points of cow body were extracted on stereo images interactively and five assistant points were determined by computer program. 3D world coordinates for these 15 points were calculated by computer program and also used for calculation of cow body parameters such as withers height. pelvic arch height. body length. slope body length. chest depth and chest width. Measured errors for body parameters were less than 10% for most cows. For a few cow. measured errors for slope body length and chest width were more than 10% due to searching errors fer their feature points at inside-body positions. Equation for chest girth estimated by chest depth and chest width was presented. Maximum of estimated error fur chest girth was within 10% of real values and mean value of estimated error was 8.2cm. The analysis of cow body parameters using stereo vision system were successful although body shape on the binocular stereo image was distorted due to cow movements.

• Journal of Korean Society for Geospatial Information Science
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• v.1 no.1 s.1
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• pp.141-152
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• 1993

This study aims to generate digital elevation model from digital satellite imagery. Digital elevation model is being increasingly used for geo-spatial information system database development and for digital map production. Image matching technique was applied to acquire conjugate image coordinates and the algorithm for digital elevation model generation is presented in this study The exterior orientation parameters of the satellite imagery is determined by bundle adjustment and standard correlation was applied for image matching conjugate of image points. The window as well as the searching area have to be defined in image matching. Different sizes of searching area were tested to study the appropriate size of the searching area. Various coordinate transformation methods were applied to improve the computation speed as well as the geometric accuracy. The results were then statistically analysed after which the searching area is determined with the safety factor. To evaluate the accuracy of digital elevation model, 3-D coordinates were extracted from 1/5000 scale topographic map and this was compared to the digital elevation model generated from satellite imagery. The algorithm for generation of digital elevation model generated from satellite imagery is presented in this study which will prove effective in the database development of geo-spatial information system and in digital elevation modelling of large areas.

• Journal of Korean Society for Geospatial Information Science
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• v.2 no.1 s.3
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• pp.121-129
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• 1994

A number of existing cadastral control stations have been destroyed and shifted by a long lapse of time and careless management. Also, results of them are partly poor owing to dependence on conventional survey method. Because of these, it is very difficult in use of results. Hereupon, correction of cadastral results is necessary in level of government. But it is very consumable to check and adjust results with existing equipments and related techniques only. It is required that this problem can be resolved efficiently. This study analyzed the conformability for cadastral control network to GPS, GPS, which determine precise 3-D coordinates on a short time, to positioning of cadastral stations. We chose DaeJon city for the test area of this study and analyzed the precision of network composed of sixteen cadastral control stations. We made comparision the old result and the new outcome which obtained from coordinate transformation method and horizontal network adjustment method. As a result of this, we detected the blunder of cadastral stations. Furthermore, we suggested effective network type according to precision analysis of GPS observation network. Therefore, there is no doubt that GPS surveying can be applied to checking and adjustment of cadastral control network. Hereafter, it is expected that the practical use of GPS is advanced in a field of cadastration.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.387-396
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• 2024

Most very high-resolution (VHR) satellite images provide rational polynomial coefficients (RPC) data to facilitate the transformation between ground coordinates and image coordinates. However, initial RPC often contains geometric errors, necessitating correction through matching with ground control points (GCPs). A GCP chip is a small image patch extracted from an orthorectified image together with height information of the center point, which can be directly used for geometric correction. Many studies have focused on area-based matching methods to accurately align GCP chips with VHR satellite images. In cases with seasonal differences or changed areas, edge-based algorithms are often used for matching due to the difficulty of relying solely on pixel values. However, traditional edge extraction algorithms,such as canny edge detectors, require appropriate threshold settings tailored to the spectral characteristics of satellite images. Therefore, this study utilizes deep learning-based edge information that is insensitive to the regional characteristics of satellite images for matching. Specifically,we use a pretrained pixel difference network (PiDiNet) to generate the edge maps for both satellite images and GCP chips. These edge maps are then used as input for normalized cross-correlation (NCC) and relative edge cross-correlation (RECC) to identify the peak points with the highest correlation between the two edge maps. To remove mismatched pairs and thus obtain the bias-compensated RPC, we iteratively apply the data snooping. Finally, we compare the results qualitatively and quantitatively with those obtained from traditional NCC and RECC methods. The PiDiNet network approach achieved high matching accuracy with root mean square error (RMSE) values ranging from 0.3 to 0.9 pixels. However, the PiDiNet-generated edges were thicker compared to those from the canny method, leading to slightly lower registration accuracy in some images. Nevertheless, PiDiNet consistently produced characteristic edge information, allowing for successful matching even in challenging regions. This study demonstrates that improving the robustness of edge-based registration methods can facilitate effective registration across diverse regions.