Reporting and Counting Intersections Between Two Sets of Line Segments

1988 ◽  
pp. 307-325 ◽  
Author(s):  
Harry G. Mairson ◽  
Jorge Stolfi
Keyword(s):  
Line Segments

Discrimination of Occluded Line Segments by Pigeons

2009 ◽  
Author(s):  
Robert G. Cook ◽  
Carl Erick Hagmann
Keyword(s):  
Line Segments

Less is more: Simple stimuli provoke more variable search strategies.

2020 ◽  
Author(s):  
Anna Nowakowska ◽  
Alasdair D F Clarke ◽  
Jessica Christie ◽  
Josephine Reuther ◽  
Amelia R. Hunt
Keyword(s):  
Line Segment ◽  
Search Strategies ◽  
Complex Objects ◽  
Search Behaviour ◽  
Line Segments ◽  
Surface Level ◽  
Efficient Search ◽  
Less Is More ◽  
Dramatic Shift

We measured the efficiency of 30 participants as they searched through simple line segment stimuli and through a set of complex icons. We observed a dramatic shift from highly variable, and mostly inefficient, strategies with the line segments, to uniformly efficient search behaviour with the icons. These results demonstrate that changing what may initially appear to be irrelevant, surface-level details of the task can lead to large changes in measured behaviour, and that visual primitives are not always representative of more complex objects.

Real-time line segments detection based on graphic processor

2009 ◽  
Vol 29 (5) ◽  
pp. 1359-1361
Author(s):  
Tong ZHANG ◽  
Zhao LIU ◽  
Ning OUYANG
Keyword(s):  
Real Time ◽  
Time Line ◽  
Line Segments ◽  
Graphic Processor

Nonlinear biobjective optimization: improving the upper envelope using feasible line segments

2021 ◽  
Vol 79 (2) ◽  
pp. 503-520
Author(s):  
Ignacio Araya ◽  
Damir Aliquintui ◽  
Franco Ardiles ◽  
Braulio Lobo
Keyword(s):  
Line Segments ◽  
Biobjective Optimization

scholarly journals Multiscale Detection of Circles, Ellipses and Line Segments, Robust to Noise and Blur

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 25554-25578
Author(s):  
Onofre Martorell ◽  
Antoni Buades ◽  
Jose Luis Lisani
Keyword(s):  
Line Segments ◽  
Robust To Noise

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

Sensors ◽  
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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