SCoBeP: Dense image registration using sparse coding and belief propagation

  • Authors:

  • Nafise Barzigar;Aminmohammad Roozgard;Samuel Cheng;Pramode Verma

  • Affiliations:

  • Department of Electrical and Computer Engineering, Oklahoma University, Tulsa, OK 74135, United States;Department of Electrical and Computer Engineering, Oklahoma University, Tulsa, OK 74135, United States;Department of Electrical and Computer Engineering, Oklahoma University, Tulsa, OK 74135, United States;Department of Electrical and Computer Engineering, Oklahoma University, Tulsa, OK 74135, United States

  • Venue:
  • Journal of Visual Communication and Image Representation
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

Image registration as a basic task in image processing has been studied widely in the literature. It is an important preprocessing step in various applications such as medical imaging, super resolution, and remote sensing. In this paper, we proposed a novel dense registration method based on sparse coding and belief propagation. We used image blocks as features, and then we employed sparse coding to find a set of candidate points. To select optimum matches, belief propagation was subsequently applied on these candidate points. Experimental results show that the proposed approach is able to robustly register scenes and is competitive as compared to high accuracy optical flow Brox et al. (2004) [1], and SIFT flow Liu et al. [2].