A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Textural Defect Detection <s> Abstract Texture is the term used to characterize the surface of a given object or phenomenon and it is undoubtedly one of the main features used in image processing and pattern recognition. It is commonly agreed that texture analysis plays a fundamental role in classifying and outlining the significant regions of a given gray level image. Texture can be seen in images ranging from multispectral remote sensed data to microscopic images. A solution to the texture analysis problem will greatly advance the image processing and pattern recognition fields and it will also bring much benefit to many possible industrial applications. This survey presents the state-of-the-art in the texture analysis field and it includes also its relation to scientific fields like artificial intelligence and visual perception. The paper outlines the main issues and difficulties and then it presents most of the relevant methods used today in texture analysis. We conclude by outlining directions for future research as they are perceived today in the image processing community. <s> BIB001 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Textural Defect Detection <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> BIB002
Texture is one of the most important characteristics in identifying defects or flaws. Fig. 1 shows some example defects in different types of material. It provides important information for recognition and interpolation. In fact, the task of detecting defects has been largely viewed as a texture analysis problem. Features with large inter-class variations and small intra-class variations are sought to better separate differing textures. Much effort has been put into extracting useful texture features. As it is not practical to provide an exhaustive survey of all texture features, this section concentrates on those techniques that have been widely used in texture analysis or demonstrate good potential for application to automatic inspection. With reference to several texture analysis survey papers  BIB001  BIB002   , we categorise texture analysis techniques used for visual inspection four ways: statistical approaches, structural approaches, filter based approaches, and model based approaches. As already noted, colour texture analysis is separately discussed later. Table 1 shows a summary list of some of the key texture analysis methods that have been applied to defect detection. Clearly, statistical and filter based approaches have been very popular.