• Proceedings of the KIEE Conference
• /
• 대한전기학회 1988년도 전기.전자공학 학술대회 논문집
• /
• pp.959-962
• /
• 1988

This paper describes an automatic visual inspection system for fabric defects based on pattern recognition techniques. The inspection for fabric defects can be separated into three sequences of operations which are the detection of fabric defects[1], the classification of figures of fabric defects, and the classification of fabric defects. Comparing projections of defect-detected images with the predefined complex, the classification accuracy of figures of fabric defects was found to be 95.3 percent. Employing the Bayes classifier using cluster shade in SGLDM and variance in decorrelation method as features, the classification accuracy of regional figure defects was found to be 82.4 percent. Finally, some experimental results for line and dispersed figures of fabric defects are included.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 한국정보통신학회 2019년도 춘계학술대회
• /
• pp.442-444
• /
• 2019

Fabric defect classification is an important issue in fabric quality control. However, automated classification is difficult because it is hard to identify various types of defects in images. classification of fabric defects mostly rely on human ability. In this paper, to solve this problem we apply Convolutional Neural Networks (CNN) for fabric defect classification. To make training CNN easier, we propose a method that is manually attentive defect regions in images. we compare the proposed method with the original image and confirm that the proposed method is effective for learning.

• Journal of Fashion Business
• /
• 제11권3호
• /
• pp.133-151
• /
• 2007

The role of fabric now directly related with the expression of the beauty of clothing and it provides new and creative ideas. This study was aimed at reviewing basic data that can be used in systematic design development through fabric expression for today's fashion designers who must study unique, original fashion design development. For systematic development of design technique through fabric expression, fabric expression methods and characteristics, aesthetic characteristics and fabric design of 'art to wear' were reviewed and the results are as follows. First, the highly wrought fabric expression of art to wear was confirmed to be comprehending a message within itself. Second, aesthetic characteristics of fabric expression used in art to wear can be classified as decorativeness, extensity, 2-D pictorialness, handicraft, compounding and rearrangement, and 3-D characteristics. Third, the 6 aesthetic characteristics have unique design features and aesthetic categories. The understanding the fabric expression techniques through study on the classification of the fabric expression in 'art to wear' is expected to be extended to proposition of creative direction and inspiration of modern fashion.

• The Research Journal of the Costume Culture
• /
• 제12권3호
• /
• pp.339-353
• /
• 2004

Purposes of this study were to analyzed coat types and characteristics of coat of young persons, and search whether fashion trend is reflected on coat. Data collected pictures that they are wearing dress in street of Daejeon city 3 places that there are much the rising generations at November, 1999. This study target was from teens latter half to 20 opening part, 154 women. Data analyzed content analysis, frequency analysis, crossing and the result is as following. First, classification standard of coat was textile fabric, form of detail and ornament. Second, coat could classify in three types, type 1 was traditional duffle coat style that is distinguished by form of detail and ornament(hood and button). Type 2 was classified property of textile fabric that used leather, padding, fur etc., and type 3 was classified by collar detail of woolen fabric coat. Specially, ornamental fur of woolen coat perceived visually strong. And design detail of coat showed significant difference in coat type. That is, duffle coat type was designed patch pocket and toggle, woolen fabric coat type was hidden button and seam pocket. Third, fashion tendency of coat was proved that is reflecting part of predicted fashion trend.

• Journal of the Korea Fashion and Costume Design Association
• /
• 제16권4호
• /
• pp.117-136
• /
• 2014

The purpose of this study is to present a classification system of the hand-made and machine-made lace according to the configuration method and re-make the category and definition of lace to consider the emergence and development of major laces techniques. The re-made category and definition of the lace is as follows. The lace usually consists of ground and motifs, however, the techniques of netting and sprang are suitable for making ground than motif, so I think it is appropriate to exclude them from the category of the lace. Many scholars are excluded openwork embroidery fabric from the category of the lace. But, an openwork embroidery fabric is the basis of a needle point lace called true lace and is consist of motif and ground. I think it is appropriate to include it in the category of the lace. I think it is also appropriate to include in the category of the lace that the eyelet embroidery fabric which mimics the openwork embroidery fabric in the machine. Lace is redefined that a fabric with openwork decoration consists of motif and ground, constructed by a variety of ways such as plaiting, twisting, looping, knotting of threads or embroidering by hand or machine. The classification of the lace is presented as follows. Hand-made lace is classified bobbin lace, needle point lace, embroidery lace, knotted lace, crochet lace, and knitting lace. Machine-made lace is classified raschel lace, leaver lace, torchon lace, and machine-made embroidery laces which include tool lace, eyelet embroidery lace, chemical lace, etc.

