R. Rasoolinezhad
Papers
1
Total Citations
45
H-Index
1
About
R. Rasoolinezhad’s research centers on intelligent navigation and computer vision, with a particular focus on autonomous vehicle and robot guidance. Their most influential work, "Vanishing point detection in corridors: using Hough transform and K-means clustering" (2012, 45 citations), addresses a fundamental challenge in steering systems: accurately determining heading direction. By combining Hough transform for line detection with K-means clustering for robust vanishing point estimation, Rasoolinezhad introduced a computationally efficient and reliable method for corridor navigation. This approach has proven valuable for mobile robots and driver-assistance systems operating in structured indoor environments. The paper’s continued citation reflects its practical utility and the enduring relevance of its core problem. Rasoolinezhad’s contributions lie at the intersection of classical image processing and machine learning, offering a streamlined solution that avoids the complexity of deep learning while maintaining strong performance. Their work provides a foundation for researchers developing low-cost, real-time navigation systems, and demonstrates how thoughtful algorithm design can overcome persistent challenges in autonomous mobility.
Research Focus
Key Achievements
Top Papers
- 1