Real-time texture error detection on textured surfaces with compressed sensing

Abstract

We present a real-time approach to detect and localise defects in grey-scale textures within a Compressed Sensing framework. Inspired by recent results in texture classification, we use compressed local grey-scale patches for texture description. In a first step, a Gaussian Mixture model is trained with the features extracted from a handful of defect-free texture samples. In a second step, the novelty detection of texture samples is performed by comparing each pixel to the likelihood obtained in the training process. The inspection stage is embedded into a multi-scale framework to enable real-time defect detection and localisation. The performance of compressed grey-scale patches for texture error detection is evaluated on two independent datasets. The proposed method is able to outperform the performance of non-compressed grey-scale patches in terms of accuracy and speed.