• Journal of the Korean Society of Costume
• /
• 제64권5호
• /
• pp.50-61
• /
• 2014

Fashion has been compared to art since Japanese avant-garde fashion designers expanded the thoughts about conceptual fashion in late 1970s. The fashion designers focused on the materiality of fashion textiles by placing more importance on it than the shapes. This bears a striking resemblance to contemporary art of 1960s and 1970s as many artists used soft materials like felt, fabric, rubber to emphasize themselves. This study establishes the materiality of fabric, which can be found in both contemporary art and fashion. The classification of materiality consists of flexibility, humanizing and temporality. In this work, there is a significant disparity between contemporary art and fashion.

• Journal of Korean Association for Spatial Structures
• /
• 제16권2호
• /
• pp.55-60
• /
• 2016

The Membrane structure has a number of problems in the application of a fireproof code based on general buildings codes. Thus, the fireproof code of membrane structure is necessary to activate the construction of the membrane structure. Because it requires a systematic classification of fire retardant and flame proof performance of membrane material. Fire retardant and flame proof tests are conducted on membrane materials mostly used in current construction to propose the fire and flame retardant performance criteria of membrane materials. Fire and flame retardant tests results, PTFE membrane material with the glass fiber fabric have a limit-combustible performance. PVDF membrane material with the polyester fabric does not ensure the fire retardant performance, but this membrane material has the flame retardant performance of a thick fabric. Also, ETFE does not ensure the fire retardant performance, but this membrane material has the flame retardant of a thin fabric.

• Physical Therapy Korea
• /
• 제22권2호
• /
• pp.52-58
• /
• 2015

Balance control deficits have been indicated to be a primary problem among cerebral palsy (CP) patients. Fabric ankle foot orthosis (AFO) can allow more efficient balance control by facilitating proprioception. The purpose of this study was to investigate the immediate effect of fabric AFO on balance, compared to a barefoot condition in children with unilateral CP. Twelve children with unilateral CP participated in this study. Their balance ability was evaluated using pediatric balance scale and bubble test. Both pediatric balance scale and bubble test showed significant improvement with the use of the fabirc AFO (p<.05). The fabric AFO could improve functional balance ability, and promote better balance among children with unilateral CP. We demonstrated that fabric AFO contributed to improving balance among children with unilateral CP, classified as Gross Motor Function Classification System level I and II. Consequently, fabric AFO might be an assistive device leading to the improvement of balance instead of the typical AFOs.

• The Journal of Information Systems
• /
• 제32권4호
• /
• pp.149-162
• /
• 2023

Purpose In the textile industry, fabric defects significantly impact product quality and consumer satisfaction. This research seeks to enhance defect detection by developing a transformer-based deep learning image segmentation model for learning high-dimensional image features, overcoming the limitations of traditional image classification methods. Design/methodology/approach This study utilizes the ZJU-Leaper dataset to develop a model for detecting defects in fabrics. The ZJU-Leaper dataset includes defects such as presses, stains, warps, and scratches across various fabric patterns. The dataset was built using the defect labeling and image files from ZJU-Leaper, and experiments were conducted with deep learning image segmentation models including Deeplabv3, SegformerB0, SegformerB1, and Dinov2. Findings The experimental results of this study indicate that the SegformerB1 model achieved the highest performance with an mIOU of 83.61% and a Pixel F1 Score of 81.84%. The SegformerB1 model excelled in sensitivity for detecting fabric defect areas compared to other models. Detailed analysis of its inferences showed accurate predictions of diverse defects, such as stains and fine scratches, within intricated fabric designs.

• Journal of Advanced Navigation Technology
• /
• 제26권2호
• /
• pp.113-118
• /
• 2022

Motivated by people highlighting important phrases while reading or taking notes we propose a neural network training method by highlighting defective pixel areas to classify effectively defect types of images with complex background textures. To verify our proposed method we apply it to the problem of classifying the defect types of tire band fabric images that are too difficult to classify. In addition we propose a backlight highlighting technique which is tailored to the tire band fabric images. Backlight highlighting images can be generated by using both the GradCAM and simple image processing. In our experiment we demonstrated that the proposed highlighting method outperforms the traditional method in the view points of both classification accuracy and training speed. It achieved up to 13.4% accuracy improvement compared to the conventional method. We also showed that the backlight highlighting technique tailored for highlighting tire band fabric images is superior to a contour highlighting technique in terms of accuracy.