Feature detection and matching is an important step in many object tracking and detection algorithms. This paper discusses methods to improve upon previous work on the SYnthetic BAsis feature descriptor (SYBA) algorithm, which describes and compares image features in an efficient and discreet manner. SYBA utilizes synthetic basis images overlaid on a feature region of interest (FRI) to generate binary numbers that uniquely describe the feature contained within the FRI. These binary numbers are then used to compare against feature values in subsequent images for matching. However, in a non-ideal environment the accuracy of the feature matching suffers due to variations in image scale, and rotation. This paper introduces a new version of SYBA which processes FRI’s such that the descriptions developed by SYBA are rotation and scale invariant. To demonstrate the improvements of this robust implementation of SYBA called rSYBA, included in this paper are applications that have to cope with high amounts of image variation. The first detects objects along an oil pipeline by transforming and comparing frame-by-frame two surveillance videos recorded at two different times. The second shows camera pose plotting for a ground based vehicle using monocular visual odometry. The third generates panoramic images through image stitching and image transforms. All applications contain large amounts of image variation between image frames and therefore require a significant amount of correct feature matches to generate acceptable results.



College and Department

Ira A. Fulton College of Engineering and Technology; Electrical and Computer Engineering



Date Submitted


Document Type





feature detection, feature description, feature matching, object tracking, visual odometry, unmanned aerial vehicle, ground vehicle, pipeline, computer vision



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Engineering Commons