• Spatial Information Research
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• v.22 no.1
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• pp.35-43
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• 2014

The Triangle Mesh Warping method is suggested and applied in coordinate transformation to world geodetic system in this study. The common points of Uiwang city are used to compare the transformation accuracy of the suggested methods with existing national coordinate transformation methods. As a result, the Triangle Mesh Warping method was satisfied with accuracy criteria for positioning on a map larger than scale 1/1,000 with smaller number of common points and without distortion modeling. Additionally, in case of Guri and Pyeongtaek city that established the World Geodetic System, the suggested method generates the result of transformation accuracy better than 5cm. Based on the test, it was found that the suggested method improves the problem of securing many common points and reduces the problem of mis-match between the transformed data of adjacent areas. Accordingly, for transformation of large-scale topographic map, cadastral map, GIS DB and serial cadastral map to the World Geodetic System, it is judged that the Triangle Mesh Warping would be a good method for economical efficiency and accuracy using by minimum common point.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.10
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• pp.2112-2122
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• 1997

This paper presents a new video codec which is based on 2-D triangular mesh-based motion compensation and two step grid point motion estimation. With this approach the efficiency of compression and the quality of reconstructed images are improved. The compensation of motion for each triangular patch is performed by image warping using motion vectors at the grid points. The prediction error coding and the rate control meet MPEG-4 VM 3.0 specification. The experimental results show that the codec system proposed is simple in complexity and moreover, the quality of decoded images is improved.

• Journal of the Korea Computer Graphics Society
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• v.25 no.3
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• pp.123-131
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• 2019

In this study, we modified CT images of femoral head in consideration of anatomically meaningful structure, proposing the method to augment the training data of convolution Neural network for segmentation of femur mesh model. First, the femur mesh model is obtained from the CT image. Then divide the mesh model into meaningful parts by using cluster analysis on geometric characteristic of mesh surface. Finally, transform the segments by using an appropriate mesh deformation algorithm, then create new CT images by warping CT images accordingly. Deep learning models using the data enhancement methods of this study show better image division performance compared to data augmentation methods which have been commonly used, such as geometric conversion or color conversion.