Applying Saliency-Based Region of Interest Detection in Developing a Collaborative Active Learning System with Augmented Reality

摘要

Learning activities are not necessary to be only in traditional physical classrooms but can also be set up in virtual environment. Therefore the authors propose a novel augmented reality system to organize a class supporting real-time collaboration and active interaction between educators and learners. A pre-processing phase is integrated into a visual search engine, the heart of our system, to recognize printed materials with low computational cost and high accuracy. The authors also propose a simple yet efficient visual saliency estimation technique based on regional contrast is developed to quickly filter out low informative regions in printed materials. This technique not only reduces unnecessary computational cost of keypoint descriptors but also increases robustness and accuracy of visual object recognition. Our experimental results show that the whole visual object recognition process can be speed up 19 times and the accuracy can increase up to 22%. Furthermore, this pre-processing stage is independent of the choice of features and matching model in a general process. Therefore it can be used to boost the performance of existing systems into real-time manner.