• Journal of the Korea Society of Computer and Information
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• v.17 no.7
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• pp.53-65
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• 2012

Existing method of creating Stereoscopy image, creates viewpoint image from the left and right by shooting one object with 2 lens in certain distance. However, in case of 3-D TV using Stereoscopy camera, the necessity to transmit 2 viewpoint images from the left and right simultaneously, increases the amount of bandwidth. Various and more effective alternatives are under discussion. Among the alternatives, DIBR(Depth Image Based Rendering) creates viewpoint images from the left and right using one image and its Depth information, thus decreasing the amount of transmitted bandwidth. For this reason, there have been various studies on Algorithm to create DIBR Image in existing Static Scene. In this paper, I would like to suggest Gaussian Hole-filling solution, which utilizes reverse-depth image to fill the hole naturally, while minimizing distortion of background. In addition, we have analyzed the effectiveness of each Algorithm by comparing and calculating its functions.

• Journal of IKEEE
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• v.9 no.1 s.16
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• pp.7-18
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• 2005

A key component of an autonomous mobile robot is to localize itself accurately and build a map of the environment simultaneously. In this paper, we propose a vision-based mobile robot localization and mapping algorithm using scale-invariant features. A camera with fisheye lens facing toward to ceiling is attached to the robot to acquire high-level features with scale invariance. These features are used in map building and localization process. As pre-processing, input images from fisheye lens are calibrated to remove radial distortion then labeling and convex hull techniques are used to segment ceiling region from wall region. At initial map building process, features are calculated for segmented regions and stored in map database. Features are continuously calculated from sequential input images and matched against existing map until map building process is finished. If features are not matched, they are added to the existing map. Localization is done simultaneously with feature matching at map building process. Localization. is performed when features are matched with existing map and map building database is updated at same time. The proposed method can perform a map building in 2 minutes on $50m^2$ area. The positioning accuracy is ${\pm}13cm$, the average error on robot angle with the positioning is ${\pm}3$ degree.