M. Kokhazadeh
Papers
1
Total Citations
2
H-Index
1
About
M. Kokhazadeh is a researcher specializing in high-performance computing and computer vision, with a particular focus on accelerating stereo vision algorithms for real-time applications. Their most cited work, "Accelerating stereo vision algorithm using SSE3, AVX2, and CUDA" (2017, 2 citations), addresses the computational bottlenecks inherent in stereo matching—a critical process for depth detection in robotics, autonomous vehicles, and aerial surveys. By leveraging modern parallel computing architectures, Kokhazadeh demonstrates how to significantly reduce execution times, enabling practical deployment in latency-sensitive environments. This contribution bridges the gap between algorithmic complexity and real-world performance, making stereo vision more accessible for embedded and real-time systems. While their citation count is modest, the work highlights a deep understanding of both hardware-level optimization and vision algorithms, positioning Kokhazadeh as a practical innovator in the field. Their research underscores the importance of cross-disciplinary approaches, combining computer vision with parallel computing to solve pressing engineering challenges.
Research Focus
Key Achievements
Top Papers
- 1Accelerating stereo vision algorithm using SSE3, AVX2, and CUDA2 citations · 2017