Defect Classification in Fabric Web Material using LabVIEW

Revathy, G.; Vidhyalakshmi, P.

doi:10.5120/10570-5665

Cited by 4 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Training of data and matching strategies are formulated based on the confidence measures. Kalaiselvi and et.al [20] have proposed a learning algorithm to create alerts incase of data imbalance during the verification of electronic transactions. In order to secure the transaction a blocking rule is developed.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of Child Items in a Gold Tree using Support Vector Machine Classifier

Sabeenian*,

Paramasivam,

et al. 2019

IJRTE

View full text Add to dashboard Cite

Sorting of images has been a challenge in Machine Learning Algorithms over the years. Various algorithms have been proposed to sort an image but none of them are able to sort the image clearly. The drawback of the existing systems is that the sorted image is not clearly identified. So, to overcome this drawback we have proposed a novel approach to sort the children of a tree and match them with the existing designs. The images will be sorted on the basis of the class of the image. The images are taken from the image and manual binning of those images are done. Then the images are trained and tested. GLCM feature is extracted from the trained and tested images which are later on fed to the SVM classifier. The classification of image is then done with the help of SVM classifier. Around 7000 images are trained on SVM and used for classification. More than 300 different classes have been created in the database for comparison. Realtime images of child items are captured and fed to the SVM for classifying. The main application of this image is the use in distinguishing the designs in the ornaments. The various parts of the ornaments can be differentiated clearly. Thus, the proposed method is precise as compared to the existing methods.

show abstract

Section: Literature Reviewmentioning

confidence: 99%