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BASIS视觉特征描述子及其硬件实现
756 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 23, NO. 5, MAY 2013
The Nature-Inspired BASIS Feature Descriptor for
UAV Imagery and Its Hardware Implementation
Spencer G. Fowers, Dah-Jye Lee, Senior Member, IEEE, Dan A. Ventura, and
James K. Archibald, Senior Member, IEEE
Abstract—This paper presents a feature descriptor well suited
for limited-resource applications such as an unmanned aerial
vehicle 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 com-
putation required for computing BASIS descriptors, reduced de-
scriptor size, and the ability to create the descriptors without the
use of a 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.
Index Terms—Computer vision, feature description, feature
descriptor, feature detection, feature detector, sparse coding.
I. Introduction
COMPUTER vision applications for low-power, limited-resource, and embedded systems are becoming increas-
ingly prevalent. We define limited-resource systems as systems
that have restricted or reduced processing capabilities due
to weight, size, or power constraints
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