A New matching strategy: Trial of the principal component coordinates

Abstract

A new matching strategy based on the equalization of the first three principal component coordinates of sample and target in a 3D eigenvector space is stated. Two series of databases including 1269 specimens of Munsell Color Book and a virtual sample population of textile materials were selected. Their first three basis functions were extracted and considered as axes of eigenvector space. The principal component coordinates of two different collections of textile samples were determined in these spaces and considered as matching criteria. The performance of the proposed algorithm is evaluated by the color difference values under different light sources as well as the root mean square differences of reflectance curves. Results indicate some types of improvements in comparison with previous algorithms in terms of spectral as well as colorimetric accuracy.

Application of ant colony optimization to color matching of dyed cotton fabrics with direct dyestuffs mixtures

“…Nowadays, several approaches were developed for color formulation problem. Some authors have adopted conventional methods, such as colorimetric and spectrophotometric methods . The choice to use one of these two methods is done according to the purpose of matching .…”

“…Some authors have adopted conventional methods, such as colorimetric and spectrophotometric methods. [10][11][12] The choice to use one of these two methods is done according to the purpose of matching. 4,8 Other researchers have used artificial intelligence techniques such as neural networks [13][14][15][16][17] and genetic algorithms.…”

Colour recipe prediction using ant colony algorithm: principle of resolution and analysis of performances

“…Different approaches and methods were developed for colour recipe prediction. Some researchers have proposed methods based on colorimetric and spectrophotometric algorithms . Others applied artificial intelligence techniques such as neural network and genetic algorithm .…”

Estimating dye concentrations in three-component wastewater by neuro-fuzzy technique

“…The first principal component has much of the variability in the data and each successive component accounts for as a large amount of the remaining variability. The PCA has been used in color science and technology since 1960s, as a mathematical and statistical means for condensing the dimensionality of large numbers of reflectance spectra, spectral imaging, and color matching (Agahian & Amirshahi, 2008;Ansari, Amirshahi, & Moradian, 2006;Fairman & Brill, 2004;Jolliffe, 2002;Shams-Nateri, 2008, 2009Shlens, 2003;Smith, 2002;Tzeng & Berns, 2005;Westland & Ripamonti, 2004). Recently, Agahian and Amirshahi (2008) suggested a new color matching algorithm by matching the principal component coordinates of sample with principal component coordinates of target.…”