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     The BAsis Sparse-coding Inspired Similarity (BASIS) descriptor uses theory taken from sparse coding to provide an efficient image feature description method for frame-to-frame feature point matching.  This 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.  An improved version of BASIS descriptor called Tree BAsis Sparse-coding Inspired Similarity feature descriptor (TreeBASIS) was also developed. TreeBASIS utilizes a binary vocabulary tree that is computed off-line using basis dictionary images (BDIs) derived from sparse coding, and the resulting tree is stored in memory for on-line searching. During the on-line algorithm, a small region around a feature point is passed into the BASIS tree, where a Hamming distance is computed between the region and the effectively descriptive BDI (EDBDI) to determine the branch taken. The path the FRI takes is saved as the descriptor, and matching is performed by following the paths of two features. Experimental results show that the TreeBASIS descriptor outperforms SIFT and SURF on frame-to-frame aerial feature point matching.

Graduate Students:

  Spencer Fowers, Alok Desai

  1. S.G. Fowers, ADesai, D.J. Lee, D. Ventura, and D.K. Wilde, “An Efficient Tree-Based Feature Descriptor and Matching Algorithm," AIAA Journal of Aerospace Information Systems, vol. 11/9, p. 596-606, September 2014.
  2. S.G. Fowers, D.J. Lee, D. Ventura, and B.J. Tippetts, “A Novel Feature Descriptor for Low-Resource Embedded Vision Sensors for Micro-UAV Applications," AIAA Journal of Aerospace Information Systems, vol. 10/8, p. 385-395, August 2013.
  3. S.G. Fowers, K.D. Lillywhite, D.J. Lee, and D.K. Wilde, “Color DoG: A Three-Channel Color Feature Detector for Embedded Systems,” SPIE Electronic Imaging, Intelligent Robots and Computer Vision XXVII: Algorithms and Techniques, vol. 7539, 75390X1-9, San Jose, CA, USA, January 17-21, 2010.
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