Efficient Texture-less Object Detection for Augmented Reality Guidance
Real-time scalable detection of texture-less objects in 2D images is a highly relevant task for augmented reality applications such as assembly guidance. The paper presents a purely edge-based method based on the approach of Damen et al. (2012) [5]. The proposed method exploits the recent structured edge detector by Dollár and Zitnick (2013) [8], which uses supervised examples for improved object outline detection. It was experimentally shown to yield consistently better results than the standard Canny edge detector. The work has identified two other areas of improvement over the original method; proposing a Hough-based tracing, bringing a speed-up of more than 5 times, and a search for edgelets in stripes instead of wedges, achieving improved performance especially at lower rates of false positives per image. Experimental evaluation proves the proposed method to be faster and more robust. The method is also demonstrated to be suitable to support an augmented reality application for assembly guidance.