Palmprint Identification Using Gabor and Wide Principal Line Features

Procedia Computer Science ◽

10.1016/j.procs.2016.07.272 ◽

2016 ◽

Vol 93 ◽

pp. 706-712 ◽

Cited By ~ 4

Author(s):

Hemantha Kumar Kalluri ◽

Munaga V.N.K. Prasad

Keyword(s):

Principal Line ◽

Palmprint Identification ◽

Line Features

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Detection of Absorption‐Line Features in the X‐Ray Spectra of the Galactic Superluminal Source GRO J1655–40

The Astrophysical Journal ◽

10.1086/305784 ◽

1998 ◽

Vol 500 (2) ◽

pp. 1069-1069 ◽

Cited By ~ 1

Author(s):

Y. Ueda ◽

H. Inoue ◽

Y. Tanaka ◽

K. Ebisawa ◽

F. Nagase ◽

...

Keyword(s):

Absorption Line ◽

X Ray ◽

Line Features

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A Multi-Feature Fusion Slam System Attaching Semantic Invariant to Points and Lines

Sensors ◽

10.3390/s21041196 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1196

Author(s):

Gang Li ◽

Yawen Zeng ◽

Huilan Huang ◽

Shaojian Song ◽

Bin Liu ◽

...

Keyword(s):

Feature Matching ◽

Feature Fusion ◽

Error Function ◽

Line Segments ◽

Cumulative Error ◽

Localization And Mapping ◽

Tracking Process ◽

Line Features ◽

Point Line ◽

Segment Data

The traditional simultaneous localization and mapping (SLAM) system uses static points of the environment as features for real-time localization and mapping. When there are few available point features, the system is difficult to implement. A feasible solution is to introduce line features. In complex scenarios containing rich line segments, the description of line segments is not strongly differentiated, which can lead to incorrect association of line segment data, thus introducing errors into the system and aggravating the cumulative error of the system. To address this problem, a point-line stereo visual SLAM system incorporating semantic invariants is proposed in this paper. This system improves the accuracy of line feature matching by fusing line features with image semantic invariant information. When defining the error function, the semantic invariant is fused with the reprojection error function, and the semantic constraint is applied to reduce the cumulative error of the poses in the long-term tracking process. Experiments on the Office sequence of the TartanAir dataset and the KITTI dataset show that this system improves the matching accuracy of line features and suppresses the cumulative error of the SLAM system to some extent, and the mean relative pose error (RPE) is 1.38 and 0.0593 m, respectively.

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