Fabric Weave Pattern and Yarn Color Recognition and Classification Using a Deep ELM Network

Abstract

Usually, a fabric weave pattern is recognized using methods which identify the warp floats and weft floats. Although these methods perform well for uniform or repetitive weave patterns, in the case of complex weave patterns, these methods become computationally complex and the classification error rates are comparatively higher. Furthermore, the fault-tolerance (invariance) and stability (selectivity) of the existing methods are still to be enhanced. We present a novel biologically-inspired method to invariantly recognize the fabric weave pattern (fabric texture) and yarn color from the color image input. We proposed a model in which the fabric weave pattern descriptor is based on the HMAX model for computer vision inspired by the hierarchy in the visual cortex, the color descriptor is based on the opponent color channel inspired by the classical opponent color theory of human vision, and the classification stage is composed of a multi-layer (deep) extreme learning machine. Since the weave pattern descriptor, yarn color descriptor, and the classification stage are all biologically inspired, we propose a method which is completely biologically plausible. The classification performance of the proposed algorithm indicates that the biologically-inspired computer-aided-vision models might provide accurate, fast, reliable and cost-effective solution to industrial automation.

A novel marble recognition system using extreme learning machine with LBP and histogram features

“…Extreme Learning Machine (ELM), proposed by Huang et al is the learning algorithm used for a single layer feed forward Artificial Neural Network (ANN) model 16 . Deep ELM can be used for texture classification of clothes with mix and complex colors 17 . Besides texture classification, the ELM‐based classifier can be used in the classification process of non‐linearly separated hyperspectral images 18,19 .…”

BELMKN: Bayesian Extreme Learning Machines Kohonen Network

“…To perform efficient clustering, it is crucial to choose the right feature learning technique. Therefore, the useful data representations are first extracted using the Extreme Learning Machine (ELM)-based feature learning technique [25,26]. ELM is a non-iterative feature learning technique that uses a single hidden layer [27].…”

“…ELM computes the weights between the hidden and output layer in one step with better generalization [25]. ELM was initially used for classification and regression [26]; recently, Huang et al, 2014 [28] proposed the Unsupervised ELM (US-ELM) to solve the clustering problem. In their study, the number of clusters is assigned apriori and feature extracted data is clustered using k-means algorithm [28].…”

Recognition of the layout of colored yarns in yarn-dyed fabrics

“…With the improvement in image processing technologies, scholars have presented many automatic recognition methods and achieved much impressive progress. 1–3 Their methods show high performance for specific kinds of fabric and can be applied in certain environments. However, there are still some problems: Some existing methods usually depend on a high image resolution, which makes the image acquisition system cumbersome and expensive. Some methods need to tune parameters according to different fabric types, causing low adaptability. The diameters of yarns and weave patterns are diverse, which causes some errors in locating and recognizing the colored yarns. …”

“…With the improvement in image processing technologies, scholars have presented many automatic recognition methods and achieved much impressive progress. [1][2][3] Their methods show high performance for specific kinds of fabric and can be applied in certain environments. However, there are still some problems:…”