Dataset for: Hidden Markov random field models applied to color homogeneity evaluation in dyed textiles images
datasetposted on 26.12.2019 by Victor Freguglia Souza, Nancy Lopes Garcia, Juliano Lemos Bicas
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
Color is one of the most important features in any textile material. Due to its competitive price, most of the colorants currently used for textile dyeing are synthetic, originated from non-renewable sources and highly pollutant. There is an increasing interest for natural processes to dye fabrics. When new textile dyeing technologies are developed, evaluating the quality of these techniques involves measuring the resulting color homogeneity using digital images. The presence of a texture effect, caused by the interlacing of warp and weft yarns as well as small displacement of the fabric, creates a sophisticated dependence structure in pixels coloring. A random effects model is emplyed in order to separate the signal from the dyeing effect (fixed effect described by smooth functions) and warp and weft texture effect (Gaussian mixture driven by a hidden Markov random field), allowing an evaluation of color homogeneity in dyed textiles regardless of the effect of the texture.