scholarly journals An Efficient Local Stereo Matching Algorithm for Dense Disparity Map Estimation Based on More Effective Use of Intensity Information and Matching Constraints

2009 ◽  
Vol E92-D (5) ◽  
pp. 1159-1167 ◽  
Author(s):  
Ali M. FOTOUHI ◽  
Abolghasem A. RAIE
Keyword(s):  
Stereo Matching ◽  
Map Estimation ◽  
Disparity Map ◽  
Matching Algorithm ◽  
Intensity Information ◽  
Effective Use ◽  
Matching Constraints

scholarly journals mproved Stereo Matching Algorithm based on Census Transform and Dynamic Histogram Cost Computation

2021 ◽  
Vol 11 (8) ◽  
pp. 48-57
Author(s):  
A. F. Kadmin ◽  
◽  
R. A. Hamzah ◽  
M. N. Abd Manap ◽  
M. S. Hamid ◽  
...  
Keyword(s):  
Stereo Matching ◽  
Disparity Map ◽  
Local Method ◽  
Matching Algorithm ◽  
Census Transform ◽  
Traditional Taxonomy ◽  
Matching Cost ◽  
Winner Takes All ◽  
The Cost ◽  
Vision Algorithm

Stereo matching is a significant subject in the stereo vision algorithm. Traditional taxonomy composition consists of several issues in the stereo correspondences process such as radiometric distortion, discontinuity, and low accuracy at the low texture regions. This new taxonomy improves the local method of stereo matching algorithm based on the dynamic cost computation for disparity map measurement. This method utilised modified dynamic cost computation in the matching cost stage. A modified Census Transform with dynamic histogram is used to provide the cost volume. An adaptive bilateral filtering is applied to retain the image depth and edge information in the cost aggregation stage. A Winner Takes All (WTA) optimisation is applied in the disparity selection and a left-right check with an adaptive bilateral median filtering are employed for final refinement. Based on the dataset of standard Middlebury, the taxonomy has better accuracy and outperformed several other state-ofthe-art algorithms. Keywords—Stereo matching, disparity map, dynamic cost, census transform, local method

A Parallel Stereo Matching Algorithm Core for FPGA Modeled by DSP Builder

2014 ◽  
Vol 536-537 ◽  
pp. 67-76
Author(s):  
Xiang Zhang ◽  
Zhang Wei Chen
Keyword(s):  
Stereo Matching ◽  
Cost Calculation ◽  
Stereo Pair ◽  
Disparity Map ◽  
High Definition ◽  
Matching Algorithm ◽  
Census Transform ◽  
Cost Aggregation ◽  
Dsp Builder ◽  
Fpga Chip

This paper proposes a FPGA implementation to apply a stereo matching algorithm based on a kind of sparse census transform in a FPGA chip which can provide a high-definition dense disparity map in real-time. The parallel stereo matching algorithm core involves census transform, cost calculation and cost aggregation modules. The circuits of the algorithm core are modeled by the Matlab/Simulink-based tool box: DSP Builder. The system can process many different sizes of stereo pair images through a configuration interface. The maximum horizon resolution of stereo images is 2048.

scholarly journals A Line-Based Adaptive-Weight Matching Algorithm Using Loopy Belief Propagation

2015 ◽  
Vol 2015 ◽  
pp. 1-13
Author(s):  
Hui Li ◽  
Xiao-Guang Zhang ◽  
Zheng Sun
Keyword(s):  
Stereo Matching ◽  
Belief Propagation ◽  
Mean Shift ◽  
Spatial Proximity ◽  
Consistency Checking ◽  
Disparity Map ◽  
Matching Effect ◽  
Matching Algorithm ◽  
Adaptive Weight ◽  
Loopy Belief Propagation

