Identification of Textile Defects Based on GLCM and Neural Networks

In modern textile industry, Tissue online Automatic Inspection (TAI) is becoming an attractive alternative to Human Vision Inspection (HVI). HVI needs a high level of attention nevertheless leading to low performance in terms of tissue inspection. Based on the co-occurrence matrix and its statistical features, as an approach for defects textile identification in the digital image, TAI can potentially provide an objective and reliable evaluation on the fabric production quality. The goal of most TAI systems is to detect the presence of faults in textiles and accurately locate the position of the defects. The motivation behind the fabric defects identification is to enable an on-line quality control of the weaving process. In this paper, we proposed a method based on texture analysis and neural networks to identify the textile defects. A feature extractor is designed based on Gray Level Co-occurrence Matrix (GLCM). A neural network is used as a classifier to identify the textile defects. The numerical simulation showed that the error recognition rates were 100% for the training and 100%, 91% for the best and worst testing respectively.

Automatic Identification of Butterfly Species Based on Gray-Level Co-occurrence Matrix Features of Image Block

In recent years, automatic identification of butterfly species arouses more and more attention in different areas. Because most of their larvae are pests, this research is not only meaningful for the popularization of science but also important to the agricultural production and the environment. Texture as a notable feature is widely used in digital image recognition technology; for describing the texture, an extremely effective method, graylevel co-occurrence matrix(GLCM), has been proposed and used in automatic identification systems. However,according to most of the existing works, GLCM is computed by the whole image, which likely misses some important features in local areas. To solve this problem, this paper presents a new method based on the GLCM features extruded from three image blocks, and a weight-based k-nearest neighbor(KNN) search algorithm used for classifier design. With this method, a butterfly classification system works on ten butterfly species which are hard to identify by shape features. The final identification accuracy is 98%.

Study of Texture Segmentation and Classification for Grading Small Hepatocellular Carcinoma Based on CT Images

To grade Small Hepatocellular Car Cinoma(SHCC)using texture analysis of CT images,we retrospectively analysed 68 cases of Grade II(medium-differentiation)and 37 cases of Grades III and IV(high-differentiation).The grading scheme follows 4 stages:(1)training a Super Resolution Generative Adversarial Network(SRGAN)migration learning model on the Lung Nodule Analysis 2016 Dataset,and employing this model to reconstruct Super Resolution Images of the SHCC Dataset(SR-SHCC)images;(2)designing a texture clustering method based on Gray-Level Co-occurrence Matrix(GLCM)to segment tumour regions,which are Regions Of Interest(ROIs),from the original and SR-SHCC images,respectively;(3)extracting texture features on the ROIs;(4)performing statistical analysis and classifications.The segmentation achieved accuracies of 0.9049 and 0.8590 in the original SHCC images and the SR-SHCC images,respectively.The classification achived an accuracy of 0.838 and an Area Under the ROC Curve(AUC)of 0.84.The grading scheme can effectively reduce poor impacts on the texture analysis of SHCC ROIs.It may play a guiding role for physicians in early diagnoses of medium-differentiation and high-differentiation in SHCC.