Kaushik Boga
Papers
1
Total Citations
5
H-Index
1
About
Kaushik Boga is a researcher whose work lies at the intersection of computational neuroscience and computer vision, with a particular focus on depth perception and stochastic neural models. His most cited paper, "A Generalized Stochastic Implementation of the Disparity Energy Model for Depth Perception" (2016), has garnered 5 citations, reflecting its contribution to advancing biologically inspired algorithms for 3D vision. In this work, Boga proposed a novel framework that generalizes the classic disparity energy model by incorporating stochastic processes, enabling more robust and efficient depth estimation from binocular cues—a critical capability for applications in robotics, autonomous navigation, and augmented reality. His approach bridges the gap between theoretical neuroscience and practical engineering, offering a scalable solution for real-time depth perception in noisy environments. While his citation count is modest, the conceptual innovation of his stochastic implementation has provided a foundation for subsequent research in neuromorphic vision systems. Boga’s work exemplifies how insights from neural computation can inspire next-generation computer vision technologies, making him a promising voice in the ongoing quest to replicate human-like depth perception in machines.
Research Focus
Key Achievements
Top Papers
- 1