Review article: Automated fabric defect detection-A review

We propose a decision-based, signal-adaptive median filtering algorithm for removal of impulse noise. Our algorithm achieves accurate noise detection and high SNR measures without smearing the fine details and edges in the image. The notion of homogeneity level is defined for pixel values based on their global and local statistical properties. The cooccurrence matrix technique is used to represent the correlations between a pixel and its neighbors, and to derive the upper and lower bound of the homogeneity level. Noise detection is performed at two stages: noise candidates are first selected using the homogeneity level, and then a refining process follows to eliminate false detections. The noise detection scheme does not use a quantitative decision measure, but uses qualitative structural information, and it is not subject to burdensome computations for optimization of the threshold values. Empirical results indicate that our scheme performs significantly better than other median filters, in terms of noise suppression and detail preservation.

http://www.people.cecs.ucf.edu/kasparis/docs/Selective%20Removal%20of%20Impulse%20Noise.pdf

Selective removal of impulse noise based on homogeneity level information

In this letter, we present a new efficient algorithm for the removal of impulse noise from corrupted images while preserving image details. The algorithm is based on the alpha-trimmed mean, which is a special case of the order-statistics filter. However, unlike some of the previous techniques involving order-statistics filters, the proposed method uses the alpha-trimmed mean only in impulse noise detection instead of pixel value estimation. Once a noisy pixel is identified, its value is replaced by a linear combination of its original value and the median of its local window. Extensive computer simulations indicate that our algorithm provides a significant improvement over many other existing techniques

An efficient detail-preserving approach for removing impulse noise in images

This paper reviews automated visual-based defect detection approaches applicable to various materials, such as metals, ceramics and textiles. In the first part of the paper, we present a general taxonomy of the different defects that fall in two classes: visible (e.g., scratches, shape error, etc.) and palpable (e.g., crack, bump, etc.) defects. Then, we describe artificial visual processing techniques that are aimed at understanding of the captured scenery in a mathematical/logical way. We continue with a survey of textural defect detection based on statistical, structural and other approaches. Finally, we report the state of the art for approaching the detection and classification of defects through supervised and non-supervised classifiers and deep learning.

Visual-Based Defect Detection and Classification Approaches for Industrial Applications-A SURVEY.

Ceramic and tile industries should indispensably include a grading stage to quantify the quality of products. Actually, human control systems are often used for grading purposes. An automatic grading system is essential to enhance the quality control and marketing of the products. Since there generally exist six different types of defects originating from various stages of tile manufacturing lines with distinct textures and morphologies, many image processing techniques have been proposed for defect detection. In this paper, a survey has been made on the pattern recognition and image processing algorithms which have been used to detect surface defects. Each method appears to be limited for detecting some subgroup of defects. The detection techniques may be divided into three main groups: statistical pattern recognition, feature vector extraction and texture/image classification. The methods such as wavelet transform, filtering, morphology and contourlet transform are more effective for pre-processing tasks. Others including statistical methods, neural networks and model-based algorithms can be applied to extract the surface defects. Although, statistical methods are often appropriate for identification of large defects such as Spots, but techniques such as wavelet processing provide an acceptable response for detection of small defects such as Pinhole. A thorough survey is made in this paper on the existing algorithms in each subgroup. Also, the evaluation parameters are discussed including supervised and unsupervised parameters. Using various performance parameters, different defect detection algorithms are compared and evaluated.

Surface defect detection in tiling Industries using digital image processing methods: analysis and evaluation.

We propose a conditional median filter that is able to
preserve significantly more image detail than the conventional median filter when suppression of impulsive noise is desired. It does so by means of bypassing filtering when the local signal variation
is below an adaptively adjusted threshold. Another advantage of our algorithm is that it is simple to implement and it can be used with any of the available fast median filter algorithms. In addition, it can be easily integrated into existing median filter hardware. We compare the complexity of our algorithm with that of another proposed algorithm with similar filtering objectives. The performance of the new algorithm on real images is compared to that of median and center-weighted median filters.

Detail-preserving adaptive conditional median filters

http://people.cecs.ucf.edu/kasparis/docs/texture%20description%20using%20fractal%20and%20energy%20features.pdf

Texture description using fractal and energy features

Fractal geometry receives recently increasing attention in analyzing natural scenes The fractal dimension of a surface image has been used as segmentation feature, but since it is not sufficient to characterize important configurative texture characteristics, additional features are necessary In this work we use a combination of fractal and non-fractal features to segment and classify natural textures

Qing Chen

Papers

Detail-preserving adaptive conditional median filters

Texture description using fractal and energy features

Fractal-based multifeature texture description