Alphabet pattern approach for color fabric texture classification
01 Jan 2017-pp 1-6
TL;DR: A user defined algorithm is derived to classify the fabric textures into one of the four categories i.e. Silk, Woolen, Nylon and Cotton based on the cumulative occurrences of alphabet patterns on 3×3 sub image.
Abstract: The present paper proposes a new approach for color fabric texture classification based on the occurrences of alphabet patterns. Generally, Patterns are identified on grey level image but the present approach identifies the patterns on RGB color image. The present approach identify the patterns on 3 color channel i.e. Red channel, Green channel and Blue channel. The present approach uses alphabet patterns using 5 pixels in 3×3 sub image. Based on the cumulative occurrences of 5 pixel alphabet patterns on 3×3 sub image, the present paper derives a user defined algorithm to classify the fabric textures into one of the four categories i.e. Silk, Woolen, Nylon and Cotton.
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01 Jan 2022
TL;DR: In this article , a well-organized fabric texture classification system is proposed based on the feature set values, and a user-defined approach to classify the fabric texture image into one of the familiar five pre-defined classes (silk, cotton, linen, wool and worsted).
Abstract: Fabric texture classification can be implemented automatically using fabric texture analysis using their images. Texture analysis using fabric images has lots of applications such as automatic recognition and classification of fabrics and to detect the defects on the fabrics and the quality of the fabrics in fabric industries. Conversely, for the existing manual systems, it is a tough task to estimate the correct fabric texture class group effectively. Manual inspection procedures are inefficient for classification due to lack of vigilance and boredom which deteriorates performance. To reduce the cost and time, an automated classification is required based on computer vision and image processing techniques. In this study, a well-organized fabric texture classification system is proposed. Based on the feature set values, the present paper proposes a user-defined approach to classify the fabric texture image into one of the familiar five pre-defined classes (silk, cotton, linen, wool and worsted).
References
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TL;DR: Research with texture pairs having identical second-order statistics has revealed that the pre-attentive texture discrimination system cannot globally process third- and higher- order statistics, and that discrimination is the result of a few local conspicuous features, called textons.
Abstract: Research with texture pairs having identical second-order statistics has revealed that the pre-attentive texture discrimination system cannot globally process third- and higher-order statistics, and that discrimination is the result of a few local conspicuous features, called textons. It seems that only the first-order statistics of these textons have perceptual significance, and the relative phase between textons cannot be perceived without detailed scrutiny by focal attention.
1,757 citations
03 Oct 1999
TL;DR: In this article, a simulated fabric model is used to understand the relationship between the fabric structure in the image space and in the frequency space, and two significant spectral diagrams are defined and used for analyzing the fabric defect.
Abstract: Many fabric defects are very small and undistinguishable, which makes them very difficult to detect by only monitoring the intensity change. Faultless fabric is a repetitive and regular global texture and Fourier transforms can be applied to monitor the spatial frequency spectrum of a fabric. When a defect occurs in fabric, its regular structure is changed so that the corresponding intensity at some specific positions of the frequency spectrum would change. However, the three-dimensional frequency spectrum is very difficult to analyze. In this paper, a simulated fabric model is used to understand the relationship between the fabric structure in the image space and in the frequency space. Based on the three-dimensional frequency spectrum, two significant spectral diagrams are defined and used for analyzing the fabric defect. These two diagrams are called the central spatial frequency spectrums. The defects are broadly classified into four classes: (1) double yarn; (2) missing yarn; (3) webs or broken fabric; and (4) yarn densities variation. After evaluating these four classes of defects using some simulated models and real samples, seven characteristic parameters for a central spatial frequency spectrum are extracted for defect classification.
455 citations
TL;DR: In this paper, the Hausdorff-Besicovitch dimension is introduced as a measure of the relative balance between long and short-range sources of variation; D can be estimated from the slope of a double logarithmic plot of the semivariogram.
Abstract: Summary
Soil variation has often been considered to be composed of‘functional’ or ‘systematic’ variation that can be explained, and random variation (‘noise’) that is unresolved. The distinction between systematic variation and noise is entirely scale dependent because increasing the scale of observation almost always reveals structure in the noise. The white noise concept of a normally distributed random function must be replaced to take into account the nested, autocorrelated and scale-dependent nature of unresolved variations. Fractals are a means of studying these phenomena. The Hausdorff-Besicovitch dimension D is introduced as a measure of the relative balance between long- and short-range sources of variation; D can be estimated from the slope of a double logarithmic plot of the semivariogram. The family of Brownian linear fractals is introduced as the model of ideal stochastic fractals.
Data from published and unpublished soil studies are examined and compared with other environmental data and simulated fractional Brownian series. The soil data are fractals because increasing the scale of observation continues to reveal more and more detail. But soil does not vary exactly as a Brownian fractal because its variation is controlled by many independent processes that can cause abrupt transitions or local second order stationarity. Estimates of D values show that soil data usually have a much higher proportion of short-range variation than landform or ground water surfaces. The practical implication is that interpolation of soil property values based on observations from single 10 cm auger observations will be unsatisfactory and that some method of bulking or block kriging should be used whenever longrange variations need to be mapped.
440 citations
"Alphabet pattern approach for color..." refers methods in this paper
...To scrutinize the textural and spatial structural uniqueness of the image, so many approaches exist; including texture measures [10], fractal dimension [11]....
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TL;DR: A more complete version of the texton theory is presented, with emphasis on the critical distances within which the density of textons is determined by the preattentive system.
Abstract: A brief outline of the texton theory was given in several review papers (Julesz and Bergen 1983; Julesz 1984a, b, 1985) without going into details. Here a more complete version of the texton theory is presented, with emphasis on the critical distances within which the density of textons is determined by the preattentive system. Particularly some recent findings by Sagi and Julesz (1985a, b) influenced the current version of the texton theory. The stimuli are restricted to drawing composed of line segments that permit a precise definition of neighborhoods and distances to which the line segments and texture elements have to be confined in order to quality for preattentive texture discrimination. These critical distances and the aperture of focal attention are scaled by the average size of the texture elements. Furthermore, it is stressed that even when the stimuli are restricted to line segments, the blobs outlined by line segments behave like textons. The preattentive system ignores the exact shape of these blobs, but is sensitive to their average width, length, and orientation.
359 citations
TL;DR: An automatic vision-based system for quality control of web textile fabrics with high detection rate with good localization accuracy, low rate of false alarms, compatibility with standard inspection tools and low price is presented.
Abstract: This paper presents an automatic vision-based system for quality control of web textile fabrics. The general hardware and software platform developed to solve this problem is presented and a powerful algorithm for defect inspection is proposed. Based on the improved binary, textural and neural network algorithms the proposed method gives good results in the detection of many types of fabric defects under real industrial conditions, where the presence of many types of noise is an inevitable phenomenon. A high detection rate with good localization accuracy, low rate of false alarms, compatibility with standard inspection tools and low price are the main advantages of the proposed system as well as the overall inspection approach.
165 citations
"Alphabet pattern approach for color..." refers background in this paper
...proposed the classification of fabric based on neural network, whose inputs are defect area, center coordinate, horizontal and vertical projection, and co-occurrence matrix [5]....
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...In [4, 5], fabric classification is based on geometrical characteristic of textile image....
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