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.

scholarly journals Implementation of Stereo Vision Semi-Global Block Matching Methods for Distance Measurement

2018 ◽  
Vol 12 (2) ◽  
pp. 585 ◽  
Author(s):  
Raden Arief Setyawan ◽  
Rudy Sunoko ◽  
Mochammad Agus Choiron ◽  
Panca Mudji Rahardjo
Keyword(s):  
Stereo Vision ◽  
Stereo Matching ◽  
Distance Estimation ◽  
Distance Measurement ◽  
Block Matching ◽  
Disparity Map ◽  
Block Matching Algorithm ◽  
Object Distance ◽  
Left And Right ◽  
3D Movie

Stereo vision has become an attractive topic research in the last decades. Many implementations such as the autonomous car, 3D movie, 3D object generation, are produced using this technique. The advantages of using two cameras in stereo vision are the disparity map between images. Disparity map will produce distance estimation of the object. Distance measurement is a crucial parameter for an autonomous car. The distance between corresponding points between the left and right images must be precisely measured to get an accurate distance. One of the most challenging in stereo vision is to find corresponding points between left and right images (stereo matching). This paper proposed distance measurement using stereo vision using Semi-Global Block Matching algorithm for stereo matching purpose. The object is captured using a calibrated stereo camera. The images pair then optimized using WLS Filter to reduce noises. The implementation results of this algorithm are furthermore converted to a metric unit for distance measurement. The result shows that the stereo vision distance measurement using Semi-Global Block Matching gives a good result. The obtained best result of this work contains error of less than 1% for 1m distance

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

Regional fuzzy binocular stereo matching algorithm based on global correlation coding for 3D measurement of rail surface

Optik ◽  
2020 ◽  
Vol 207 ◽  
pp. 164488 ◽  
Author(s):  
Zhaoxin Wang ◽  
Jiang Yue ◽  
Jing Han ◽  
Yong Jin ◽  
Baoming Li
Keyword(s):  
Stereo Matching ◽  
3D Measurement ◽  
Matching Algorithm ◽  
Global Correlation ◽  
Binocular Stereo

COMPUTATION AND PERFORMANCE ANALYSIS OF DOUBLE STAGE FILTER FOR IMAGE PROCESSING

2015 ◽  
Vol 77 (19) ◽  
Author(s):  
Teo Chee Huat ◽  
Nurulfajar Abdul Manap ◽  
Masrullizam Mat Ibrahim
Keyword(s):  
Stereo Matching ◽  
Ground Truth ◽  
Block Matching ◽  
Basic Block ◽  
Median Filtering ◽  
Disparity Map ◽  
Evaluation Functions ◽  
Map Segmentation ◽  
Disparity Maps ◽  
And Performance

Double Stage Filter (DSF) is a hybrid stereo matching algorithm which consists of basic block matching and dynamic programming algorithms, basic median filtering and new technique of segmentation. The algorithm acquire disparity maps which will be analyzed by using evaluation functions such as PSNR, MSE and SSIM. The computation of DSF and existing algorithms are presented in this paper. The Phase 2 in DSF is to remove the unwanted aspects such as depth discontinuities and holes from occlusion from the raw disparity map. Segmentation, merging and median filtering are the major parts for post processing of DSF algorithm. From the results of evaluation functions, the disparity maps attained by DSF is closer to the ground truth compared to other algorithms while its computation takes only few seconds longer than DP algorithm but its capable to obtain better results of disparity map.

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.

scholarly journals Equal Baseline Camera Array—Calibration, Testbed and Applications

2021 ◽  
Vol 11 (18) ◽  
pp. 8464
Author(s):  
Adam L. Kaczmarek ◽  
Bernhard Blaschitz
Keyword(s):  
Stereo Matching ◽  
Outdoor Environment ◽  
Learning Technology ◽  
Disparity Map ◽  
Stereo Camera ◽  
Camera Array ◽  
3D Data ◽  
Novel Approach ◽  
Calibration Methods ◽  
Wide Range

This paper presents research on 3D scanning by taking advantage of a camera array consisting of up to five adjacent cameras. Such an array makes it possible to make a disparity map with a higher precision than a stereo camera, however it preserves the advantages of a stereo camera such as a possibility to operate in wide range of distances and in highly illuminated areas. In an outdoor environment, the array is a competitive alternative to other 3D imaging equipment such as Structured-light 3D scanners or Light Detection and Ranging (LIDAR). The considered kinds of arrays are called Equal Baseline Camera Array (EBCA). This paper presents a novel approach to calibrating the array based on the use of self-calibration methods. This paper also introduces a testbed which makes it possible to develop new algorithms for obtaining 3D data from images taken by the array. The testbed was released under open-source. Moreover, this paper shows new results of using these arrays with different stereo matching algorithms including an algorithm based on a convolutional neural network and deep learning technology.

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.

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