Anzeige
Erschienen in:
2008 | OriginalPaper | Buchkapitel
Image Matching with Spatially Variant Contrast and Offset: A Quadratic Programming Approach
verfasst von : Alexander Shorin, Georgy Gimel’farb, Patrice Delmas, Jonh Morris
Erschienen in: Structural, Syntactic, and Statistical Pattern Recognition
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
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.