Objective grading of fabric pilling with wavelet texture analysis

A new objective fabric pilling grading method based on wavelet texture analysis was developed. The new method created a complex texture feature vector based on the wavelet detail coefficients from all decomposition levels and horizontal, vertical and diagonal orientations, permitting a much richer and more complete representation of pilling texture in the image to be used as a basis for classification. Standard multi-factor classification techniques of principal components analysis and discriminant analysis were then used to classify the pilling samples into five pilling degrees. The preliminary investigation of the method was performed using standard pilling image sets of knitted, woven and non-woven fabrics. The results showed that this method could successfully evaluate the pilling intensity of knitted, woven and non-woven fabrics by selecting the suitable wavelet and associated analysis scale.

Identification of animal fibers with wavelet texture analysis

This paper presents the use of the wavelet transform to extract fiber surface texture features for classifying cashmere and superfine merino wool fibers. Extracting features from brightness variations caused by the cuticular scale height, shape and interval provides an effective way for characterizing different animal fibers and subsequently classifying them. This may enable the development of a completely automated and objective system for animal fiber identification.

Automatic grain texture analysis using integral transforms

Tonewood for musical instruments is quarter-sawn and frequently quality-graded based on visual appearance, mechanical and acoustic properties. The assessment uses simple human (subjective) observation, and two ‘‘experts’’ can rate the same sample differently. This paper describes the application of integral transforms (Fourier and Radon) for automatic (objective) assessment of the visual appearance of 10 Sitka spruce (Picea sitchensis) sample images. This work considers surface classification on the basis of grain orientation, count, spacing, and evenness or uniformity.

Ranking of fibre-reinforced composite plate surface finish quality by wavelet texture analysis

In the automotive and other industries, the visual appearance of external surfaces is a key factor in perceived product quality. Traditionally, the quality of an automotive surface finish has been judged by expert human auditors. A set of 17 fibre-reinforced composite plates was previously manufactured to have a range of surface finish qualities and these plates were ranked by three expert observers and also optically digitally imaged. Following validation of the previous rankings, the wavelet texture analysis (WTA) technique was applied to the digital photographs to derive an instrumental measure of surface finish quality based on the panel images. The rank correlation between the human expert surface finish quality ratings and those from the W TA image analysis process was found to be positive, large and statistically significant. This finding indicates that WTA could form the basis of an inexpensive and practical instrumental method for the ranking of fibre-reinforced composite surface finish quality.

The application of ant colony systems to image texture classification

This paper presents a novel ant system based optimisation method which integrates genetic algorithms and simplex algorithms. This method is able to not only speed up the search process for solutions, but also improve the quality of the solutions. In this paper, the proposed method is applied to set up a learning model for the "tuned" mask, which is used for texture classification. Experimental results on aerial images and comparisons with genetic algorithms and genetic simplex algorithms are presented to illustrate the merit and feasibility of the proposed method.

Identification of cashmere and superfine merino wool with wavelet texture analysis

This paper presents the use of the wavelet transform to extract fibre surface texture features for classifying cashmere and superfine merino wool fibres. To extract features from brightness variations caused by the cuticular scale height, shape and interval provides an effective way for characterising different animal fibres and subsequently classifying them. This may enable the development of a completely automated and objective system for animal fibre
identification.

Targeting and measuring housing problems in developing countries through urban texture analysis

Latin-american countries passed from predominantely rural to predominantely urban within few decades. The level of urbanisation in Brazil progressed from 36% in 1950, 50% in 1970, and scalating to 85% in 2005. This rapid transformation resulted in many social problems, as cities were not able to provide appropriate housing and infrastructure for the growing population. As a response, the Brazilian Ministry for Cities, in 2005, created the National System for Social Housing, with the goal to establish guidelines in the Federal level, and build capacity and fund social housing projects in the State and Local levels. This paper presents a research developed in Gramado city, Brazil, as part of the Local Social Housing Plan process, with the goal to produce innovative tools to help social housing planning and management. It proposes and test a methodology to locate and characterise/rank housing defficiencies across the city combining GIS and fractal geometry analysis. Fractal measurements, such as fractal dimension and lacunarity, are able to differentiate urban morphology, and integrated to infrastructure and socio-economical spatial indicators, they can be used to estimate housing problems and help to target, classify and schedule actions to improve housing in cities and regions. Gramado city was divided in a grid with 1,000 cells. For each cell, the following indicators were measured: average income of households, % of roads length which are paved (as a proxy for availability of infrastructures as water and sewage), fractal dimension and lacunarity of the dwellings spatial distribution. A statistical model combining those measurements was produced using a sample of 10% of the cells divided in five housing standards (from high income/low density dwellings to slum's dwellings). The estimation of the location and level of social housing deficiencies in the whole region using the model, compared to the real situation, achived high correlations. Simple and based on easily accessible and inexpensive data, the method also helped to overcome limitations of lack of information and fragmented knowledge of the area related to housing conditions by local professionals.

