Stereo Matching Algorithm Using a Weighted Average of Costs Aggregated by Various Window Sizes

2006 ◽  
pp. 771-780 ◽  
Author(s):  
Kan’ya Sasaki ◽  
Seiji Kameda ◽  
Atsushi Iwata
Keyword(s):  
Stereo Matching ◽  
Weighted Average ◽  
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.

Efficient local stereo matching algorithm based on fast gradient domain guided image filtering

2021 ◽  
Vol 95 ◽  
pp. 116280
Author(s):  
Weimin Yuan ◽  
Cai Meng ◽  
Xiaoyan Tong ◽  
Zhaoxi Li
Keyword(s):  
Stereo Matching ◽  
Image Filtering ◽  
Matching Algorithm ◽  
Gradient Domain ◽  
Fast Gradient ◽  
Guided Image Filtering

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

Research on stereo Matching algorithm based on neural network

2021 ◽  
Author(s):  
Menglei Kong ◽  
Kai Gao
Keyword(s):  
Neural Network ◽  
Stereo Matching ◽  
Matching Algorithm

A census-based stereo matching algorithm with multiple sparse windows

2015 ◽  
Author(s):  
Kyeong-ryeol Bae ◽  
Hyeon-Sik Son ◽  
Jongkil Hyun ◽  
Byungin Moon
Keyword(s):  
Stereo Matching ◽  
Matching Algorithm

Research and Realization of a New Algorithm for Stereo Matching Based on Binocular Vision

2013 ◽  
Vol 670 ◽  
pp. 202-207 ◽  
Author(s):  
Jun Ting Cheng ◽  
C. Zhao ◽  
W.L. Zhao ◽  
W.H. Wu
Keyword(s):  
Binocular Vision ◽  
Stereo Matching ◽  
Three Dimensional ◽  
Cnc Machining ◽  
Matching Algorithm ◽  
Evaluation Test ◽  
Three Dimensional Measurement ◽  
Binocular Stereo ◽  
Measuring Machine ◽  
3D Solid

In the development of a three-dimensional measurement system, binocular stereo matching is the most important and difficult. In the basis of introducing selective principles of matching algorithm, a new stereo matching algorithm for binocular vision is put forward that is named noncoded difference measuring distance. The algorithm effectively grapples with the problem of searching for the coincidence relation of raster and can efficiently and accurately obtain three-dimensional world coordinates of the entities. Experiment results show that this 3D measuring machine can effectively measure the 3D solid profile of free surface. During the evaluation test for accuracy, scan a standard plane. Fit all 3D points in one plane, and then the flatness value of this plane is obtained. The flatness value of the standard plane has been ultimately measured as: ± 0.0462mm, this measuring accuracy can completely satisfy the requirements of rapid prototyping or CNC machining, it as well as achieves the stated accuracy (± 0.05mm).

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