Hideaki Orii
Papers
1
Total Citations
2
H-Index
1
About
Hideaki Orii’s research lies at the intersection of computer vision, robotics, and intelligent automation, with a particular focus on enabling machines to perceive and interact meaningfully with human-centered environments. His most cited work, "A SIFT Feature-Based Template Matching Method for Detecting and Counting Objects in Life Space" (2010), addresses a fundamental challenge in autonomous robotics: reliably counting objects in real-world, unstructured settings such as nursing care or medical facilities. By leveraging scale-invariant feature transform (SIFT) for robust template matching, Orii contributed a method that enhances a robot’s ability to recognize and tally objects despite variations in lighting, perspective, or occlusion—a critical step toward deploying autonomous systems in caregiving contexts. Though his citation count is modest, the work’s practical orientation and early focus on assistive robotics underscore its relevance to ongoing efforts in healthcare automation. Orii’s research exemplifies how foundational computer vision techniques can be adapted for socially impactful applications, bridging the gap between laboratory algorithms and real-world assistive technologies.
Research Focus
Key Achievements
Top Papers
- 1