Learning transformation invariance for pairwise image matching
Image matching is a fundamental problem in computer vision. In this thesis, we address the image matching problem as learning and classifying correspondences. More precisely, we formulate the image matching problem as: given a set of training pairs of images that implicitly captures the tra...
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sg-ntu-dr.10356-414332023-03-04T00:47:10Z Learning transformation invariance for pairwise image matching Chen, Xi Cham Tat Jen School of Computer Engineering Centre for Multimedia and Network Technology DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Image matching is a fundamental problem in computer vision. In this thesis, we address the image matching problem as learning and classifying correspondences. More precisely, we formulate the image matching problem as: given a set of training pairs of images that implicitly captures the transformation(with both positive and negative classes), identify if a new pair of test images is matched via the transformation class. In this formulation, all the training data, as well as test data, are image pairs. The approach taken is to consider only relative visual content, rather than absolute visual content, so the learned image matching classifier could be applied to images of totally different visual content as compared to the training data. This is in contrast to appearance-based object detection methods, for which once the training process is completed, the classifiers may only be used to recognize objects of the same categories with the training images. DOCTOR OF PHILOSOPHY (SCE) 2010-07-05T03:19:25Z 2010-07-05T03:19:25Z 2008 2008 Thesis Chen, X. (2008). Learning transformation invariance for pairwise image matching. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/41433 10.32657/10356/41433 en 161 p. application/pdf |
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DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Chen, Xi Learning transformation invariance for pairwise image matching |
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Image matching is a fundamental problem in computer vision. In this thesis, we
address the image matching problem as learning and classifying correspondences.
More precisely, we formulate the image matching problem as: given a set of training
pairs of images that implicitly captures the transformation(with both positive
and negative classes), identify if a new pair of test images is matched via the transformation
class. In this formulation, all the training data, as well as test data, are
image pairs. The approach taken is to consider only relative visual content, rather
than absolute visual content, so the learned image matching classifier could be applied
to images of totally different visual content as compared to the training data.
This is in contrast to appearance-based object detection methods, for which once
the training process is completed, the classifiers may only be used to recognize
objects of the same categories with the training images. |
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Cham Tat Jen |
author_facet |
Cham Tat Jen Chen, Xi |
format |
Theses and Dissertations |
author |
Chen, Xi |
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Chen, Xi |
title |
Learning transformation invariance for pairwise image matching |
title_short |
Learning transformation invariance for pairwise image matching |
title_full |
Learning transformation invariance for pairwise image matching |
title_fullStr |
Learning transformation invariance for pairwise image matching |
title_full_unstemmed |
Learning transformation invariance for pairwise image matching |
title_sort |
learning transformation invariance for pairwise image matching |
publishDate |
2010 |
url |
https://hdl.handle.net/10356/41433 |
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1759855487052939264 |