Ramani Pichumani
Papers
1
Total Citations
18
H-Index
1
About
Ramani Pichumani is a researcher at the forefront of applying deep learning to non-destructive evaluation and 3D imaging. His key research areas center on computer vision, automated object detection, and segmentation within complex volumetric data, particularly for industrial and security applications. Pichumani’s major contribution lies in pioneering the use of state-of-the-art deep learning models to automatically identify and measure buried package features in 3D X-ray images—a task traditionally reliant on manual, time-consuming analysis. His most cited work, "Automated Attribute Measurements of Buried Package Features in 3D X-ray Images using Deep Learning" (2021), has garnered 18 citations, demonstrating its growing influence in bridging the gap between medical/robotics-inspired AI techniques and practical industrial inspection. By adapting advanced segmentation architectures for challenging, occluded environments, Pichumani has opened new pathways for enhancing security screening and quality control. His research not only showcases the versatility of deep learning beyond conventional domains but also promises to accelerate and improve the accuracy of detecting critical structures like through-hole components in dense 3D scans.
Research Focus
Key Achievements
Top Papers
- 1