Robust Linear and Support Vector Regression
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Registration and Analysis of Vascular Images
International Journal of Computer Vision - Special Issue on Research at the University of North Carolina Medical Image Display Analysis Group (MIDAG)
Superresolution under photometric diversity of images
EURASIP Journal on Applied Signal Processing
Image alignment and stitching: a tutorial
Foundations and Trends® in Computer Graphics and Vision
SIFT Features Tracking for Video Stabilization
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
Groupwise Geometric and Photometric Direct Image Registration
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Class of Algorithms for Fast Digital Image Registration
IEEE Transactions on Computers
Geometric registration of images with arbitrarily-shaped local intensity variations from shadows
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A fast parametric motion estimation algorithm with illumination and lens distortion correction
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
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In this paper, we extend our previous work on presenting a registration model for images having arbitrarily-shaped locally variant illuminations from shadows to multiple shading levels. These variations tend to degrade the performance of geometric registration and impact subsequent processing. Often, traditional registration models use a least-squares estimator that is sensitive to outliers. Instead, we propose using a robust Huber M-estimator to increase the geometric registration accuracy (GRA). We demonstrate the proposed model and compare it to other models on simulated and real data. This modification shows clear improvements in terms of GRA and illumination correction.