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. © 2008 Springer Berlin Heidelberg.
CITATION STYLE
Shorin, A., Gimel’Farb, G., Delmas, P., & Morris, J. (2008). Image matching with spatially variant contrast and offset: A quadratic programming approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5342 LNCS, pp. 127–136). https://doi.org/10.1007/978-3-540-89689-0_17
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