Study on a New Sine Raster Generating Algorithm for Phase-Based Matching

2010 ◽  
Vol 29-32 ◽  
pp. 2633-2638
Author(s):  
Xiao Hui Liu ◽  
Kai Yong Jiang ◽  
Jun Yi Lin
Keyword(s):  
Stereo Matching ◽  
Phase Distribution ◽  
Gamma Correction ◽  
Matching Method ◽  
Binocular Stereo Vision ◽  
Binocular Stereo ◽  
Pair Matching ◽  
Efficient Matching ◽  
Influence Of Noise ◽  
Experiment Analysis

Phase-based Stereo Matching (PSM) is an efficient matching method in binocular stereo vision. It bases on correspondence points which have the same phase-distribution. Sine raster generating algorithm is a basic and important step in PSM study. This paper advances that using defocusing rectify-binary patterns (DRBP) method substituted the focusing-sinusoidal-patterns (FSP) method. It has following advantages: (1) No gamma correction is required; (2) The object illuminance measurement is not desired; (3) The influence of noise is minified. There are same phases distribution in correspond points of the pair matching lines by experiment analysis. The feasibility of using DRBP to generate sine raster in PSM is demonstrated.

Research of Stereo Matching Based on HALCON

2013 ◽  
Vol 415 ◽  
pp. 361-364
Author(s):  
Hui Yu Xiang ◽  
Zhe Li ◽  
Jia Jun Huang ◽  
Baoan Han
Keyword(s):  
Image Matching ◽  
Stereo Matching ◽  
Vision System ◽  
Difficult Problem ◽  
Matching Method ◽  
Matching Accuracy ◽  
Matching Algorithm ◽  
Binocular Stereo Vision ◽  
Binocular Stereo ◽  
Matching Window

Binocular stereo matching is a hot and difficult problem in machine vision. In this paper, based on the matching method of Halcon which is visual software perform image matching. First, performing binocular stereo vision system calibration, based on the calibration results acquired the epipolar standard geometric structure. Then, image matching researched under this structure. At last, using ncc matching algorithm, through comparing the different parameters matching window obtain ideal match results. Experiments prove that this method not only can effectively shorten matching time, but also can achieve higher matching accuracy.

scholarly journals High-Accuracy Recognition and Localization of Moving Targets in an Indoor Environment Using Binocular Stereo Vision

2021 ◽  
Vol 10 (4) ◽  
pp. 234
Author(s):  
Jing Ding ◽  
Zhigang Yan ◽  
Xuchen We
Keyword(s):  
Stereo Vision ◽  
Stereo Matching ◽  
Three Dimensional ◽  
Target Localization ◽  
Parallel Structure ◽  
Moving Target ◽  
Target Area ◽  
Moving Targets ◽  
Binocular Stereo Vision ◽  
Binocular Stereo

To obtain effective indoor moving target localization, a reliable and stable moving target localization method based on binocular stereo vision is proposed in this paper. A moving target recognition extraction algorithm, which integrates displacement pyramid Horn–Schunck (HS) optical flow, Delaunay triangulation and Otsu threshold segmentation, is presented to separate a moving target from a complex background, called the Otsu Delaunay HS (O-DHS) method. Additionally, a stereo matching algorithm based on deep matching and stereo vision is presented to obtain dense stereo matching points pairs, called stereo deep matching (S-DM). The stereo matching point pairs of the moving target were extracted with the moving target area and stereo deep matching point pairs, then the three dimensional coordinates of the points in the moving target area were reconstructed according to the principle of binocular vision’s parallel structure. Finally, the moving target was located by the centroid method. The experimental results showed that this method can better resist image noise and repeated texture, can effectively detect and separate moving targets, and can match stereo image points in repeated textured areas more accurately and stability. This method can effectively improve the effectiveness, accuracy and robustness of three-dimensional moving target coordinates.

