Successful landing of an autonomous unmanned aerial vehicle requires a high degree of accuracy and efficient, real-time processing. This research applies systems engineering concepts to investigate the feasibility of applying computer vision techniques and visual feedback in the control loop for an autonomous system. This thesis examines the framework and performance of an algorithm designed to detect and track a runway in images captured from a camera onboard an aircraft during the final approach and landing stages of flight. Using a series of image processing techniques to localize the runway and the Hough transformation for line detection, the algorithm is capable of detecting the edges of a runway with over 96 percent accuracy through 3000 test images. The operating conditions for this algorithm include any scenario in which visual flight rules apply. Additionally, the system will perform with runways that comply with Federal Aviation Administration regulations. Future applications of this algorithm should include aircraft attitude and pose estimation as well as full integration into an autonomous aircraft control system.