Takeshi Okumura
Papers
2
Total Citations
7
H-Index
2
About
Takeshi Okumura is a researcher in computer vision, with a focused interest in generic object recognition—a critical capability for applications such as robot vision and image retrieval. His work centers on advancing probabilistic graphical models, particularly Conditional Random Fields (CRFs), to improve how machines identify and classify objects within complex visual scenes. Okumura’s major contributions include integrating Bag-of-Features (BoF) as global features into CRF frameworks, a novel approach that enhances recognition accuracy by combining local region details with broader scene context. He further extended this concept by developing Tree Conditional Random Fields based on hierarchical segmentation, allowing for more structured and efficient recognition across multiple object classes. Although his most-cited papers, such as “Generic Object Recognition using CRF by Incorporating BoF as Global Features” (2009, 5 citations) and “Generic Object Recognition by Tree Conditional Random Field Based on Hierarchical Segmentation” (2010, 2 citations), have modest citation counts, they represent foundational steps in refining CRF-based methods for generic object recognition. Okumura’s work is notable for its methodological rigor and its potential to impact autonomous systems and visual search technologies.
Research Focus
Key Achievements
Top Papers
- 1
- 2