We're sorry but this page doesn't work properly without JavaScript enabled. Please enable it to continue.
Feedback

Reflectance recovery using localised weighted method

Formale Metadaten

Titel
Reflectance recovery using localised weighted method
Serientitel
Teil
30
Anzahl der Teile
31
Autor
Lizenz
CC-Namensnennung - keine Bearbeitung 2.0 UK: England & Wales:
Sie dürfen das Werk in unveränderter Form zu jedem legalen Zweck nutzen, vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen.
Identifikatoren
Herausgeber
Erscheinungsjahr2012
SpracheEnglisch

Inhaltliche Metadaten

Fachgebiet
Genre
Abstract
This paper evaluated four conventional methods for reflectance recovery: smoothness method, principle component analysis, basis functions with smoothness constraint and Wiener estimation. Most of these methods adopt a “learning-based” procedure with a training set. Modifications based on the training set were applied for improving the reflectance recovery performance. This paper described combined methods involving the application of localised training data and localised training data with a weighted matrix to the four recovery methods. All these methods were applied to recover reflectance from XYZ values for two datasets. Both the training and testing performance were evaluated in terms of CIEDE2000 colour differences. The results showed that the performance of the methods with localised training data significantly improved. There are also limited improvements by applying the weighted matrix. Overall, the localised weighted method (using a local training set with a weighted matrix) with Weiner estimation method performed the best.