scholarly journals A Real-Time Range Finding System with Binocular Stereo Vision

10.5772/50921 ◽  
2012 ◽  
Vol 9 (1) ◽  
pp. 26 ◽  
Author(s):  
Xiao-Bo Lai ◽  
Hai-Shun Wang ◽  
Yue-Hong Xu
Keyword(s):  
Real Time ◽  
Stereo Vision ◽  
Stereo Matching ◽  
Single Point ◽  
Time Range ◽  
Real Time Control ◽  
Matching Algorithm ◽  
Range Finding ◽  
Binocular Stereo Vision ◽  
Binocular Stereo

To acquire range information for mobile robots, a TMS320DM642 DSP-based range finding system with binocular stereo vision is proposed. Firstly, paired images of the target are captured and a Gaussian filter, as well as improved Sobel kernels, are achieved. Secondly, a feature-based local stereo matching algorithm is performed so that the space location of the target can be determined. Finally, in order to improve the reliability and robustness of the stereo matching algorithm under complex conditions, the confidence filter and the left-right consistency filter are investigated to eliminate the mismatching points. In addition, the range finding algorithm is implemented in the DSP/BIOS operating system to gain real-time control. Experimental results show that the average accuracy of range finding is more than 99% for measuring single-point distances equal to 120cm in the simple scenario and the algorithm takes about 39ms for ranging a time in a complex scenario. The effectivity, as well as the feasibility, of the proposed range finding system are verified.

Binocular stereo matching method based on structure tensor

2016 ◽  
Author(s):  
Xiaowei Song ◽  
Manyi Yang ◽  
Yubo Fan ◽  
Lei Yang
Keyword(s):  
Stereo Matching ◽  
Structure Tensor ◽  
Matching Method ◽  
Binocular Stereo

Object Tracking and Positioning Based on Stereo Vision

2013 ◽  
Vol 303-306 ◽  
pp. 313-317 ◽  
Author(s):  
Zhong Wei Zhou ◽  
Min Xu ◽  
Wei Fu ◽  
Ji Zeng Zhao
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Stereo Vision ◽  
Stereo Matching ◽  
Three Dimensional ◽  
Spatial Location ◽  
Binocular Stereo Vision ◽  
Camshift Algorithm ◽  
Spatial Location Information ◽  
Binocular Stereo

The goal of this paper is to present a method for object tracking and positioning based on stereo vision in real time. The method effectively combined stereo matching algorithm with object tracking algorithm, and calculated the spatial location information of the object by using binocular stereo vision while the object is being tracked. The stereo matching used dynamic programming, image pyramids and control points modification algorithm, and the object tracking mainly utilized CamShift algorithm in this paper. The experimental results have confirmed that the proposed method realized real-time tracking for moving object, accurate calculating for the object three-dimensional coordinates, which meet the applied needs of servo follow-up system.

Measurement Error Correction of y Coordinate in Three Dimensional Localization of Tomatoes Using Binocular Stereo Vision

2014 ◽  
Vol 610 ◽  
pp. 209-215 ◽  
Author(s):  
Rong Xiang ◽  
Huan Yu Jiang ◽  
Yi Bin Ying
Keyword(s):  
Linear Regression ◽  
Measurement Errors ◽  
Regression Models ◽  
Stereo Matching ◽  
Three Dimensional ◽  
Linear Regression Models ◽  
Matching Methods ◽  
Binocular Stereo Vision ◽  
Binocular Stereo ◽  
Binary Linear Regression

Accuracy three dimensional coordinates of fruits and vegetables are very important to harvesting robots to harvest fruits and vegetables correctly. To decrease the measurement errors of the y coordinates of tomatoes, we analyzed the measurement errors of y coordinate acquired using binocular stereo vision based on three stereo matching methods. These three stereo matching methods were centroid-based, area-based, and combination stereo matching methods. After stereo matching, the three dimensional coordinates of tomatoes could be acquired based on the triangle ranging principle. Tests of 225 pairs of stereo images of three plastic balls used as normal balls acquired at the distances from 300 to 1000 mm showed that the ranges of the measurement errors of y coordinate acquired based on three stereo matching methods changed with the image acquisition distances obviously. Moreover, the measurement errors of y coordinate appeared linear decreasing trends approximately. Therefore, binary linear regression models were set up to reduce the ranges of the measurement errors of y coordinate of three balls. These models were used as correction models of the measurement values of y coordinate and were helpful to reduce the measurement errors of y coordinate. However, there were owe correction and overcorrection conditions when the image acquisition distances were smaller and larger than 750 mm separately. Then, the correction models based on piecewise binary linear regression were used to solve this problem. The ranges of the measurement errors of y coordinate were reduced further. Tests of 225 pairs of stereo images of three tomatoes acquired at the distances from 300 to 1000 mm showed that the ranges of the measurement errors of y coordinate acquired based on three stereo matching methods were separately from [-20.9, -6.6], [-19.9, -3.44], [-19.9, -3.48] mm to [-6.84, -0.06], [-5.84, -0.82], [-5.85, -0.83] mm after the correction using the piecewise binary linear regression models. It proved that the piecewise binary linear regression models were helpful to reduce the measurement errors of y coordinate in three dimensional localization of tomatoes using binocular stereo vision.

