Sameera Fonseka
Papers
1
Total Citations
10
H-Index
1
About
Dr. Sameera Fonseka is a researcher whose work sits at the intersection of computer vision, industrial automation, and electronics manufacturing. Her most cited contribution, "Feature Extraction and Template Matching Algorithms Classification for PCB Fiducial Verification" (2018), tackles a critical bottleneck in Automatic Optical Inspection (AOI) systems—the precise alignment of printed circuit boards (PCBs) during surface mount device (SMD) inspection. By systematically classifying and evaluating feature extraction and template matching algorithms for fiducial mark verification, her work provides a foundational framework for improving the speed and accuracy of PCB alignment in high-volume electronics assembly. With 10 citations, this paper serves as a key reference for engineers and researchers developing robust AOI systems. Dr. Fonseka’s research directly addresses real-world industrial challenges, bridging the gap between algorithmic theory and practical manufacturing quality control. Her contributions are particularly valuable for students and professionals working in automated inspection, pattern recognition, and Industry 4.0 applications, offering clear guidance on selecting optimal algorithms for reliable PCB positioning in demanding production environments.
Research Focus
Key Achievements
Top Papers
- 1