Texture inspection for defects using neural networks and support vector machines

Ajay Kumar Pathak, Helen C. Shen

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

24 Citations (Scopus)


This paper investigates two methods for the detection of defects on textured surfaces using neural networks and support vector machines. Every pixel from the inspection image is characterized by a feature vector, which serves as a local measure of homogeneity of texture. The feature vectors from the gray-level arrangement of neighboring pixels are transformed to eigenspace using Principal Component Analysis (PCA). The transformed features from a predetermined set of training images are used to train the classifier. The trained classifier is used to classify every pixel from inspection image into two-class, i.e. with- or without-defect. The experimental results on real fabric defects show that the proposed scheme can successfully segment the defects from the inspection images.
Original languageEnglish
Title of host publicationIEEE International Conference on Image Processing
Publication statusPublished - 1 Jan 2002
Externally publishedYes
EventInternational Conference on Image Processing (ICIP'02) - Rochester, NY, United States
Duration: 22 Sept 200225 Sept 2002


ConferenceInternational Conference on Image Processing (ICIP'02)
Country/TerritoryUnited States
CityRochester, NY

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


Dive into the research topics of 'Texture inspection for defects using neural networks and support vector machines'. Together they form a unique fingerprint.

Cite this