Baggage Specification Detection Based on the Binocular Vision

2013 ◽  
Vol 278-280 ◽  
pp. 861-865
Author(s):  
Qing Ji Gao ◽  
Lu Yang
Keyword(s):  
Stereo Vision ◽  
Stereo Matching ◽  
Three Dimensional ◽  
Detection Algorithm ◽  
Epipolar Constraint ◽  
Middle Point ◽  
Vision Measurement ◽  
Binocular Stereo Vision ◽  
Binocular Stereo ◽  
Stereo Vision Measurement

To the baggage specification automatically detection problem of self-service bag drop system, a baggage size detection algorithm based on stereo vision is proposed. The algorithm is based on binocular stereo vision measurement principle. Firstly, the canny edges of baggage image are extracted as the feature points. With the disparity gradient constraint and epipolar constraint, Stereo matching algorithm based on edge features is proposed, meanwhile, the two images play a symmetric role to ensure the reliability of matching in the matching process. The coordinates of the three dimensional points are derived with approximation of the middle point of the common perpendicular line in different planes. Experimental results show that the proposed algorithm can detect the baggage specification with appropriate accuracy.

3D Reconstruction Based on Binocular Stereo Vision of Robot

2011 ◽  
Vol 338 ◽  
pp. 645-648 ◽  
Author(s):  
Zhi Gang Niu ◽  
Li Jun Li ◽  
Tie Wang
Keyword(s):  
Stereo Vision ◽  
Coal Mine ◽  
Stereo Matching ◽  
Least Square Method ◽  
Least Square ◽  
Mine Detection ◽  
Binocular Stereo Vision ◽  
Harris Corner ◽  
Binocular Stereo ◽  
Projection Matrices

In order to meet the need of identifying obstacles and navigating for the Coal Mine Detection Robot which is used to rescue life and detect environment from coal mine disaster, binocular stereo vision is researched and 3D model of objects around the robot is reconstructed by means of two cameras of visual system built in the robot. The two cameras are calibrated and two projection matrices of them are obtained. Then, two images of the same scene are obtained by the two cameras. The matching points of two-dimensional coordinate are got through Harris corner extraction and stereo matching. According to the principle of binocular vision, equations are obtained and solved by least square method, which can calculated the discrete points of 3D coordinate.

Spatial Point Reconstruction Based on Binocular Stereo Vision

2014 ◽  
Vol 962-965 ◽  
pp. 2809-2813 ◽  
Author(s):  
Jie Yu ◽  
Yu Min Ge ◽  
Bao Shu Li ◽  
Shang Chen
Keyword(s):  
Stereo Vision ◽  
Stereo Matching ◽  
Three Dimensional ◽  
Robot Vision ◽  
Spatial Location ◽  
It Use ◽  
Spatial Point ◽  
Binocular Stereo Vision ◽  
Spatial Location Information ◽  
Binocular Stereo

Binocular stereo vision is an important branch of robot vision technology, it use two cameras in different position or a camera which can be move or rotate to shoot the same scene images, by calculating the parallax of spatial point in two images, get the spatial location information. There is a study based on the binocular stereo vision for three-dimensional spatial reconstruction, in view of the problem of vision image acquisition, camera calibration,stereo matching and 3d reconstruction in the binocular stereo application technology.

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