A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Structural approaches <s> A technique to analyze patterns in terms of individual texture primitives and their spatial relationships is described. The technique is applied to natural textures. The descriptions consist of the primitive sizes and their repetition pattern if any. The derived descriptions can be used for recognition or reconstruction of the pattern.* INTRODUCTION Areas of an image are better characterized by descriptions of their texture than by pure intensity information. Texture is most easily described as the pattern of the spatial arrangement of different intensities (or colors). The different textures in an image are usually very apparent to a human observer, but automatic description of these patterns has proved to be very complex. In this research, we are concerned with a description of the texture which corresponds, in some sense, to a description produced by a person looking at the image. Many statistical textural measures have been proposed in the past [1-4]. Reference 1 gives a good review of various texture analysis methods. Among the statistical measures which have been discussed, and used, analysis of generalized gray-level co-occurrence matrices [2], analysis of edge directions with co-occurrence matrices [3], and analysis of the edges (or micro-edges) in a subwindow [4]. The statistical methods, by themselves, do not produce descriptions in the form which we desire. Some of the measures may indicate certain underlying structures in the pattern, but do not produce a general description. The Fourier transform has been used to determine some structural descriptions but was only partially successful for more complex patterns [5]. <s> BIB001 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Structural approaches <s> From a structural point of view, a textured image is considered to be composed of skeleton and background primitives which occur repeatedly according to certain placement rule. Identification of defects in textured images is an important topic in computer vision. In this paper, an approach to defect detection is developed. A textured image is first thresholded using histogram analysis. Then it is mapped into a special data structure called the skeleton representation. Based on both location and length histograms from the data structure, several statistical measurements are defined. These measurements are used to identify and locate defects such as fluctuation, mean jump, and end influency in the textured image. The experimental results show satisfactory performance of this approach and its potential usefulness in industrial environments. <s> BIB002 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Structural approaches <s> An important area of automatic industrial inspection that has been largely overlooked by the recent wave of research in machine vision applications is the detection of defects in textured surfaces. In this paper, we present different algorithms for the detection of surface abnormalities both in the chromatic and structural properties of random textures. We present very promising results on the detection of cracks, blobs, and chromatic defects in ceramic and granite tiles. <s> BIB003 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Structural approaches <s> Machine vision and automatic surface inspection has been an active field of research during the last few years. However, very little research has been contributed to the area of defect detection in textured images, especially for the case of random textures. In this paper, we propose a novel algorithm that uses colour and texture information to solve the problem. A new colour clustering scheme based on human colour perception is developed. The algorithm is training based and produces very promising results on defect detection in random textured images and in particular, granite images. <s> BIB004 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Structural approaches <s> Morphological operations such as erosion and opening are applied to both direct and spatially filtered images of test fabrics to identify defects. Detecting defects morpholog ically on spatially filtered images of fabrics produces better results, particularly when the fabric is fine and contains defects of small size. The diffraction pattern of the test fabric is obtained optically by illuminating it with a collimated laser beam. A spatial filter is placed at the Fourier plane to remove the periodic grating structure of the fabric from the image. Morphological operations with a critically selected structuring element are then applied to the image after suitable pre-processing. <s> BIB005
In structural approaches, texture is characterised by texture primitives or texture elements, and the spatial arrangement of these primitives BIB001 . Thus, the primary goals of structural approaches are firstly to extract texture primitives, and secondly to model or generalise the spatial placement rules. The texture primitive can be as simple as individual pixels, a region with uniform graylevels, or line segments. The placement rules can be obtained through modelling geometric relationships between primitives or learning statistical properties from texture primitives. In BIB002 , Chen and Jain proposed a structural approach to identify defects in textile images. The image was first thresholded using histogram analysis and then was mapped into a data structure which represents the skeleton structure of the texture. Statistical measurements were taken from both location and length histograms of the skeleton. These measurements were compared with a pre-defined acceptance range which was learnt from defect-free samples to detect defects. Kittler et al. BIB003 BIB004 used K-means clustering to split randomly textured tile images into binary stacks, in which blob analysis was performed to measure the primitives. The measurements included size, perimeter fractality, elongatedness, and spatial distribution. In BIB005 , the authors applied morphological operations to highlight defects in fabrics. Wen and Xia  performed leather surface defect detection by extracting edge segments and statistically evaluating those edges, for example, based on their length and strength.