We present a novel descriptor algorithm (DUDE) using line/point duality and a randomization strategy that provides simple but robust, consistent feature extraction and correspondence. Using duality enables us to effectively capture a distribution of line segments, and the proposed randomization strategy improves repeatability over existing techniques by generating more line features in common between two images. We demonstrate the effectiveness of our approach using a challenging set of disparate image pairs, and show that the DUDE descriptor performs comparably to state-of-the-art methods with significantly less computation expense.
['Youngwook Paul Kwon', 'Hyojin Kim', 'Goran Konjevod', 'Sara McMains']
Dude (Duality descriptor): A robust descriptor for disparate images using line segment duality