Date of Award
Master of Science
Brian Armstrong, Jun Zhang, Zeyun Yu, Peter Schmidt
Ellipse Detection, Hough Transform, Machine Learning, Support Vector Machine
Elliptical shape detection is widely used in practical applications. Nearly all classical ellipse detection algorithms require some form of threshold, which can be a major cause of detection failure, especially in the challenging case of Moire Phase Tracking (MPT) target images. To meet the challenge, a threshold free detection algorithm for elliptical landmarks is proposed in this thesis. The proposed Aligned Gradient and Unaligned Gradient (AGUG) algorithm is a Support Vector Machine (SVM)-based classification algorithm, original features are extracted from the gradient information corresponding to the sampled pixels. with proper selection of features, the proposed algorithm has a high accuracy and a stronger robustness in blurring and contrast variation. The thesis confirms that the removal of thresholds in ellipse detection algorithm improves robustness.
Zhang, Lifan, "Threshold Free Detection of Elliptical Landmarks Using Machine Learning" (2017). Theses and Dissertations. 1729.