We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
A hybrid descriptor for low-textural image stitching in real-time surface inspection systems.
- Authors
Subramanyam, Vasanth; Kumar, Jayendra; Singh, Shiva Nand
- Abstract
Surface inspection systems in the steel industry use multiple machine-vision (MV) cameras to inspect steel sheets for real-time quality control. Conventional approaches are classified into direct, deep-learning-based, and feature-based methodologies. Direct techniques perform poorly on parallax, while deep-learning-based algorithms require higher execution times and are ineffective for real-time applications. We propose a hybrid descriptor that uses defect detection to effectively stitch low-textural images captured by multiple cameras that are evaluated based on matching accuracy, execution time, and quality of stitched images and compared to popular feature-based image descriptor algorithms. Experimental results show that the proposed hybrid descriptor outperforms existing feature descriptors with 91% matching accuracy and an execution time of 49 milliseconds, producing a seamlessly stitched output.
- Subjects
REAL-time control; TEXTURE analysis (Image processing); STEEL industry; SHEET-steel; QUALITY control; PARALLAX
- Publication
Multimedia Tools & Applications, 2024, Vol 83, Issue 7, p20653
- ISSN
1380-7501
- Publication type
Article
- DOI
10.1007/s11042-023-16357-y