T. Bieseman
Papers
1
Total Citations
24
H-Index
1
About
T. Bieseman is a researcher whose work sits at the intersection of computer vision and geometric encoding, with a particular focus on efficient object recognition and spatial localization. His most-cited contribution, the 2005 paper "Visual Object Recognition Using Pseudo-random Grid Encoding," introduces a novel grid node indexing method based on pseudo-random binary array (PRBA) encoding. This technique is notable for its efficiency: it requires only one code bit per grid step, regardless of the desired resolution—a significant departure from traditional methods that scale poorly. Bieseman demonstrates the method's versatility by applying it to both 3-D object recognition and 2-D absolute position recovery, showing how a single encoding scheme can serve multiple vision tasks. With 24 citations, this work has provided a practical foundation for researchers seeking lightweight, scalable encoding solutions in structured light and spatial mapping. Bieseman's contributions are particularly valuable for applications in robotics and augmented reality, where efficient, low-overhead encoding is critical.
Research Focus
Key Achievements
Top Papers
- 1Visual Object Recognition Using Pseudo-random Grid Encoding24 citations · 2005