Microstructural and surface texture analysis due to machining in Super Austenitic Stainless Steel

Inferior surface quality is a significant problem faced by machinist. The purpose of this study is to present a surface texture analysis undertaken as part of machinability assessment of Super Austenitic Stainless Steel alloy-AL6XN. The surface texture analysis includes measuring the surface roughness and investigating the microstructural behaviour of the machined surfaces. Eight milling trials were conducted using combination of cutting parameters under wet machining. An optical profilometer (non-contact), was used to evaluate the surface texture at three positions. The surface texture was represented using the parameter, average surface roughness. Scanning Electron Microscope was utilised to inspect the machined surface microstructure and co relate with the surface roughness results. Results showed that maximum roughness values recorded at the three positions in the longitudinal direction (perpendicular to the machining grooves) were 1.21 μm (trial 1), 1.63 μm (trial 6) and 1.68 μm (trial 7) respectively whereas the roughness values were greatly reduced in the lateral direction. Also, results showed that the feed rate parameter significantly influences the roughness values compared to the other cutting parameters. The microstructure of the machined surfaces was distorted by the existence of cracks, deformed edges and bands and wear deposition due to machining process.

Texture recognition by using GLCM and various aggregation functions

We discuss the problem of texture recognition based on the grey level co-occurrence matrix (GLCM). We performed a number of numerical experiments to establish whether the accuracy of classification is optimal when GLCM entries are aggregated into standard metrics like contrast, dissimilarity, homogeneity, entropy, etc., and compared these metrics to several alternative aggregation methods.We conclude that k nearest neighbors classification based on raw GLCM entries typically works better than classification based on the standard metrics for noiseless data, that metrics based on principal component analysis inprove classification, and that a simple change from the arithmetic to quadratic mean in calculating the standard metrics also improves classification.

New methods for objective evaluation of fabric pilling by frequency domain image processing

Fabric pilling is a serious problem for the apparel industry. Resistance to pilling is normally tested by simulated accelerated wear and manual assessment of degree of pilling based on a visual comparison of the sample to a set of test images. A number of automated systems based on image analysis have been developed. The authors propose new methods of image analysis based on the two-dimensional wavelet transform to objectively measure the pilling intensity in sample images. Initial work employed the detail coefficients of the two-dimensional discrete wavelet transform (2DDWT) as a measure of the pilling intensity of woven/knitted fabrics.

This method is shown to be robust to image translation and brightness variation. Using the approximation coefficients of the 2DDWT, the method is extended to non-woven pilling image sets. Wavelet texture analysis (WTA) combined with principal components analysis are shown to produce a richer texture description of pilling for analysis and classification. Finally, employing the two-dimensional dual-tree complex wavelet transform as the basis for the WTA feature vector is shown to produce good automated classification on a range of standard pilling image sets.

Objective surface evaluation of fiber reinforced polymer composites

The mechanical properties of advanced composites are essential for their structural performance, but the surface finish on exterior composite panels is of critical importance for customer satisfaction. This paper describes the application of wavelet texture analysis (WTA) to the task of automatically classifying the surface finish properties of two fiber reinforced polymer (FRP) composite construction types (clear resin and gel-coat) into three quality grades. Samples were imaged and wavelet multi-scale decomposition was used to create a visual texture representation of the sample, capturing image features at different scales and orientations. Principal components analysis was used to reduce the dimensionality of the texture feature vector, permitting successful classification of the samples using only the first principal component. This work extends and further validates the feasibility of this approach as the basis for automated non-contact classification of composite surface finish using image analysis.