Ayşın Ertüzün

An efficient method for texture defect detection: sub-band domain co-occurrence matrices

Abstract In this paper, an efficient algorithm, which combines concepts from wavelet theory and co-occurrence matrices, is presented for detection of defects encountered in textile images. Detection of defects within the inspected texture is performed first by decomposing the gray level images into sub-bands, then by partitioning the textured image into non-overlapping sub-windows and extracting the co-occurrence features and finally by classifying each sub-window as defective or non-defective with a Mahalanobis distance classifier being trained on defect free samples a priori. The experimental results demonstrating the use of this algorithm for the visual inspection of textile products obtained from the real factory environment are also presented. Experiments show that focusing on a particular band with high discriminatory power improves the detection performance as well as increases the computational efficiency.

Defect detection in textile fabric images using wavelet transforms and independent component analysis

In this paper, a new method based on the use of wavelet transformation prior to independent component analysis for solving the problem of defect detection in textile fabric images is presented. Different subbands of the wavelet packet tree scheme of the defect-free subwindows are obtained and independent components of these subbands are calculated as basis vectors. The true feature vectors corresponding to these basis vectors are computed. The test subwindow is labeled as defective, or not according to the Euclidean distance between the true feature vector representing the non-defective regions and the feature vector of the subwindow under test. The advantage of adding wavelet analysis prior to the independent component analysis is presented.

Using perceptual relation of regularity and anisotropy in the texture with independent component model for defect detection

This paper addresses the raw textile defect detection problem using independent components approach with insights from human vision system. Human vision system is known to have specialized receptive fields that respond to certain type of input signals. Orientation-selective bar cells and grating cells are examples of receptive fields in the primary visual cortex that are selective to periodic- and aperiodic-patterns, respectively. Regularity and anisotropy are two high-level features of texture perception, and we can say that disruption in regularity and/or orientation field of the texture pattern causes structural defects. In our research, we observed that independent components extracted from texture images give bar or grating cell like results depending on the structure of the texture. For those textures having lower regularity and dominant local anisotropy (orientation or directionality), independent components look similar to bar cells whereas textures with high regularity and lower anisotropy have independent components acting like grating cells. Thus, we will expect different bar or grating cell like independent components to respond to defective and defect-free regions. With this motivation, statistical analysis of the structure of the texture by means of independent components and then extraction of the disturbance in the structure can be a promising approach to understand perception of local disorder of texture in human vision system. In this paper, we will show how to detect regions of structural defects in raw textile data that have certain regularity and local orientation characteristics with the application of independent component analysis (ICA), and we will present results on real textile images with detailed discussions.

Texture defect detection using subband domain co-occurrence matrices

In this paper, a new defect detection algorithm for textured images is presented. The algorithm is based on the subband decomposition of gray level images through wavelet filters and extraction of the co-occurrence features from the subband images. Detection of defects within the inspected texture is performed by partitioning the textured image into non-overlapping subwindows and classifying each subwindow as defective or nondefective with a mahalanobis distance classifier being trained on defect free samples a priori. The experimental results demonstrating the use of this algorithm for the visual inspection of textile products obtained from the real factory environment are also presented.

Comparative evaluation of texture analysis algorithms for defect inspection of textile products

Quality control is one of the basic issues in textile industry. Texture analysis plays an important role in the automated visual inspection of texture images to detect their defects. For this purpose, model-based and feature-based methods are implemented and tested for textile images in a laboratory environment. The methods are compared in terms of their success rates in determining the defects.

An improved 2-D lattice filter and its entropy relations

Abstract In this paper, an improved lattice filter structure to model two-dimensional (2-D) autoregressive (AR) fields is presented. This work is the generalization of the three-parameter lattice filter developed by Parker and Kayran. The proposed structure generates four prediction error fields (one forward and three backward prediction error fields) at the first stage. After the first stage, two additional prediction error fields are generated using two of the backward prediction error fields at the output of the first stage. This leads to six prediction error fields whose linear combination defines the successive lattice stages and the reflection coefficients. A recursive relationship between the reflection coefficients of the lattice filter and the AR coefficients is derived. In addition, the new structure and the three-parameter lattice filter are compared from information-theoretic point of view. The entropy calculations are carried out for Gaussian distributed data. It is concluded that the new structure approximates the maximum entropy more closely compared to the three-parameter structure. The increase in entropy naturally leads to a more reliable and better modelling of AR data fields.

Applications of multiwavelet techniques to image denoising

The developments in wavelet theory have given rise to the wavelet thresholding method, for extracting a signal from noisy data. Multiwavelets, wavelets with several scaling functions, have been introduced and they offer simultaneous orthogonality, symmetry and short support; which is not possible with ordinary wavelets, also called scalar wavelets. This property makes multiwavelets more suitable for various signal processing applications, especially compression and denoising. Like scalar wavelets, multiwavelets can be realized as filterbanks, however the filterbanks are now matrix-valued; requiring two or more input streams, which can be accomplished by prefiltering. Several thresholding methods to be used with different multiwavelets for image denoising are presented. The performances of multiwavelets are compared with those of scalar wavelets. Simulations reveal that multiwavelet based image denoising schemes outperform wavelet based methods both subjectively and objectively.

Modeling of non-stationary autoregressive alpha-stable processes by particle filters

In the literature, impulsive signals are mostly modeled by symmetric alpha-stable processes. To represent their temporal dependencies, usually autoregressive models with time-invariant coefficients are utilized. We propose a general sequential Bayesian modeling methodology where both unknown autoregressive coefficients and distribution parameters can be estimated successfully, even when they are time-varying. In contrast to most work in the literature on signal processing with alpha-stable distributions, our work is general and models also skewed alpha-stable processes. Successful performance of our method is demonstrated by computer simulations. We support our empirical results by providing posterior Cramer-Rao lower bounds. The proposed method is also tested on a practical application where seismic data events are modeled.

Modeling non-Gaussian time-varying vector autoregressive processes by particle filtering

We present a novel and general methodology for modeling time-varying vector autoregressive processes which are widely used in many areas such as modeling of chemical processes, mobile communication channels and biomedical signals. In the literature, most work utilize multivariate Gaussian models for the mentioned applications, mainly due to the lack of efficient analytical tools for modeling with non-Gaussian distributions. In this paper, we propose a particle filtering approach which can model non-Gaussian autoregressive processes having cross-correlations among them. Moreover, time-varying parameters of the process can be modeled as the most general case by using this sequential Bayesian estimation method. Simulation results justify the performance of the proposed technique, which potentially can model also Gaussian processes as a sub-case.

Wind Speed Forecasting Based on Second Order Blind Identification and Autoregressive Model

Wind power may present undesirable discontinuities and fluctuations due to considerable variations in wind speed, which may affect adversely the smooth operation of the grid. Effective wind forecast is essential in order to report the amount of energy supply with high accuracy, which is crucial for planning energy resources for power system operators. Variations in wind power cannot be sufficiently estimated by persistence type basic forecasting methods particularly in medium and long terms. Therefore a new statistical method is presented here in this paper based on independent component analysis (ICA) and autoregressive (AR) model. ICA is utilized in order to exploit the hidden factors which may exist in the wind speed time-series. It is understood that ICA, especially ICA methods based on exploiting the time structure like second order blind identification (SOBI) can be used as a preliminary step in wind speed forecasting.