Analysis of feature detector and descriptor combinations with a localization   experiment for various performance metrics

ERTUĞRUL BAYRAKTAR; PINAR BOYRAZ

Article Details

Analysis of feature detector and descriptor combinations with a localization experiment for various performance metrics

Authors:

ERTUĞRUL BAYRAKTAR | PINAR BOYRAZ

Publisher:

TUBITAK

Journal:

Turkish Journal of Electrical Engineering and Computer Science 2017 Vol.25 Issue 3, pp.2444-2454

Document Type:

Article

Publication Date:

5/2017

Document Language:

Turkish

Document ID (Org No.):

2000740928

Frequency:

Bimonthly

eISSN:

1303-6203

pISSN:

1300-0632

Abstract:

 

The purpose of this study is to provide a detailed   performance comparison of feature detector/descriptor methods, particularly   when their various combinations are used for image-matching. The   localization experiments of a mobile robot in an indoor environment are   presented as a case study. In these experiments, 3090 query images and 127   dataset images were used. This study includes five methods for feature   detectors (features from accelerated segment test (FAST), oriented FAST and   rotated binary robust independent elementary features (BRIEF) (ORB),   speeded-up robust features (SURF), scale invariant feature transform (SIFT),   and binary robust invariant scalable keypoints (BRISK)) and five other   methods for feature descriptors (BRIEF, BRISK, SIFT, SURF, and ORB). These   methods were used in 23 different combinations and it was possible to obtain   meaningful and consistent comparison results using the performance criteria   defined in this study. All of these methods were used independently and   separately from each other as either feature detector or descriptor. The   performance analysis shows the discriminative power of various combinations   of detector and descriptor methods. The analysis is completed using five   parameters: (i) accuracy, (ii) time, (iii) angle difference between   keypoints, (iv) number of correct matches, and (v) distance between   correctly matched keypoints. In a range of 60∘60∘{60^{\circ}}, covering five rotational pose points for our system, the FAST-SURF   combination had the lowest distance and angle difference values and the   highest number of matched keypoints. SIFT-SURF was the most accurate   combination with a 98.41% correct classification rate. The fastest   algorithm was ORB-BRIEF, with a total running time of 21,303.30 s to match   560 images captured during motion with 127 dataset images.

Article Details

Analysis of feature detector and descriptor combinations with a localization experiment for various performance metrics

Authors: ERTUĞRUL BAYRAKTAR | PINAR BOYRAZ

Publisher: TUBITAK

Journal: Turkish Journal of Electrical Engineering and Computer Science 2017 Vol.25 Issue 3, pp.2444-2454

Document Type: Article

Publication Date: 5/2017

Document Language: Turkish

Document ID (Org No.): 2000740928

Frequency: Bimonthly

eISSN: 1303-6203

pISSN: 1300-0632

Issues

Authors:

ERTUĞRUL BAYRAKTAR | PINAR BOYRAZ

Publisher:

TUBITAK

Journal:

Turkish Journal of Electrical Engineering and Computer Science 2017 Vol.25 Issue 3, pp.2444-2454

Document Type:

Article

Publication Date:

5/2017

Document Language:

Turkish

Document ID (Org No.):

2000740928

Frequency:

Bimonthly

eISSN:

1303-6203

pISSN:

1300-0632