Rotation-invariant fast features for large-scale recognition and real-time tracking

被引：9
|
作者：
Takacs, Gabriel [1 ]
Chandrasekhar, Vijay [1 ]
Tsai, Sam [1 ]
Chen, David [1 ]
Grzeszczuk, Radek
Girod, Bernd [1 ]
机构：
[1] Stanford Univ, Stanford, CA 94305 USA
来源：
SIGNAL PROCESSING-IMAGE COMMUNICATION | 2013年 / 28卷 / 04期
关键词：
Feature descriptors; Keypoints; Tracking; Visual search;
D O I：
10.1016/j.image.2012.11.004
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
We present an end-to-end feature description pipeline which uses a novel interest point detector and rotation-invariant fast feature (RIFF) descriptors. The proposed RIFF algorithm is 15 x faster than SURF [1] while producing large-scale retrieval results that are comparable to SIFT [2]. Such high-speed features benefit a range of applications from mobile augmented reality (MAR) to web-scale image retrieval and analysis. In particular, RIFF enables unified tracking and recognition for MAR. (c) 2013 Published by Elsevier B.V.
引用
收藏
页码：334 / 344
页数：11
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