The "Invaders' Algorithm: Range of Values Modulation for Accelerated Correlation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient least squares fusion of MRI and CT images using a phase congruency model
Pattern Recognition Letters
NPAR '08 Proceedings of the 6th international symposium on Non-photorealistic animation and rendering
Image Matching with Spatially Variant Contrast and Offset: A Quadratic Programming Approach
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Fast phase-based registration of multimodal image data
Signal Processing
Binocular Based Moving Target Tracking for Mobile Robot
ICIRA '09 Proceedings of the 2nd International Conference on Intelligent Robotics and Applications
Fast and robust photomapping with an unmanned aerial vehicle (UAV)
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Fast frequency template matching using higher order statistics
IEEE Transactions on Image Processing
Euler Principal Component Analysis
International Journal of Computer Vision
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A new, fast, statistically robust, exhaustive, translational image-matching technique is presented: fast robust correlation. Existing methods are either slow or non-robust, or rely on optimization. Fast robust correlation works by expressing a robust matching surface as a series of correlations. Speed is obtained by computing correlations in the frequency domain. Computational cost is analyzed and the method is shown to be fast. Speed is comparable to conventional correlation and, for large images, thousands of times faster than direct robust matching. Three experiments demonstrate the advantage of the technique over standard correlation.