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We propose a novel image stitching method using multiple homographies. The method can stitch images having different parallaxes, such as an image that contains distant buildings and close trees. Such images might not be stitched with fine quality by single global homography. Therefore, we select a background homography by analyzing the inliers of the homographies estimated by RANSAC (random sample...
In this paper, we propose an efficient image stitching using structure deformation. We use image stitching based on common stitching algorithms such as speeded up robust features (SURF) feature detection, approximated nearest neighbor (ANN) matching and random sample consensus (RANSAC) parameter estimation. And we use homography similarity to identify if input images have enough correlation. To reduce...
In this paper, we propose a novel resolution enhancement method for cloud networks by selective interpolation. Resolution up-sampling algorithms for high quality should require high complex processing. To realize real time services, we propose the up-sampling method processed in cloud and client by off-loading. In the experimental results, the efficiency of the proposed method is shown by the comparison...
We propose a novel method that detects moving objects in depth image sequences using background images and motion depth (MD) values. The background image represents the camera view with no moving objects and the MDs are the depth values of moving objects. Foreground regions can be easily detected by background subtraction; however, the foreground regions have some noise and do not contain a regional...
In this paper, we propose new method of Motion compensated frame interpolation (MCFI) using accumulated weighted sum. Block artifacts observed and the motion regions may not be correctly compensated in conventional MCFI methods. To prevent these drawbacks, we proposed a method that uses motion vectors of whole pixels and weighted sums, and the interpolation values determined by the representative...
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