Multi-Scale Edge Extraction Based Stereo Matching Algorithm

2010 ◽  
Vol 44-47 ◽  
pp. 4162-4166
Author(s):  
Jing Lian ◽  
Lin Hui Li ◽  
Xiao Yong Shen ◽  
Xian Peng Hao
Keyword(s):  
Stereo Matching ◽  
Robot Vision ◽  
Outdoor Environment ◽  
Linear Filter ◽  
Edge Extraction ◽  
Matching Algorithm ◽  
Multi Scale ◽  
Feature Based ◽  
3D Information ◽  
Edge Feature

In the field of robot vision, edge feature based stereo matching algorithm can reconstruct the targets with clear contours, which needs accurate and continuous target edges been extracted. In the paper, the smoothing filter operator was designed based on the discrete criteria of edge extraction and its correspondence optimal linear filter. Edge extraction was carried out incorporated the nonmaximum suppression and two thresholds techniques. The discrete criteria based multi-scale edge extraction method was studied with full use of the multi-scale character of the edge information. The detected multi-scale edges were synthesized to obtain the accurate and continuous single pixel wide edge. Then an edge feature based stereo matching algorithm was proposed to obtain 3D information of target. The experimental results demonstrate that the method can effectively suppress disturbance in outdoor environment and reconstruct target contour clearly.

Stereo Matching Algorithm Based on Edge Feature of Segmented Image

2013 ◽  
Vol 333-335 ◽  
pp. 948-953
Author(s):  
Yi Zhang ◽  
Cheng Liang Huang ◽  
Lian Fa Bai
Keyword(s):  
Stereo Matching ◽  
Mean Shift ◽  
Weighting Factor ◽  
Reference Image ◽  
Matching Accuracy ◽  
Matching Algorithm ◽  
Mean Shift Algorithm ◽  
Fast Speed ◽  
Segmented Image ◽  
Edge Feature

Stereo matching of the disparity discontinuous boundaries and weak texture regions is still a problem of computer vision. Local-based stereo matching algorithm with the advantages of fast speed and high accuracy is the most common method. In order to improve the matching accuracy of the mentioned regions,a stereo matching algorithm based on edge feature of segmented image is proposed. Firstly, the reference image was segmented by Mean-Shift algorithm. Then, support window was dynamically allocated based on edge feature of segmented image. Finally, the disparity distribution of support window was adjusted by introducing weighting factor. The experimental results show that this algorithm can reduce noise and effectively improve the matching accuracy.

A Multi-Gradation Stereo Matching Method Based on Edge Feature Points

2013 ◽  
Vol 748 ◽  
pp. 624-628
Author(s):  
Zhu Lin Li
Keyword(s):  
Object Recognition ◽  
Stereo Matching ◽  
Three Dimensional ◽  
Feature Points ◽  
Image Pair ◽  
Three Dimensional Reconstruction ◽  
Matching Method ◽  
Matching Algorithm ◽  
Dimensional Reconstruction ◽  
Edge Feature

A gradation stereo matching algorithm based on edge feature points was proposed. Its basic idea is: firstly edge feature points of image pair were extracted; then, gradient invariability and singular eigenvalue invariability were analyzed, two-grade stereo matching method was build, foundation matrix was solved further, and three-grade stereo matching algorithm was finished by foundation matrix guidance. The result indicates that the algorithm can improve matching precision, from 58.3% to 73.2%, it is simple and utility, and it is important for object recognition, tracking, and three-dimensional reconstruction.

An Affine SIFT Matching Algorithm Based on Local Patch Shape Estimation

2014 ◽  
Vol 519-520 ◽  
pp. 553-556
Author(s):  
Zun Shang Zhu ◽  
Yue Qiang Zhang ◽  
Xiang Zhou ◽  
Yang Shang
Keyword(s):  
Stereo Matching ◽  
Affine Invariant ◽  
Shape Estimation ◽  
Moment Matrix ◽  
Matching Algorithm ◽  
Local Patch ◽  
Feature Based ◽  
Initial Transformation ◽  
Fine Registration ◽  
Better Than

In this paper we present an affine SIFT matching method to achieve reliable correspondence points in stereo matching with large viewpoint changes. We extended the affine invariant of the conventional SIFT approach by estimating the shape of the local patch around the interest point. Since we can obtain the scale information by SIFT detector, a second moment matrix (SMM) descriptor was employed to describe the shape. Furthermore, by comparing the shapes of the potential matches, we can normalize the template of SIFT descriptor and obtain the initial affine transformation. At last, we applied the iterative based method to achieve a fine registration with the estimated initial transformation parameters. The experiment results show that the proposed method is more robust to viewpoint changes and the accuracy of registration is better than feature based methods.

scholarly journals Improved stereo matching algorithm based on multi-scale fusion

2021 ◽  
Vol 39 (4) ◽  
pp. 876-882
Author(s):  
Xing Chen ◽  
Wenhai Zhang ◽  
Yu Hou ◽  
Lin Yang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Stereo Matching ◽  
Dynamic Programming Algorithm ◽  
Good Effect ◽  
Disparity Map ◽  
Data Set ◽  
Matching Algorithm ◽  
Multi Scale ◽  
Feature Pyramid

Aiming at the low matching accuracy of local stereo matching algorithm in weak texture or discontinuous disparity areas, a stereo matching algorithm combining multi-scale fusion of convolutional neural network (CNN) and feature pyramid structure (FPN) is proposed. The feature pyramid is applied on the basis of the convolutional neural network to realize the multi-scale feature extraction and fusion of the image, which improves the matching similarity of the image blocks. The guide graph filter is used to quickly and effectively complete the cost aggregation. The disparity selection stage adapts the improvement dynamic programming algorithm to obtain the initial disparity map. The initial disparity map is refined so as to obtain the final disparity map. The algorithm is trained and tested on the image provided by Middlebury data set, and the result shows that the disparity map obtained by the algorithm has good effect.

A Proposal of 3D Feature based on Occupancy of Point Cloud in Multi-Scale Shell Region

2016 ◽  
Vol 136 (8) ◽  
pp. 1078-1084
Author(s):  
Shoichi Takei ◽  
Shuichi Akizuki ◽  
Manabu Hashimoto
Keyword(s):  
Point Cloud ◽  
Multi Scale ◽  
Feature Based ◽  
Shell Region
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