Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/41433
Title: Learning transformation invariance for pairwise image matching
Authors: Chen, Xi
Keywords: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Issue Date: 2008
Source: Chen, X. (2008). Learning transformation invariance for pairwise image matching. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: 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.
URI: https://hdl.handle.net/10356/41433
DOI: 10.32657/10356/41433
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Theses

Files in This Item:
File Description SizeFormat 
ChenXi08.pdf20.78 MBAdobe PDFThumbnail
View/Open

Page view(s) 50

353
Updated on Jun 23, 2021

Download(s) 50

110
Updated on Jun 23, 2021

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.