Robust image matching under partial occlusion and spatially varying illumination change
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Photometric Stereo with General, Unknown Lighting
International Journal of Computer Vision
Robust face imagematching under illumination variations
EURASIP Journal on Applied Signal Processing
A robust measure for visual correspondence
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
Fast algorithm for robust template matching with M-estimators
IEEE Transactions on Signal Processing
IEEE Transactions on Image Processing
Robust and Efficient Image Alignment Based on Relative Gradient Matching
IEEE Transactions on Image Processing
A Comparative Study of Local Matching Approach for Face Recognition
IEEE Transactions on Image Processing
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Template matching is widely used in machine vision, digital photogrammetry, and multimedia data mining to search for a target object by similarity between its prototype image (template) and a sensed image of a natural scene containing the target. In the real-world environment, similarity scores are frequently affected by contrast / offset deviations between the template and target signals. Most of the popular least-squares scores presume only simple smooth deviations that can be approximated with a low-order polynomial. This paper proposes an alternative and more general quadratic programming based matching score that extends the conventional least-squares framework onto both smooth and non-smooth signal deviations.