This work develops a feature descriptor well-suited for limited resource applications such as UAV embedded systems, small microprocessors, and small, low power field programmable gate array (FPGA) fabric.  The BAsis Sparse-coding Inspired Similarity (BASIS) descriptor utilizes sparse coding to create dictionary images that model the regions in the human visual cortex.  Due to the reduced amount of computation required for computing BASIS descriptors, reduced descriptor size, and the ability to create the descriptors without the use of floating point, this approach is an excellent candidate for FPGA hardware implementation.  The bit-level-accurate BASIS descriptor was tested on a dataset of real aerial images with the task of calculating a frame-to-frame homography and compared to software versions of Scale Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF).  Experimental results show that the BASIS descriptor outperforms SIFT and performs comparably to SURF on frame-to-frame aerial feature point matching.  BASIS descriptors require less memory storage than other descriptors, and can be computed entirely in hardware, allowing the descriptor to operate at real-time frame rates on a low power, embedded platform such as an FPGA.

 Graduate Students:

Spencer Fowers, Alok Desai 

1. S.G. Fowers, A. Desai, D.J. Lee, D. Ventura, and J.K Archibald, “Tree-Based Feature Descriptor and Its Hardware Implementation,” International Journal of Reconfigurable Computing, vol. 2014, Article ID 606210, 12 pages, November 2014.
2. S.G. Fowers, D.J. Lee, D.A. Ventura, and J.K Archibald, “Nature Inspired BASIS Feature Descriptor and Its Hardware Implementation,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 23/5, p. 756-768, May 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.