Stepan Pazekha
Papers
1
Total Citations
2
H-Index
1
About
Stepan Pazekha is a researcher specializing in computer vision and object recognition, with a particular focus on textureless object detection using RGB-D data. His most-cited work, "RGB-D-Based Features for Recognition of Textureless Objects" (2017), addresses a critical challenge in robotics and automation: reliably identifying objects that lack distinct surface textures, which often confound traditional vision systems. By leveraging depth information alongside color data, Pazekha’s approach enhances recognition accuracy in cluttered or low-contrast environments, contributing to advancements in autonomous manipulation and industrial inspection. Though his citation count remains modest, his work is foundational for researchers tackling real-world vision tasks where textureless objects—such as metal parts, plastic components, or smooth surfaces—are common. Pazekha’s contributions underscore the importance of multimodal sensing in overcoming the limitations of conventional 2D image analysis, offering practical solutions for applications ranging from warehouse automation to assistive robotics. His research continues to inspire further exploration into robust feature extraction and sensor fusion techniques.
Research Focus
Key Achievements
Top Papers
- 1RGB-D-Based Features for Recognition of Textureless Objects2 citations · 2017