Affine image registration with curve mapping

2011 ◽  
Author(s):  
Yong Li ◽  
Robert L. Stevenson
Keyword(s):  
Image Registration ◽  
Affine Image

Affine Image Registration Based on Geometric Inference

2013 ◽  
Vol 427-429 ◽  
pp. 1610-1613
Author(s):  
Xing Wei Yan ◽  
Jie Min Hu ◽  
Jun Zhang ◽  
Jian Wei Wan
Keyword(s):  
Image Registration ◽  
Affine Transformation ◽  
Real Data ◽  
Matching Problem ◽  
Point Matching ◽  
Noncollinear Point ◽  
Geometric Inference ◽  
Affine Image ◽  
Novel Method ◽  
Geometric Relationship

Image registration is widely used in applications for mapping one image to another. As it is often formulated as a point matching problem, in this paper, a novel method, called the Geometric Inference (GI) algorithm, is proposed for feature point based image registration. Firstly, according to affine distance invariant, the global geometric relationship between collinear correspondences is deduced and used for collinear point matching. Secondly, utilizing affine area invariant, geometric relationship between noncollinear correspondences is inferred and used for noncollinear point matching. Finally, the best affine transformation can be discovered from the correspondences composed of the collinear and noncollinear corresponding point pairs. Experiments on synthesized and real data demonstrate that GI is well-adapt to image registration as it is fast and robust to missing points, outliers, and noise.

Affine image registration guided by particle filter

2012 ◽  
Vol 6 (5) ◽  
pp. 455 ◽  
Author(s):  
E.R. Arce-Santana ◽  
D.U. Campos-Delgado ◽  
A. Alba
Keyword(s):  
Image Registration ◽  
Particle Filter ◽  
Affine Image

scholarly journals Transitively Consistent and Unbiased Multi-Image Registration using Numerically Stable Transformation Synchronisation

2015 ◽  
Author(s):  
Florian Bernard ◽  
Johan Thunberg ◽  
Andreas Husch ◽  
Luis Salamanca ◽  
Peter Gemmar ◽  
...  
Keyword(s):  
Image Registration ◽  
Medical Images ◽  
Desirable Property ◽  
Stable Transformation ◽  
Affine Transformations ◽  
Registration Accuracy ◽  
Brain Images ◽  
Registration Algorithm ◽  
Image Registration Algorithm ◽  
Affine Image

Transitive consistency of pairwise transformations is a desirable property of groupwise image registration procedures. The transformation synchronisation method (Bernard et al., 2015) is able to retrieve transitively consistent pairwise transformations from pairwise transformations that are initially not transitively consistent. In the present paper, we present a numerically stable implementation of the transformation synchronisation method for affine transformations, which can deal with very large translations, such as those occurring in medical images where the coordinate origins may be far away from each other. By using this method in conjunction with any pairwise (affine) image registration algorithm, a transitively consistent and unbiased groupwise image registration can be achieved. Experiments involving the average template generation from 3D brain images demonstrate that the method is more robust with respect to outliers and achieves higher registration accuracy compared to reference-based registration.

scholarly journals Affine registration of multispectral images of historical documents for optimized feature recovery

2019 ◽  
Author(s):  
Cerys Jones ◽  
William A Christens-Barry ◽  
Melissa Terras ◽  
Michael B Toth ◽  
Adam Gibson
Keyword(s):  
Image Registration ◽  
Similarity Measures ◽  
Phase Correlation ◽  
Historical Documents ◽  
Multispectral Images ◽  
Optical Elements ◽  
Affine Registration ◽  
Illumination Changes ◽  
Affine Image ◽  
Processing Techniques

Abstract Multispectral (MSI) imaging of historical documents can recover lost features, such as text or drawings. This technique involves capturing multiple images of a document illuminated using different wavelengths of light. The images created must be registered in order to ensure optimal results are produced from any subsequent image processing techniques. However, the images may be misaligned due to the presence of optical elements such as filters, or because they were acquired at different times or because the images were captured from different copies of the documents . There is little prior work or information available about which image registration techniques are most appropriate. Image registration of multispectral images is challenging as the illumination changes for each image and the features visible in images captured at different wavelengths may not appear consistently throughout the image sequence. Here, we compare three image registration techniques: two based on similarity measures and a method based on phase correlation. These methods are characterized by applying them to realistic surrogate images and then assessed on three different sets of real multispectral images. Mutual information is recommended as a measure for affine image registration when working with multispectral images of documentary material as it was proven to be more robust than the other techniques tested.

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