In traditional adaptive-weight stereo matching, the rectangular shaped support region requires excess memory consumption and time. We propose a novel line-based stereo matching algorithm for obtaining a more accurate disparity map with low computation complexity. This algorithm can be divided into two steps: disparity map initialization and disparity map refinement. In the initialization step, a new adaptive-weight model based on the linear support region is put forward for cost aggregation. In this model, the neural network is used to evaluate the spatial proximity, and the mean-shift segmentation method is used to improve the accuracy of color similarity; the Birchfield pixel dissimilarity function and the census transform are adopted to establish the dissimilarity measurement function. Then the initial disparity map is obtained by loopy belief propagation. In the refinement step, the disparity map is optimized by iterative left-right consistency checking method and segmentation voting method. The parameter values involved in this algorithm are determined with many simulation experiments to further improve the matching effect. Simulation results indicate that this new matching method performs well on standard stereo benchmarks and running time of our algorithm is remarkably lower than that of algorithm with rectangle-shaped support region.

scholarly journals A new function of stereo matching algorithm based on hybrid convolutional neural network

2022 ◽  
Vol 25 (1) ◽  
pp. 223
Author(s):  
Mohd Saad Hamid ◽  
Nurulfajar Abd Manap ◽  
Rostam Affendi Hamzah ◽  
Ahmad Fauzan Kadmin ◽  
Shamsul Fakhar Abd Gani ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Stereo Matching ◽  
Three Dimensional ◽  
Bilateral Filter ◽  
Disparity Map ◽  
Matching Algorithm ◽  
Matching Cost ◽  
The Difference ◽  
Aggregation Step

This paper proposes a new hybrid method between the learning-based and handcrafted methods for a stereo matching algorithm. The main purpose of the stereo matching algorithm is to produce a disparity map. This map is essential for many applications, including three-dimensional (3D) reconstruction. The raw disparity map computed by a convolutional neural network (CNN) is still prone to errors in the low texture region. The algorithm is set to improve the matching cost computation stage with hybrid CNN-based combined with truncated directional intensity computation. The difference in truncated directional intensity value is employed to decrease radiometric errors. The proposed method’s raw matching cost went through the cost aggregation step using the bilateral filter (BF) to improve accuracy. The winner-take-all (WTA) optimization uses the aggregated cost volume to produce an initial disparity map. Finally, a series of refinement processes enhance the initial disparity map for a more accurate final disparity map. This paper verified the performance of the algorithm using the Middlebury online stereo benchmarking system. The proposed algorithm achieves the objective of generating a more accurate and smooth disparity map with different depths at low texture regions through better matching cost quality.

A Stereo Matching Algorithm with Support Regions Based on Color and Texture Estimate

2012 ◽  
Vol 433-440 ◽  
pp. 3656-3661
Author(s):  
Cheng Hui Zhu ◽  
Qi Yi Jiao ◽  
Jian Ping Wang ◽  
Xiao Bing Xu
Keyword(s):  
Stereo Matching ◽  
Experimental Results ◽  
Disparity Map ◽  
Matching Algorithm ◽  
Support Weight ◽  
Adjustment Method ◽  
Initial Support

A stereo matching algorithm with support regions based on color and texture estimate is proposed. Firstly, the initial support regions are selected from the image according to the distribution of the quantized color labels. Then, the texture similarity is used to determine the arm length growing and combine adjacent regions. The accurate support regions are obtained. Thirdly, the support weight is introduced under the constraint of support region. Finally, the initial disparity can be corrected by using disparity adjustment method until a reasonable disparity map is obtained. The experimental results show that the good disparity result can be obtained.

scholarly journals Stereo Matching Algorithm Based on Hybrid Convolutional Neural Network and Directional Intensity Difference

2021 ◽  
Vol 11 (6) ◽  
pp. 86-96
Author(s):  
Mohd Saad Hamid ◽  
◽  
Nurulfajar Abd Manap ◽  
Rostam Affendi Hamzah ◽  
Ahmad Fauzan Kadmin
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Stereo Matching ◽  
Good Accuracy ◽  
Intensity Difference ◽  
Disparity Map ◽  
Matching Algorithm ◽  
Matching Cost ◽  
Autonomous Vehicle Navigation ◽  
Repetitive Pattern

