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     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. Technologies such as 2D barcode scanning or radio frequency identification (RFID) tags have recently been proposed to improve this process.  They both incur significant upfront costs and require a large investment of time to fit books with special tags before the system can be productive. 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. This paper presents the development of a simple color enhancement feature descriptor called Color Difference of Gaussians Scale Invariant Feature Transform (CDSIFT).  CDSIFT is well suited for library inventory process automation and this paper introduces such a system for this unique application.

Collaborators Dr. Guangming Xiong, Beijing Institute of Technology, Beijing, China

Students:

Spencer Fowers, Clint Pitzak

Publications:
  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.
  3. D.J. Lee, Y.C. Chang, J.K. Archibald, and C.J. Pitzak, ”Matching Book-Spine Images for Library Shelf-Reading Process Automation,” IEEE Conference on Automation Science and Engineering (CASE), p. 738-743, Washington DC, USA, August 23-26, 2008.
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