Image stitching

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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Main Author: Chen, Wei Qiang.
Other Authors: Chua Chin Seng
Format: Final Year Project
Language:English
Published: 2011
Subjects:
Online Access:http://hdl.handle.net/10356/46173
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Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-46173
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spelling sg-ntu-dr.10356-461732023-07-07T15:48:41Z Image stitching Chen, Wei Qiang. Chua Chin Seng School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering 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 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 in C++. Bachelor of Engineering 2011-06-29T09:19:37Z 2011-06-29T09:19:37Z 2011 2011 Final Year Project (FYP) http://hdl.handle.net/10356/46173 en Nanyang Technological University 54 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Chen, Wei Qiang.
Image stitching
description 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 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 in C++.
author2 Chua Chin Seng
author_facet Chua Chin Seng
Chen, Wei Qiang.
format Final Year Project
author Chen, Wei Qiang.
author_sort Chen, Wei Qiang.
title Image stitching
title_short Image stitching
title_full Image stitching
title_fullStr Image stitching
title_full_unstemmed Image stitching
title_sort image stitching
publishDate 2011
url http://hdl.handle.net/10356/46173
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