scholarly journals Statistical Analysis of a Stereo Matching Algorithm

1992 ◽  
Author(s):  
N. A. Thacker ◽  
P. Courtney
Keyword(s):  
Statistical Analysis ◽  
Stereo Matching ◽  
Matching Algorithm

scholarly journals Statistical Analysis of a Stereo Matching Algorithm

BMVC92 ◽  
1992 ◽  
pp. 316-326 ◽  
Author(s):  
N. A. Thacker ◽  
P. Courtney
Keyword(s):  
Statistical Analysis ◽  
Stereo Matching ◽  
Matching Algorithm

Towards an Embedded Stereo Matching Algorithm Based on Multiple Correlation Windows

2019 ◽  
Author(s):  
M. A. Palacios-Ramos ◽  
H. D. Vázquez-Delgado ◽  
Abiel Aguilar-González ◽  
M. Pérez-Patricio
Keyword(s):  
Stereo Matching ◽  
Multiple Correlation ◽  
Matching Algorithm

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.

A modified stereo matching algorithm suitable for implementation on a convolution specialized hardware

1992 ◽  
Vol 13 (7) ◽  
pp. 523-528 ◽  
Author(s):  
E. Stella ◽  
A. Distante ◽  
G. Attolico ◽  
T. D'Orazio
Keyword(s):  
Stereo Matching ◽  
Matching Algorithm ◽  
Specialized Hardware

Stereo matching algorithm with guided filter and modified dynamic programming

2015 ◽  
Vol 76 (1) ◽  
pp. 199-216 ◽  
Author(s):  
Shiping Zhu ◽  
Ruidong Gao ◽  
Zheng Li
Keyword(s):  
Dynamic Programming ◽  
Stereo Matching ◽  
Guided Filter ◽  
Matching Algorithm

A multi-BRIEF-descriptor stereo matching algorithm for binocular visual sensing of fillet welds with indistinct features

2021 ◽  
Vol 66 ◽  
pp. 636-650
Author(s):  
Tao Wang ◽  
Zhijiang Wang ◽  
Yue Cao ◽  
Yuchen Wang ◽  
Shengsun Hu
Keyword(s):  
Stereo Matching ◽  
Fillet Welds ◽  
Matching Algorithm ◽  
Visual Sensing
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