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A local feature descriptor based on Local Binary Patterns

Authors: Gaoqing Cheng; Jiaxing Chen;

A local feature descriptor based on Local Binary Patterns

Abstract

This paper presents a local feature descriptor based on Local Binary Patterns (LBP). This descriptor uses binary bit string to represent the local region of images and the integral image to mean filter that makes descriptor building faster. Compared to Scale-invariant feature transform (SIFT)that the calculation is large and the process is time consuming in the description of the characteristics, the descriptor based on LBP speeds about 1 orders of magnitude. In the matching effect for blurred image, illumination changes, and different JPEG compression ratio, the matching results are better than descriptors based on SIFT.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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Average
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