Segmentation of defects in textile fabric with robust texture representation and total variation

PurposeVisual quality control on raw textile fabrics is a vital process in weaving factories to ensure their exterior quality (visual defects or imperfection) satisfying customer requirements. Commonly, this critical process is manually conducted by human inspectors, which can hardly provide a fast and reliable inspection results due to fatigue and subjective errors. To meet modern production needs, it is highly demanded to develop an automated defect inspection system by replacing human eyes with computer vision.Design/methodology/approachAs a structural texture, fabric textures can be effectively represented by a linearly summation of basic elements (dictionary). To create a robust representation of a fabric texture in an unsupervised manner, a smooth constraint is imposed on dictionary learning model. Such representation is robust to defects when using it to recover a defective image. Thus an abnormal map (likelihood of defective regions) can be computed by measuring similarity between recovered version and itself. Finally, the total variation (TV) based model is built to segment defects on the abnormal map.FindingsDifferent from traditional dictionary learning method, a smooth constraint is introduced in dictionary learning that not only able to create a robust representation for fabric textures but also avoid the selection of dictionary size. In addition, a TV based model is designed according to defects' characteristics. The experimental results demonstrate that (1) the dictionary with smooth constraint can generate a more robust representation of fabric textures compared to traditional dictionary; (2) the TV based model can achieve a robust and good segmentation result.Originality/valueThe major originality of the proposed method are: (1) Dictionary size can be set as a constant instead of selecting it empirically; (2) The total variation based model is built, which can enhance less salient defects, improving segmentation performance significantly.

The Acquisition of Productive Plural Morphology by Children With Hearing Loss

Purpose Normal-hearing (NH) children acquire plural morphemes at different rates, with the segmental allomorphs /–s, –z/ (e.g., cat-s ) being acquired before the syllabic allomorph /–əz/ (e.g., bus-es ). Children with hearing loss (HL) have been reported to show delays in the production of plural morphology, raising the possibility that this might be due to challenges acquiring different types of lexical/morphological representations. This study therefore examined the comprehension of plural morphology by 3- to 7-year-olds with HL and compared this with performance by their NH peers. We also investigated comprehension as a function of wearing hearing aids (HAs) versus cochlear implants (CIs). Method Participants included 129 NH children aged 3–5 years and 25 children with HL aged 3–7 years (13 with HAs, 12 with CIs). All participated in a novel word two-alternative forced-choice task presented on an iPad. The task tested comprehension of the segmental (e.g., teps, mubz ) and syllabic (e.g., kosses ) plural, as well as their singular counterparts (e.g., tep, mub, koss ). Results While the children with NH were above chance for all conditions, those with HL performed at chance. As a group, the performance of the children with HL did not improve with age. However, results suggest possible differences between children with HAs and those with CIs, where those with HAs appeared to be in the process of developing representations of consonant–vowel–consonant singulars. Conclusions Results suggest that preschoolers with HL do not yet have a robust representation of plural morphology for words they have not heard before. However, those with HAs are beginning to access the singular/plural system as they get older.