A survey of image registration techniques
ACM Computing Surveys (CSUR)
Using Geometric Distance Fits for 3-D Object Modeling and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Orthogonal Distance Fitting of Implicit Curves and Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Model-Based Motion Estimation
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Bundle Adjustment - A Modern Synthesis
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
Determining the Camera Response from Images: What Is Knowable?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
Overview of total least-squares methods
Signal Processing
Groupwise Geometric and Photometric Direct Image Registration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized least squares-based parametric motion estimation
Computer Vision and Image Understanding
Comparametric equations with practical applications in quantigraphic image processing
IEEE Transactions on Image Processing
Jointly registering images in domain and range by piecewise linear comparametric analysis
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A new optimal seam finding method based on tensor analysis for automatic panorama construction
Pattern Recognition Letters
Online algorithm based on support vectors for orthogonal regression
Pattern Recognition Letters
Local joint entropy based non-rigid multimodality image registration
Pattern Recognition Letters
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This paper presents a novel robust image alignment technique that performs joint geometric and photometric registration in the total least square (TLS) sense. Therefore, we employ the total least square metric instead of the ordinary least square (OLS) metric, which is commonly used in the literature. While the OLS model is sufficient to tackle geometric registration problems, it gives no mutually consistent estimates when dealing with photometric deformations. By introducing a new TLS model, we obtain mutually consistent parameters. Experimental results show that our method is indeed more consistent and accurate in presence of noise compared to existing joint registration algorithms.