Image registration under affine transformation using cellular simultaneous recurrent networks

2010 ◽  
Author(s):  
Khan M. Iftekharuddin ◽  
Keith Anderson
Keyword(s):  
Image Registration ◽  
Affine Transformation ◽  
Recurrent Networks

Breast image registration by using non-linear local affine transformation

2013 ◽  
Author(s):  
Feiyu Chen ◽  
Peng Zheng ◽  
Penglong Xu ◽  
Andrew D. A. Maidment ◽  
Predrag R. Bakic ◽  
...  
Keyword(s):  
Image Registration ◽  
Affine Transformation ◽  
Breast Image ◽  
Non Linear

Cylindrical affine transformation model for image registration

2010 ◽  
Author(s):  
Christine Tanner ◽  
Timothy Carter ◽  
David Hawkes ◽  
Gàbor Székely
Keyword(s):  
Image Registration ◽  
Affine Transformation ◽  
Transformation Model

Continuous piecewise affine transformation for image registration

2015 ◽  
Vol 13 (01) ◽  
pp. 1550006
Author(s):  
Yun-Shan Lee ◽  
Wen-Liang Hwang ◽  
Xiaolin Tian
Keyword(s):  
Image Registration ◽  
Affine Transformation ◽  
Design Methodology ◽  
Geometric Distortion ◽  
Geometric Transformation ◽  
Successful Completion ◽  
Piecewise Affine ◽  
Transitivity Property ◽  
Closed Form Analytical Solution ◽  
Associated Design

The image registration problem involves determining a geometric transformation to properly align images of interest. This paper proposes a transformation approach called Continuous Piecewise Affine Transformation (CPAT) to model the geometric distortion in images. The associated design methodology for the 2D registration problem is also discussed. Registration on CPAT has two advantages: (1) The optimal transformation has a closed form analytical solution; and (2) the transformation is invertible and transitive. Because of these advantages, CPAT could be used for real-time registration and progressive registration, where the registration speed and transitivity property are important for the successful completion of the tasks. The results of experiments demonstrate the efficacy of CPAT in resolving the image registration problem.

Image Registration Techniques and Frameworks

2017 ◽  
pp. 102-114 ◽  
Author(s):  
Sayan Chakraborty ◽  
Prasenjit Kumar Patra ◽  
Prasenjit Maji ◽  
Amira S. Ashour ◽  
Nilanjan Dey
Keyword(s):  
Image Registration ◽  
Affine Transformation ◽  
Feature Detection ◽  
Feature Matching ◽  
Mapping Function ◽  
Intermediate Step ◽  
B Splines ◽  
Function Design ◽  
Feature Based ◽  
Step Over

Image registration allude to transforming one image with reference to another (geometrically alignment of reference and sensed images) i.e. the process of overlaying images of the same scene, seized by assorted sensors, from different viewpoints at variant time. Virtually all large image evaluating or mining systems require image registration, as an intermediate step. Over the years, a broad range of techniques has been flourished for various types of data and problems. These approaches are classified according to their nature mainly as area-based and feature-based and on four basic tread of image registration procedure namely feature detection, feature matching, mapping function design, and image transformation and resampling. The current chapter highlights the cogitation effect of four different registration techniques, namely Affine transformation based registration, Rigid transformation based registration, B-splines registration, and Demons registration. It provides a comparative study among all of these registration techniques as well as different frameworks involved in registration process.

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.

Binary image registration using cellular simultaneous recurrent networks

2009 ◽  
Author(s):  
Keith Anderson ◽  
Khan Iftekharuddin ◽  
Paul Kim
Keyword(s):  
Image Registration ◽  
Binary Image ◽  
Recurrent Networks
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