# A Novel Dataset for Fabric Defect Detection: Bridging Gaps in Anomaly Detection
## 2 Related Work
### Fabric Defect Detection
#### 2.2.1 Traditional Methods
There are many works that use this technique across many types of defects, in different materials such as ceramics [48], electronic surfaces [49], solar cells [50], and other industrial images [51]. **Wavelet transform**. The wavelet transform technique was developed as an alternative to the Fourier transform, to achieve multi-resolution signal decomposition. This transform converts an image into a series of wavelets, small waves of varying frequency, which provide information on horizontal, vertical, and diagonal directions in that given image [52,53]. Contemporarily, the wavelet transform is mostly used as an intermediate image preprocessing step or as a feature extractor for neural networks [54, 55, 56]. **Gabor transform**. Gabor filters are a well-known method for analysing textured images, using a joint or spatial-frequency representation. These filters use a Gaussian distribution function and can be customized with different scale and angle values according to the analysed texture [2]. This approach attempts the optimal joint localization in spatial and spatial frequency domains [57]. In regards to fabric defect detection, this approach has been used many times over the last decades [58, 59, 60] but more recently has been mostly used as a feature extractor for machine learning methods [61, 62, 63]. Model-Based Approaches  
These revolve around the construction of an image model that can both describe and synthesize texture. They are most effective with fabric images with stochastic surface variations, or for randomly textured fabrics for which statistical or spectral approaches are ineffective [64]. While there are many different types of approaches, the literature is mostly focused on autoregressive models and Markov Random Fields (MRFs). **Autoregressive models**. These models characterize the linear dependence of pixels in any given textured image. As such, to compute it, one is only required to solve a system of linear equations, which requires much less computational time, making this a widely used technique for many areas [65]. However, this technique does not seem to be used much for fabric defect detection. **Markov Random Fields**. Markov Random Fields (MRFs) model context-dependent entities, such as pixels, which depend on their neighbouring pixels, by combining statistical and structural information. They are often used in segmentation [66] or classification problems [67]. In recent years, very few works were found exploring this approach, which casts doubt regarding its applicability in this area [68, 69]. Structural-Based Approaches  
Structural approaches consider the fabric texture as a composition of texture elements, referred to as texture primitives, with a certain spatial arrangement, according to arrangement rules. The goal for these approaches then is to extract the texture primitives, which can consist of individual pixels, uniform grey-level regions, or line segments, and from there infer their spatial arrangement rules, by learning their statistical properties or modelling geometric relationships. This approach is considered more effective in regular textures [70].