Rizwana Arshad

University of Engineering and Technology Peshawar

Papers

Total Citations

H-Index

About

Rizwana Arshad is a computer scientist whose research focuses on efficient image processing and computer vision algorithms, particularly for resource-constrained systems. Her most cited work, "A Swift and Memory Efficient Hough Transform for Systems with Limited Fast Memory" (2009, 9 citations), addresses a critical challenge in embedded and real-time vision applications: performing robust line detection with minimal memory and computational overhead. This contribution is especially valuable for systems where fast memory is scarce, such as mobile devices, robotics, and edge computing platforms. By optimizing the classic Hough Transform—a foundational technique in pattern recognition—Arshad’s work enables faster, more practical implementations without sacrificing accuracy. Her research bridges the gap between theoretical algorithm design and real-world hardware limitations, making advanced computer vision more accessible in low-power environments. While her citation count reflects a focused, specialized impact, the practical significance of her work resonates with engineers and researchers developing efficient vision systems. Arshad’s contributions exemplify how targeted algorithmic improvements can drive progress in embedded and mobile computer vision, a field increasingly vital for autonomous systems and smart devices.