Matching patterns of line segments by affine-invariant area features

2002 ◽  
Author(s):  
Hau-bang, Bernard Chan
Keyword(s):  
Affine Invariant ◽  
Line Segments

Efficient Cues for Discriminating Skewed Curved-Line Segments

Perception ◽  
1996 ◽  
Vol 25 (1_suppl) ◽  
pp. 49-49
Author(s):  
D H Foster ◽  
H T Kukkonen
Keyword(s):  
Affine Invariant ◽  
Affine Transformations ◽  
Statistical Efficiency ◽  
Line Segments ◽  
Relative Invariant ◽  
Circular Arcs ◽  
Viewpoint Change ◽  
Spatial Attributes ◽  
Efficiency Theory ◽  
Relative Invariants

Visual discrimination of circular arcs differing in curvature reaches hyperacute levels of performance. What spatial attributes of curved contours provide the necessary visual cue? Statistical-efficiency theory has previously been applied to data on the discrimination of symmetric curved-line segments undergoing expansions and contractions perpendicular to their chords. The results have suggested that relative invariants with respect to these transformations are the best cues, since they accounted for the most variance in the data (a relative invariant is an attribute such that the ratio of its values is constant under transformation). Expansions and contractions are examples of affine transformations, which in general provide a good approximation to the effects of viewpoint change. If some attribute of a curved line is a relative invariant with respect to affine transformations, is it then a good cue? An experiment was performed in which observers discriminated curved-line segments that had been affine transformed by progressive amounts of shear along their chords as well as expansions and contractions along and perpendicular to their chords. Shear can be interpreted as a relative affine invariant, and, since shear destroys symmetry by skewing the curve, it should provide a good cue. In fact, although expansions and contractions proved to be good cues, shear did not. Candidate cues that were not relative affine invariants (eg Euclidean curvature, turning angle) were also poor cues. It appears that being a relative affine invariant is a necessary but not sufficient condition for a cue to be efficient in the discrimination of curved-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

scholarly journals Matching Large Baseline Oblique Stereo Images Using an End-to-End Convolutional Neural Network

2021 ◽  
Vol 13 (2) ◽  
pp. 274
Author(s):  
Guobiao Yao ◽  
Alper Yilmaz ◽  
Li Zhang ◽  
Fei Meng ◽  
Haibin Ai ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Stereo Matching ◽  
Least Square ◽  
Affine Invariant ◽  
Stereo Images ◽  
Distance Ratio ◽  
Matching Algorithm ◽  
End To End

The available stereo matching algorithms produce large number of false positive matches or only produce a few true-positives across oblique stereo images with large baseline. This undesired result happens due to the complex perspective deformation and radiometric distortion across the images. To address this problem, we propose a novel affine invariant feature matching algorithm with subpixel accuracy based on an end-to-end convolutional neural network (CNN). In our method, we adopt and modify a Hessian affine network, which we refer to as IHesAffNet, to obtain affine invariant Hessian regions using deep learning framework. To improve the correlation between corresponding features, we introduce an empirical weighted loss function (EWLF) based on the negative samples using K nearest neighbors, and then generate deep learning-based descriptors with high discrimination that is realized with our multiple hard network structure (MTHardNets). Following this step, the conjugate features are produced by using the Euclidean distance ratio as the matching metric, and the accuracy of matches are optimized through the deep learning transform based least square matching (DLT-LSM). Finally, experiments on Large baseline oblique stereo images acquired by ground close-range and unmanned aerial vehicle (UAV) verify the effectiveness of the proposed approach, and comprehensive comparisons demonstrate that our matching algorithm outperforms the state-of-art methods in terms of accuracy, distribution and correct ratio. The main contributions of this article are: (i) our proposed MTHardNets can generate high quality descriptors; and (ii) the IHesAffNet can produce substantial affine invariant corresponding features with reliable transform parameters.

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
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