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       We used our feature descriptors for two applications.  The first one is for pose estimation for UAVs and the other one is for book spine feature matching for library shelf reading application.
      Feature point matching is an important step for many vision-based UAV applications. We used our feature descriptors for feature point matching that is well suited for micro-UAVs equipped with a low-resource, compact, lightweight, and low-power embedded vision sensor.  The BAsis Sparse-coding Inspired Similarity (BASIS) descriptor requires simple mathematical operations, uses comparatively small memory storage, and can support color and grayscale feature description.  It is an excellent candidate for implementation on low-resource systems that require real-time performance, where complex mathematical operations are prohibitively expensive. To demonstrate its performance, the feature matching result was used to calculate a frame-to-frame homography that is essential to UAV applications such as pose estimation and obstacle detection for navigation.
     The library book inventory or so called shelf-reading process is an important task for libraries to identify missing books and to locate misplaced books.  Currently, this process requires humans to remove each book from the shelf, open the front cover, scan its barcode, and reshelve the book. It is a labor intensive and often error-prone process. A vision-based automation system is proposed to improve this process without those prohibitively high upfront costs.  This low-cost shelf-reading system includes a hand-held imaging device such as a smartphone with wireless communication capability and a server that processes the captured book spine images for book identification.  Existing color feature descriptors for feature matching typically use gray scale feature detectors, which omit important color edges.  Also, photometric invariant color feature descriptors require complex computations that are not necessary to provide color descriptor information. 

 Graduate Students:

  Spencer Fowers

  1. S.G. Fowers and D.J. Lee, “An Effective Color Addition to Feature Detection and Description for Book Spine Image Matching,” International Scholarly Research Network–Machine Vision, vol. 2012, Article ID 945973, 15 pages, January 2012.
  2. S.G. Fowers, D.J. Lee, and G.M. Xiong, “Improve Library Shelf Reading Using Color Feature Matching of Book-Spine Image,” IEEE Proceedings of the International Conference on Control, Automation, Robotics and Vision (ICARCV) , p. 2160-2165, Singapore, December 7-10, 2010.
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