K. Krishna Reddy
Papers
1
Total Citations
9
H-Index
1
About
K. Krishna Reddy is a computer vision researcher whose work centers on scene understanding, particularly the detection of occlusion edges—critical boundaries where objects in a scene create depth discontinuities. His most-cited paper, "Occlusion Edge Detection in RGB-D Frames using Deep Convolutional Networks" (2014), introduced a pioneering deep learning approach to extracting these edges from RGB-D data, a task essential for mobile robotics and visual perception. By leveraging convolutional networks, Reddy demonstrated how to infer depth discontinuities directly from images and video, bypassing the need for explicit range sensors in many applications. Though his citation count (9) reflects a focused, early-stage impact, this work laid groundwork for later advances in semantic segmentation and 3D reconstruction. Reddy’s contributions are notable for bridging classical geometric vision with modern deep learning, offering practical solutions for autonomous systems. His research continues to influence how machines perceive spatial relationships in dynamic environments.
Research Focus
Key Achievements
Top Papers
- 1