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• W2015337617 abstract "Feature point matching is an important step for many vision-based unmanned-aerial-vehicle applications. This paper presents the development of a new feature descriptor for feature point matching that is well suited for micro unmanned aerial vehicles equipped with a low-resource, compact, lightweight, low-power embedded vision sensor. The Basis Sparse-Coding Inspired Similarity 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 descriptions. 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 unmanned-aerial-vehicle applic..." @default.
• W2015337617 created "2016-06-24" @default.
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• W2015337617 date "2013-08-01" @default.
• W2015337617 modified "2023-09-27" @default.
• W2015337617 title "Novel Feature Descriptor for Low-Resource Embedded Vision Sensors for Micro Unmanned-Aerial-Vehicle Applications" @default.
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• W2015337617 doi "https://doi.org/10.2514/1.i010028" @default.
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