Miquel Ralló

Modeling of woven fabric structures based on Fourier image analysis

The periodic woven structures of fabrics can be defined on the basis of the convolution theorem. Here an elementary unit with the minimum number of thread crossings and a nonrectangular two-dimensional comb function for the pattern of repetition is used to define woven structures. The expression derived is more compact than the conventional diagram for weaving, and the parameters that one needs to determine a given fabric can easily be extracted from its Fourier transform. Several results with real samples of the most common structures-plain, twill, and satin-are presented.

Weave-repeat identification by structural analysis of fabric images

Two descriptions of the image of a web structure, a convolution model and an additive model, in both the spatial and frequency domains, are combined in the design of a method to extract information about the fabric structure by image analysis. The method allows the extraction of the conventional and also the minimal weave repeats, their size in terms of number of threads, their interlacing patterns, and their patterns of repetition. It is applicable to fabrics with square and nonsquare conventional weave repeat. Experimental results with image of real samples are presented and discussed.

Unsupervised novelty detection using Gabor filters for defect segmentation in textures

Gabor wavelets are applied to develop an unsupervised novelty method for defect detection and segmentation that is fully automatic and free of any adjustable parameter. The algorithm combines the Gabor analysis of the sample image with a statistical analysis of the wavelet coefficients corresponding to each detail. The statistical distribution of the coefficients corresponding to the defect-free background texture is calculated from the coefficients distribution of the sample under inspection. Once the background texture features are estimated, a threshold is automatically fixed and applied to all the details, whose information is merged into a single binary output image in which the defect appears segmented from the background. The method is applicable to random, nonperiodic, and periodic textures. Since all the information to inspect a sample is obtained from the sample itself, the method is proof against heterogeneities between different samples of the material, in-plane positioning errors, scale variations, and lack of homogeneous illumination. Experimental results are presented. Some results are compared with other unsupervised methods designed for defect segmentation in periodic textures.

Referenceless segmentation of flaws in woven fabrics

The automatic segmentation of flaws in woven fabrics is achieved by applying Fourier analysis to the image of the sample under inspection, without considering any reference image. No prior information about the fabric structure or the defect is required. The algorithm is based on the structural feature extraction of the weave repeat from the Fourier transform of the sample image. These features are used to define a set of multiresolution bandpass filters, adapted to the fabric structure, that operate in the Fourier domain. Inverse Fourier transformation, binarization, and merging of the information obtained at different scales lead to the output image that contains flaws segmented from the fabric background. The whole process is fully automatic and can be implemented either optically or electronically. Experimental results are presented and discussed for a variety of fabrics and defects.

Objective measure of woven fabric’s cover factor by image processing

Digital image processing techniques have been applied to perform an automatic method for the objective measure of woven fabric’s cover factor. Based on a frequency domain analysis, digital images of woven fabric samples, obtained with a camera assembled to a microscope, were cropped to enclose the maximum integer number of warp and weft periods and leveled for non-uniform illumination. Posterior thresholding, designed to perform satisfactorily for both high and low cover factor fabric samples, gave rise to the objective value. The method was applied to three different sets of samples manufactured in plain weave, with known yarn numbers and thread counts. Cover factors obtained by this method showed good correlation with those obtained by a set of visual observers and were consistent with woven fabric parameters: yarn numbers (tex) and thread counts (yarns/cm). The procedure could be useful to monitor mean cover factor as well as cover factor variability in fabric batches. It does not require sophisticated equipment and could be straightforwardly implemented in a textile analysis laboratory.

Image processing techniques to quantify microprojections on outer corneal epithelial cells

It is widely accepted that cellular microprojections (microvilli and/or microplicae) of the corneal surface are essential to maintain the functionality of the tissue. To date, the characterization of these vital structures has been made by analysing scanning or transmission electron microscopy images of the cornea by methods that are intrinsically subjective and imprecise (qualitative or semiquantitative methods). In the present study, numerical data concerning three microprojection features were obtained by an automated method and analysed to establish which of them showed less variability. We propose that the most stable microprojection characteristic would be a useful sign in early detection of epithelial damage or disease. With this aim, the scanning electron microscopy images of 220 corneal epithelial cells of nine rabbits were subjected to several image processing techniques to quantify microprojection density, microprojection average size and surface covered by microprojections (SCM). We then assessed the reliability of the methods used and performed a statistical analysis of the data. Our results show that the thresholding process, the basis of all image processing techniques used in this work, is highly reliable in separating microprojections from the rest of the cell membrane. Assessment of histogram information from thresholded images is a good method to quantify SCM. Amongst the three studied variables, SCM was the most stable (with a coefficient of variation of 15.24%), as 89.09% of the sample cells had SCM values ≥ 40%. We also found that the variability of SCM was mainly due to intercellular differences (the cell factor contribution represented 88.78% of the total variation in the analysed cell areas). Further studies are required to elucidate how healthy corneas maintain high SCM values.

