Panoramic image stitching using SIFT
This report describes how SIFT keypoints are obtained by using Difference-of-Gaussian for detecting local maxima and minima and methods to filter detected local features in order to obtain stable keypoints. The magnitudes of gradient and orientations are added to each keypoint before creating each k...
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sg-ntu-dr.10356-178222023-07-07T15:47:24Z Panoramic image stitching using SIFT Nar, Soon Keong Chua Chin Seng School of Electrical and Electronic Engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision This report describes how SIFT keypoints are obtained by using Difference-of-Gaussian for detecting local maxima and minima and methods to filter detected local features in order to obtain stable keypoints. The magnitudes of gradient and orientations are added to each keypoint before creating each keypoint descriptor. These highly distinctive SIFT features are matched against each other to find k nearest-neighbors for each feature. These correspondences are then used to find m candidate matching images for each image. Based on the extracted local features, image transformation matrices are built, images are to be processed based on the transformation matrices. This report also discussed methods for blending images to create seamless panorama. Bachelor of Engineering 2009-06-15T03:33:26Z 2009-06-15T03:33:26Z 2009 2009 Final Year Project (FYP) http://hdl.handle.net/10356/17822 en Nanyang Technological University 47 p. application/pdf |
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DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Nar, Soon Keong Panoramic image stitching using SIFT |
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This report describes how SIFT keypoints are obtained by using Difference-of-Gaussian for detecting local maxima and minima and methods to filter detected local features in order to obtain stable keypoints. The magnitudes of gradient and orientations are added to each keypoint before creating each keypoint descriptor. These highly distinctive SIFT features are matched against each other to find k nearest-neighbors for each feature. These correspondences are then used to find m candidate matching images for each image. Based on the extracted local features, image transformation matrices are built, images are to be processed based on the transformation matrices. This report also discussed methods for blending images to create seamless panorama. |
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Chua Chin Seng |
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Chua Chin Seng Nar, Soon Keong |
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Final Year Project |
author |
Nar, Soon Keong |
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Nar, Soon Keong |
title |
Panoramic image stitching using SIFT |
title_short |
Panoramic image stitching using SIFT |
title_full |
Panoramic image stitching using SIFT |
title_fullStr |
Panoramic image stitching using SIFT |
title_full_unstemmed |
Panoramic image stitching using SIFT |
title_sort |
panoramic image stitching using sift |
publishDate |
2009 |
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http://hdl.handle.net/10356/17822 |
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1772825907191349248 |