Z. Kokhazad

Academic Center for Education, Culture and Research

Papers

Total Citations

H-Index

About

Z. Kokhazad is a researcher focused on high-performance computing and computer vision, with a particular emphasis on accelerating computationally intensive algorithms for real-time applications. Their most-cited work, "Accelerating stereo vision algorithm using SSE3, AVX2, and CUDA" (2017), addresses a critical bottleneck in stereo vision—the heavy computational demands of stereo matching, which is essential for robotics, autonomous vehicles, and aerial surveys. By leveraging modern parallel processing technologies, including SSE3, AVX2, and CUDA, Kokhazad demonstrates how to significantly reduce execution time for depth detection algorithms, enabling practical deployment in real-time systems. Though their citation count is modest at 2, this work highlights a pragmatic approach to bridging algorithmic complexity with hardware capabilities. Kokhazad’s contributions underscore the importance of optimization in making advanced vision systems viable for time-sensitive applications, offering valuable insights for students and researchers working at the intersection of computer vision and parallel computing. Their research serves as a stepping stone for further innovations in efficient, real-time depth perception.