A comparison of prefilters in ORB-based object detection

被引：11

作者：

Sharif, Helia ^{[1
]}

Hoelzel, Matthew ^{[2
]}

机构：

[1] German Aerosp Ctr, Inst Space Syst DLR, Guidance Nav & Control Dept, Robert Hooke Str 7, D-28359 Bremen, Germany

[2] Univ Bremen, Parallel Comp Embedded Sensor Syst Grp, Bibliotekstr 1, D-28359 Bremen, Germany

来源：

PATTERN RECOGNITION LETTERS | 2017年 / 93卷

关键词：

Keypoint recognition; Feature matching; Edge detection; Curve filter; Smoothing filter;

D O I：

10.1016/j.patrec.2016.11.007

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we study the effects of prefiltering on Oriented Fast and Rotated BRIEF (ORB) -based object detection. Specifically, we examine the trade-off between execution runtime and the minimum Hamming distance between matched feature descriptors, since ORB uses the minimum distance to determine whether the object is present. Furthermore, we introduce a covariance-based method of choosing the Hamming distance thresholds for each of the prefiltered ORB detectors which compares the minimum Hamming distance values for both positive and negative training images. We also use the same method to assess the prefilter performance. (C) 2016 Elsevier B.V. All rights reserved.

引用

页码：154 / 161

页数：8