Fast Computation of Rotation-Invariant Image Features by an Approximate Radial Gradient Transform

被引：42
|
作者：
Takacs, Gabriel [1 ]
Chandrasekhar, Vijay [2 ]
Tsai, Sam S. [2 ]
Chen, David [2 ]
Grzeszczuk, Radek [1 ]
Girod, Bernd [2 ]
机构：
[1] Microsoft Corp, Sunnyvale, CA 94089 USA
[2] Stanford Univ, Stanford, CA 94305 USA
来源：
IEEE TRANSACTIONS ON IMAGE PROCESSING | 2013年 / 22卷 / 08期
基金：
美国国家科学基金会;
关键词：
Computer vision; signal processing; real-time systems; feature representation; invariants;
D O I：
10.1109/TIP.2012.2230011
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We present the radial gradient transform (RGT) and a fast approximation, the approximate RGT (ARGT). We analyze the effects of the approximation on gradient quantization and histogramming. The ARGT is incorporated into the rotation-invariant fast feature (RIFF) algorithm. We demonstrate that, using the ARGT, RIFF extracts features 16x faster than SURF while achieving a similar performance for image matching and retrieval.
引用
收藏
页码：2970 / 2982
页数：13
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