Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.288648
Title: Fast statistically robust image registration
Author: Fitch, Alistair John
ISNI:       0000 0001 3467 7317
Awarding Body: University of Surrey
Current Institution: University of Surrey
Date of Award: 2003
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Abstract:
Image registration is the automatic alignment of images. It is a fundamental task in computer vision. Image registration is challenging, in part, because of the wide range of applications with an equally wide range of content. Applications that require the automatic alignment of images include: super-resolution, face detection, video coding, medical imaging, mosaicking, post-production video effects, and satellite image registration. The wide and diverse range of applications have led to a wide and diverse range of image registration algorithms. An image registration algorithm is defined by its transformation, criterion, and search. The transformation is the model of image deformation required for alignment. The criterion is the definition of the best registration. The search describes how the best registration is to be found. This thesis presents two image registration methods; fast robust correlation and orientation correlation. The presented methods find translational transformations. Both define their criterion of the best registration using robust statistics. Fast robust correlation applies robust statistics to pixel intensity differences. Orientation correlation applies robust statistics to differences in orientation of intensity gradient. This gives orientation correlation the property of illumination invariance. Both use an exhaustive search to find the best registration. The novelty of fast robust correlation and orientation correlation is the combination of robust statistics, with an exhaustive search that can be computed quickly with fast Fourier transforms (FFTs). This is achieved by expressing a statistically robust registration surface with correlations. The correlations are computed quickly using FFTs. Computation with FFTs is shown to be particularly advantageous in registration of large images of similar size. Experimental comparisons demonstrate the advantages of the methods over standard correlation-based approaches. Advantage is shown in the experiments of: video coding, video frame registration, tolerance of rotation and zoom, registration of multimodal microscopy images, and face registration.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.288648  DOI: Not available
Keywords: Pattern recognition & image processing
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