• Cartoon and Animation Studies
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• s.33
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• pp.503-529
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• 2013

The commemorative AR postage stamps which are the world first presented at The YEOSU EXPO 2012 has had meaning of communicating with future in this present from a convergence that the most analog medium is using now and that the AR is cutting edge of digital technology. The AR stamps printed 10 kind out of 33 commemorative stamps. These have great significance that is artistic value than that is world first. The applied AR images are not only expressed 3D real images but also artic represented and signifying each stamp images from visualized creativity process, and build 'new art space' that is new concept between on real(analog) and virtual(digital). This study analyzes meaning of images and then makes concept of AR contents design. The processing is designed and considered the meaning of architectures and environments, and the regional specific feature of the Yeosu with surrealistic graphic concept. The 10 of deducted images were expressed after AR coding such as visual arts. This study realized markerless 3D image tracking AR stamps and deducted research result are; the first, it was able to figure out how to realize AR in the process of registering the reference images, coordinating transformation, and hybriding AR on the stamps for the mobile devices. The second, it was able to be seeked a possibility of new virtual exhibition space. The third, it was able to know possibility of satisfaction of immersing with visual formativeness and usability with informativity.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.6
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• pp.315-320
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• 2017

Estimating the correct pose of augmented objects on the real camera view efficiently is one of the most important questions in image tracking area. In computer vision, Homography is used for camera pose estimation in augmented reality system with markerless. To estimating Homography, several algorithm like SURF features which extracted from images are used. Based on extracted features, Homography is estimated. For this purpose, RANSAC algorithm is well used to estimate homography and DCS-RANSAC algorithm is researched which apply constraints dynamically based on Constraint Satisfaction Problem to improve performance. In DCS-RANSAC, however, the dataset is based on pattern of feature distribution of images manually, so this algorithm cannot classify the input image, pattern of feature distribution is not recognized in DCS-RANSAC algorithm, which lead to reduce it's performance. To improve this problem, we suggest the KCS-RANSAC algorithm using K-means clustering in CS-RANSAC to cluster the images automatically based on pattern of feature distribution and apply constraints to each image groups. The suggested algorithm cluster the images automatically and apply the constraints to each clustered image groups. The experiment result shows that our KCS-RANSAC algorithm outperformed the DCS-RANSAC algorithm in terms of speed, accuracy, and inlier rate.