@inbook {IOPORT.05282965,
    author       = {van der Weken, Dietrich and de Witte, Val\'erie and Nachtegael, Mike and Schulte, Stefan and Kerre, Etienne},
    title        = {Colour image comparison using vector operators.},
    year         = {2008},
    booktitle    = {Fuzzy sets and their extensions: representation, aggregation and models. Intelligent systems from decision making to data mining, web intelligence and computer vision},
    isbn         = {978-3-540-73722-3},
    pages        = {639-656},
    publisher    = {Berlin: Springer},
    abstract     = {Summary: Objective quality measures or measures of comparison are of great importance in the field of image processing. These measures serve as a tool to evaluate and to compare different algorithms designed to solve particular problems, such as noise reduction, deblurring, compression, ... Consequently these measures serve as a basis on which one algorithm is preferred to another. In [{\it D. van der Weken}, {\it M. Nachtegael} and {\it E. E. Kerre}, "Using Similarity Measures and Homogeneity for the Comparisons of Images", Image and Vision Computing, 22, 695--702 (2004) and "Improved Image Quality Measures Using Ordered Histograms", Proc. MMSP'2004 (IEEE Signal Processing Society International Workshop on Multimedia Signal Processing, Siena Italy, September 2004), 67--70 (2004)] we constructed several new fuzzy similarity measures for grey-scale images that outperform the classical measures of comparison, like Root Mean Square Error or Peak Signal to Noise Ratio, in the sense of image quality evaluation. In this chapter we investigate the usefulness of these similarity measures for the comparison of colour images. First of all, we discuss the component-based approach in three different colour spaces, namely the RGB, HSV and Lab colour spaces. And secondly, we discuss a vector-based approach using vector morphological operators. Both approaches are compared by means of several experiments.},
    identifier   = {05282965},
}