Real-Time Rendering Based on GPU for Binocular Stereo System

2012 ◽  
pp. 492-499
Author(s):  
Zhuan Zheng ◽  
Ping An ◽  
Bing Zhao ◽  
Zhaoyang Zhang
Keyword(s):  
Real Time ◽  
Binocular Stereo

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.

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.

A Real-Time Image Matching Algorithm for Binocular Stereo Measurement System

2019 ◽  
Author(s):  
Jianyu Duan ◽  
Lingyu Sun ◽  
Lijun Li ◽  
Zongmiao Dai ◽  
Zhenkai Xiong ◽  
...  
Keyword(s):  
Real Time ◽  
Measurement System ◽  
Image Matching ◽  
Great Influence ◽  
Consensus Method ◽  
Feature Descriptor ◽  
Matching Algorithm ◽  
Real Time Image ◽  
Binocular Stereo ◽  
Time Image

Abstract Binocular stereo measurement system can obtain accurate three-dimensional information from two-dimensional images. It has been widely applied in many fields such as vehicle tracking, robot navigating, automatic crane lifting, as well as other fields. The crucial step of binocular stereo measurement is image matching. For the image matching, it is a great challenge to ensure both real-time and matching accuracy simultaneously. The image matching algorithm has a great influence on the image matching time and accuracy. In this paper, a real-time image matching algorithm for binocular stereo measurement system is proposed based on Speedup Robust Features (SURF) algorithm. In the proposed algorithm, firstly, the key feature points are identified by the original SURF algorithm method. Secondly, the main direction of the key feature point is determined by intensity centroid method. Then, the feature descriptor is calculated by the BRIEF binary method so that the time of feature description can be shortened. Finally, RANSAC (Random Sample Consensus) method is adopted to remove mismatching points. The experiments results show that the proposed algorithm can shorten image matching time obviously and improve the accuracy of matching points.

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