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Hauptverfasser: Dutta, Aritra, Suseela, G, Sood, Asmita
Format: Preprint
Veröffentlicht: 2023
Schlagworte:
Online-Zugang:https://arxiv.org/abs/2312.17010
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author Dutta, Aritra
Suseela, G
Sood, Asmita
author_facet Dutta, Aritra
Suseela, G
Sood, Asmita
contents Multi-modal image stitching can be a difficult feat. That's why, in this paper, we've devised a unique and comprehensive image-stitching pipeline that taps into OpenCV's stitching module. Our approach integrates feature-based matching, transformation estimation, and blending techniques to bring about panoramic views that are of top-tier quality - irrespective of lighting, scale or orientation differences between images. We've put our pipeline to the test with a varied dataset and found that it's very effective in enhancing scene understanding and finding real-world applications.
format Preprint
id arxiv_https___arxiv_org_abs_2312_17010
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Robust Multi-Modal Image Stitching for Improved Scene Understanding
Dutta, Aritra
Suseela, G
Sood, Asmita
Computer Vision and Pattern Recognition
Multi-modal image stitching can be a difficult feat. That's why, in this paper, we've devised a unique and comprehensive image-stitching pipeline that taps into OpenCV's stitching module. Our approach integrates feature-based matching, transformation estimation, and blending techniques to bring about panoramic views that are of top-tier quality - irrespective of lighting, scale or orientation differences between images. We've put our pipeline to the test with a varied dataset and found that it's very effective in enhancing scene understanding and finding real-world applications.
title Robust Multi-Modal Image Stitching for Improved Scene Understanding
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2312.17010