Authors:
Fabian Timm
;
Sascha Klement
;
Erhardt Barth
and
Thomas Martinetz
Affiliation:
University of Luebeck, Germany
Keyword(s):
Feature extraction, One-class classification, Welding seam inspection, Machine vision.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computer Vision, Visualization and Computer Graphics
;
Feature Extraction
;
Features Extraction
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Image and Video Analysis
;
Informatics in Control, Automation and Robotics
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Signal Processing, Sensors, Systems Modeling and Control
;
Soft Computing
;
Statistical Approach
;
Theory and Methods
Abstract:
We present a framework for automatic inspection of welding seams based on specular reflections. Therefore, we introduce a novel feature set -- called specularity features (SPECs) -- describing statistical properties of specular reflections. For classification we use a one-class support-vector approach. The SPECs significantly outperform statistical geometric features and raw pixel intensities, since they capture more complex characteristics and depencies of shape and geometry.We obtain an error rate of 9%, which corresponds to the level of human performance.