Fundamentally, a stereo matching algorithm produces a disparity map or depth map. This map contains valuable information for many applications, such as range estimation, autonomous vehicle navigation and 3D surface reconstruction. The stereo matching process faces various challenges to get an accurate result for example low texture area, repetitive pattern and discontinuity regions. The proposed algorithm must be robust and viable with all of these challenges and is capable to deliver good accuracy. Hence, this article proposes a new stereo matching algorithm based on a hybrid Convolutional Neural Network (CNN) combined with directional intensity differences at the matching cost stage. The proposed algorithm contains a deep learning-based method and a handcrafted method. Then, the bilateral filter is used to aggregate the matching cost volume while preserving the object edges. The Winner-Take-All (WTA) is utilized at the optimization stage which the WTA normalizes the disparity values. At the last stage, a series of refinement processes will be applied to enhance the final disparity map. A standard benchmarking evaluation system from the Middlebury Stereo dataset is used to measure the algorithm performance. This dataset provides images with the characteristics of low texture area, repetitive pattern and discontinuity regions. The average error produced for all pixel regions is 8.51%, while the nonoccluded region is 5.77%. Based on the experimental results, the proposed algorithm produces good accuracy and robustness against the stereo matching challenges. It is also competitive with other published methods and can be used as a complete algorithm

scholarly journals Development of stereo matching algorithm based on sum of absolute RGB color differences and gradient Matching

2020 ◽  
Vol 10 (3) ◽  
pp. 2375
Author(s):  
Rostam Affendi Hamzah ◽  
M. G. Yeou Wei ◽  
N. Syahrim Nik Anwar
Keyword(s):  
Stereo Matching ◽  
Accurate Result ◽  
Bilateral Filter ◽  
Disparity Map ◽  
Edge Preserving ◽  
High Brightness ◽  
Matching Algorithm ◽  
Second Stage ◽  
Quantitative Measurements ◽  
Color Differences

This paper proposes a new stereo matching algorithm which uses local-based method. The Sum of Absolute Differences (SAD) algorithm produces accurate result on the disparity map for the textured regions. However, this algorithm is sensitive to low texture areas and high noise on images with high different brightness and contrast. To get over these problems, the proposed algorithm utilizes SAD algorithm with RGB color channels differences and combination of gradient matching to improve the accuracy on the images with high brightness and contrast. Additionally, an edge-preserving filter is used at the second stage which is known as Bilateral Filter (BF). The BF filter is capable to work with the low texture areas and to reduce the noise and sharpen the images. Additionally, BF is strong  against the  distortions due to high brightness and contrast. The proposed work in this paper produces accurate results and performs much better compared with some established algorithms. This comparison is based on the standard quantitative measurements using the stereo benchmarking evaluation from the Middlebury.

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.

Disparity Map Acquisition Based on Matlab Calibration Toolbox and OpenCV Stereo Matching Algorithm

2014 ◽  
Vol 926-930 ◽  
pp. 3030-3033 ◽  
Author(s):  
Wei Gu ◽  
Jing Yin ◽  
Xiao Fang Yang ◽  
Pu Liu
Keyword(s):  
Information Acquisition ◽  
Stereo Matching ◽  
Block Matching ◽  
3D Measurement ◽  
Image Correction ◽  
Disparity Map ◽  
Matching Algorithm ◽  
3D Data ◽  
Difficult Issue

The key and difficult issue in the research of binocular vision-based 3D measurement is the accurate calibration of internal and external parameters of the camera and stereo matching. Matlab calibration is more efficient and accurate compared with manual or OpenCV calibration. In this paper, binocular camera is calibrated by Matlab calibration toolbox, and calibration parameters imported in OpenCV for follow-up image correction and stereo matching. By studying and comparing Block Matching (BM) and Graph Cut (GC) stereo matching algorithms, a disparity image of the object is obtained, thus laying foundation for follow-up 3D data information acquisition and reconstruction.

Sign in / Sign up

Export Citation Format

Share Document