Minimum correspondence sets for improving large-scale augmented paper

  • Authors:

  • Xin Yang;Chunyuan Liao;Qiong Liu;Kwang-Ting Cheng

  • Affiliations:

  • University of California, Santa Barbara;FX Palo Alto Laboratory;FX Palo Alto Laboratory;University of California, Santa Barbara

  • Venue:
  • Proceedings of the 10th International Conference on Virtual Reality Continuum and Its Applications in Industry
  • Year:
  • 2011

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Abstract

Augmented Paper (AP) is an important area of Augmented Reality (AR). Many AP systems rely on visual features for paper document identification. Although promising, these systems can hardly support large sets of documents (i.e. one million documents) because of high memory and time cost in handling high-dimensional features. On the other hand, general large-scale image identification techniques are not well customized to AP, costing unnecessarily more resources to achieve the identification accuracy required by AP. To address this mismatching between AP and image identification techniques, we propose a novel large-scale image identification technique well geared to AP. At its core is a geometric verification scheme based on Minimum visual-word Correspondence Set (MICSs). MICS is a set of visual word (i.e. quantized visual feature) correspondences, each of which contains a minimum number of correspondences that are sufficient for deriving a transformation hypothesis between a captured document image and an indexed image. Our method selects appropriate MICSs to vote in a Hough space of transformation parameters, and uses a robust dense region detection algorithm to locate the possible transformation models in the space. The models are then utilized to verify all the visual word correspondences to precisely identify the matching indexed image. By taking advantage of unique geometric constraints in AP, our method can significantly reduce the time and memory cost while achieving high accuracy. As showed in evaluation with two AP systems called FACT and EMM, over a dataset with 1M+ images, our method achieves 100% identification accuracy and 0.67% registration error for FACT; For EMM, our method outperforms the state-of-the-art image identification approach by achieving 4% improvements in detection rate and almost perfect precision, while saving 40% and 70% memory and time cost.