Automatic measurements of partial cover factors and yarn diameters in fabrics using image processing

Warp and weft cover factors and their mean yarn diameters were automatically estimated from microscope digital images of woven fabric samples. Regarding the periodicity of weaves, images were processed in the frequency domain. Frequencies were carefully analyzed to suitably crop the image to an integer number of complete yarn crossings in warp and weft directions, so as to eliminate possible bias due to fragmented yarn crossings, if any, in the original image. Warp and weft frequencies were identified in from the power spectrum of the cropped image by means of the Radon transform. Image filtering in the Fourier domain and binarization yielded partial cover factor estimates. The method was tested using a set of 81 diverse woven fabric samples and the results were consistent with their manufacturing specifications: yarn linear densities and thread counts. Finally, the mean yarn diameters were derived from partial cover factor estimates. To validate the proposed method the results were compared with the mean yarn diameters directly measured from images.

Unsupervised defect segmentation of patterned materials under NIR illumination

An unsupervised detection method for automatic flaw segmentation in patterned materials (textile, non-woven, paper) that has no need of any defect-free references or a training stage is presented in this paper. Printed materials having a pattern of colored squares, bands, etc. superimposed to the background texture can be advantageously analyzed using NIR illumination and a camera with enough sensitivity to this region of the spectrum. The contrast reduction of the pattern in the NIR image facilitates material inspection and defect segmentation. Underdetection and misdetection errors can be reduced in comparison with the inspection performed under visible illumination. For woven fabrics, with periodic structure, the algorithm is based on the structural feature extraction of the weave repeat from the Fourier transform of the sample image. These features are used to define a set of multiresolution bandpass filters adapted to the fabric structure that operate in the Fourier domain. Inverse Fourier transformation, binarization and merging of the information obtained at different scales lead to the output image that contains flaws segmented from the fabric background. For non-woven and random textured materials, the algorithm combines the multiresolution Gabor analysis of the sample image with a statistical analysis of the wavelet coefficients corresponding to each detail. The information of all the channels is merged in a single binary output image where the defect appears segmented from the background. The method is applicable to random, non-periodic, and periodic textures. Since all the information to inspect a sample is obtained from the sample itself, the method is proof against heterogeneities between different samples of the material, in-plane positioning errors, scale variations and lack of homogeneous illumination. Experimental results are presented for a variety of materials and defects.

Image processing techniques in a preliminary morphometric characterization of corneal epithelium by scanning electron microscopy

Several processing techniques of digital images allowed us to quantify the percentage of cell surface covered by microprojections (microvilli or microplicae) (SCM), the adhesion between epithelial cells by the parameter intercellular junctions (IJ), the size (cell area), shape (cell shape) and shade (cell shade) of cells on the corneal epithelium of nine rabbits. The data were analyzed and the epithelial cells were classified into three groups by cluster analysis. Assuming the representativeness of the sample, our findings suggest that for a normal corneal epithelium, 80% of the cells could show SCM >41%, and IJ >0.98 (being one a cell to cell junction without disruptions). Standard deviations of cell shade lower than 21 gray levels could indicate a tendency to lose the cell shade mosaic. Normal corneas could show a majority of cells (54–69%) included in group 2 with smaller mean size (80% of cells with cell area <242 μm2), higher SCM (80% of cells with SCM >44.83%), polygonal mean shape and brighter shade. Group 1 (15–30% of cells) could show a larger mean size (80% of cells with cell area >494 μm2), lower SCM (although 80% of cells with SCM >32.61%), circular mean shape and darker electron reflex. Group 3 could display a medium mean size, higher SCM (similar to group 2), circular mean shape (similar to group 1), and brighter shade. These analyses could possibly be used to further assess the corneal response to ocular drugs, contact lens, and surgical procedures or to discriminate between pathology stages. Microsc. Res. Tech. 73:1059–1066, 2010.

Psychophysical Validation of A Digital Method to Assess Ill-Defined Visual Boundaries: An Example with Fabric Openness Factor

Fabric openness factor (OF) is the fraction of the web area that is uncovered by yarns. OF is a critical feature regarding the end-use performance of the fabric and should be accurately assessed. However, digital OF estimates yielded by image binarization algorithms differ among them depending on the criteria used, mainly due to ill-defined boundaries, thus precluding a straightforward assessment of the actual fabric OF value. Lacking any standard to compare actual OF values with measured OF values, we addressed the validation procedure of the digital assessment method from visual OF estimates. OF of 81 distinct fabric samples was evaluated from digital images by a panel of 18 observers using visual binarization technique. Following the psychophysical models of Fechner and Stevens, these visual estimates were correlated with digital estimates yielded by several binarization algorithms. Stevens’ psychophysical model and an automatic binarization algorithm developed by us scored the highest correlation.