Defect detection on patterned fabrics using texture periodicity and the coordinated clusters representation

Abstract

Patterned fabrics may be regarded as periodic textures, which are defined as the regular tessellation of a primitive unit. A patterned fabric is considered as defective when a primitive unit is different from the others. In this paper, we propose a one-class classifier that uses Reduced Coordinated Cluster Representation (RCCR) as features. In the training step, the size of the primitive unit of defect-free fabrics is automatically estimated using a texture periodicity algorithm. After that, the fabrics are split into samples of one unit and their local structure is learnt with the RCCR features in a one-class classifier. During the test step, defective and non-defective fabrics are also split into samples and are analyzed unit by unit. If the features of a given unit do not satisfy the classification criterion, it is considered to be a defect. Among the advantages of the RCCR is that it represents structural information of textures in a low-dimensional feature space with high discrimination performance. Results from experiments on an extensive database of real fabric images show that our method yields accurate detections, outperforming other state-of-the-art algorithms.

A New Fabric Defect Detection Model based on Summed-up Distance Matching Function and Gabor Filter Bank

“…x y is the Gabor filter. Gabor filter bank formed by two Gabor filters is shown in (7). For a normal fabric image, the energy mean after convolution of Gabor filter bank is larger than abnormal part, and variance is smaller.…”

“…Bhajantri proposed an automatic texture periodicity detection method based on distance matching functions (DMFs) and calculated forward differentials to identify fabric defects with continuous periodic textures automatically [6]. Lizarraga-Morales R A, Sanchez-Yanez R E and Baeza-Serrato R used a simplified single cluster classifier with Reduced Coordinated Cluster Representation (RCCR) as features [7]. And estimate the cell size of a defect-free fabric automatically using a texture periodic algorithm during training to detect defective samples.…”

Texture Segmentation: An Objective Comparison between Five Traditional Algorithms and a Deep-Learning U-Net Architecture

“…Texture, and more specifically textural characteristics in images, has been widely studied in the past decades as texture is one of the most important features present in images and can be used for feature extraction [1][2][3][4][5][6][7][8] and classification and segmentation [9][10][11][12][13][14]. The areas of study where texture is present range from crystallographic texture [15], stratigraphy [16,17], food science of potatoes [18] or apples [19], patterned fabrics [20] to natural stone industry [21]. In medical imaging, there is a large volume of research which exploits the use of texture for different purposes, like segmentation or classification in most acquisition modalities like magnetic resonance imaging (MRI) [22][23][24][25][26], ultrasound [27,28], computed tomography (CT) [29][30][31], microscopy [32,33] and histology [34].…”

Texture Segmentation: An Objective Comparison between Traditional and Deep-Learning Methodologies

“…Texture, and more specifically textural characteristics in images, has been widely studied in the past decades as texture is one of the most important features present in images and can be used for feature extraction [1][2][3][4][5][6][7][8] and classification and segmentation [9][10][11][12][13][14]. The areas of study where texture is present range from crystallographic texture [15], stratigraphy [16,17], food science of potatoes [18] or apples [19], patterned fabrics [20] to natural stone industry [21]. In medical imaging, there is a large volume of research which exploits the use of texture for different purposes like segmentation of classification in most acquisition modalities like magnetic resonance imaging (MRI) [22][23][24][25][26], ultrasound [27,28], computed tomography (CT) [29][30][31], microscopy [32,33] and histology [34].…”