scholarly journals Three-dimensional Spatial Localization Based on Binocular Vsion

2021 ◽  
Author(s):  
Lan Zang ◽  
Kun Zhang ◽  
Chuan Tian ◽  
Chong Shen ◽  
Bhatti Uzair Aslam ◽  
...  
Keyword(s):  
Spatial Data ◽  
Recognition Rate ◽  
Three Dimensional ◽  
Target Object ◽  
Block Matching ◽  
Calibration Error ◽  
Search Range ◽  
Block Matching Algorithm ◽  
Binocular Stereo Vision ◽  
Binocular Stereo

Abstract In order to solve the problems of low accuracy and unstable system performance existing in binocular vision alone, this paper proposes a threedimensional space recognition and positioning algorithm based on binocular stereo vision and deep learning algorithms. First, a binocular camera for Zhang Zhengyou calibrated by several adjustments, calibration error will eventually set at 0.10pixels best, select and SAD in block matching algorithm in the algorithm, the matching point of the search range reduction, mitigation data for subsequent experiments burden. Then input the three-dimensional spatial data calculated by using the binocular ”parallax” principle into the Faster R-CNN model for data training, extract and classify the target features, and finally realize real-time detection of the target object and its position coordinate information. The analysis of experimental data shows that when the best calibration error is selected and the number of data training is sufficient, the algorithm in this paper can effectively improve the quality of target detection. The positioning accuracy and target recognition rate are increased by about 3%-5%, and it can achieve faster fps.

Three-Dimensional Object Surface Reconstruction Based on Camera Calibration and SIFT

2015 ◽  
Vol 719-720 ◽  
pp. 1191-1197 ◽  
Author(s):  
Jun Zhang ◽  
Long Ye ◽  
Qin Zhang ◽  
Jing Ling Wang
Keyword(s):  
3D Reconstruction ◽  
Camera Calibration ◽  
Image Matching ◽  
Three Dimensional ◽  
Target Object ◽  
Object A ◽  
Binocular Stereo Vision ◽  
Binocular Stereo ◽  
Dimensional Object

This paper is focused on camera calibration, image matching, both of which are the key issues in three-dimensional (3D) reconstruction. In terms of camera calibration firstly, we adopt the method based on the method proposed by Zhengyou Zhang. In addition to this, it is selective for us to deal with tangential distortion. In respect of image matching, we use the SIFT algorithm, which is invariant to image translation, scaling, rotation, and partially invariant to illumination changes and to affine or 3D projections. It performs well in the follow-up matching the corresponding points. Lastly, we perform 3D reconstruction of the surface of the target object. A Graphical User Interface is designed to help us to realize the key function of binocular stereo vision, with better visualization. Apparently, the entire GUI brings convenience to the follow-up work.

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 Three-Dimensional Mirror Surface Measurement Based on Local Blur Analysis of Phase Measuring Deflectometry System

2020 ◽  
Vol 37 (5) ◽  
pp. 763-771
Author(s):  
Hongyu Sun ◽  
Le Wang ◽  
Zhan Song ◽  
Geng Chen
Keyword(s):  
Measurement Techniques ◽  
Three Dimensional ◽  
Block Matching ◽  
Surface Measurement ◽  
Mirror Surface ◽  
Reconstruction Accuracy ◽  
Surface Reflection ◽  
Block Matching Algorithm ◽  
Reflective Mirror ◽  
Spatially Varying

Despite the marked progress in recent years, structured light-based three-dimensional (3D) measurement techniques still have difficulty in capturing mirror surface reflection. The accuracy of 3D reconstruction for mirror objects should be further improved to adapt to the high reflectivity and curvature of such objects. To improve the stripe definition and reconstruction accuracy of highly reflective mirror objects, this paper analyzes the local blur of defocus stripes in phase measuring deflectometry (PMD) system, and presents a method to analyze the spatially varying defocusing and de-blurring, with the aid of a 3D block matching algorithm, thereby focusing on defocus stripes. Experimental results show that the proposed method can achieve micron-level reconstruction accuracy of standard flat mirrors, and detect the defects on highly reflective mirror objects at a high precision.

The Technology of Circular Parts Precise Measurement Based on Binocular Calibration

2013 ◽  
Vol 333-335 ◽  
pp. 199-206
Author(s):  
Chuan Wang ◽  
Jie Yang ◽  
Yan Wei Zhou
Keyword(s):  
Stereo Vision ◽  
Image Matching ◽  
Precise Measurement ◽  
Three Dimensional ◽  
Epipolar Constraint ◽  
Binocular Stereo Vision ◽  
Binocular Stereo ◽  
Analytic Expression ◽  
Lm Algorithm ◽  
Calibration Plane

A method of measuring the radius of circular parts by binocular stereo vision technology is proposed. First of all the interior and exterior parameters of cameras are acquired by the camera calibration technology. Then the epipolar constraint can be calculated which contains the calibrated information. The image matching which uses the epipolar constraint is done to find the matching points. The three dimensional (3D) coordinates of edges points are reconstructed through trigonometry reconstruction. At last the analytic expression of the plane in which the circle lies and the circles radius are calculated in two steps by Levenberg-Marquardt (LM) algorithm. The proposed method does not require the prior knowledge of position between the measuring plane and the calibration plane which monocular measurement needs. Experimental results show that the measurement has high precision.

scholarly journals A Framework for Identification of Healthy Potted Seedlings in Automatic Transplanting System Using Computer Vision

2021 ◽  
Vol 12 ◽  
Author(s):  
Xin Jin ◽  
Chenglin Wang ◽  
Kaikang Chen ◽  
Jiangtao Ji ◽  
Suchwen Liu ◽  
...  
Keyword(s):  
Computer Vision ◽  
Three Dimensional ◽  
Comparison Method ◽  
Block Matching ◽  
Identification Accuracy ◽  
Position Information ◽  
Block Matching Algorithm ◽  
Fundamental Process ◽  
Vegetable Seedlings ◽  
Vegetable Cultivation

Automatic transplanting of seedlings is of great significance to vegetable cultivation factories. Accurate and efficient identification of healthy seedlings is the fundamental process of automatic transplanting. This study proposed a computer vision-based identification framework of healthy seedlings. Vegetable seedlings were planted in trays in the form of potted seedlings. Two-color index operators were proposed for image preprocessing of potted seedlings. An optimal thresholding method based on the genetic algorithm and the three-dimensional block-matching algorithm (BM3D) was developed to denoise and segment the image of potted seedlings. The leaf area of the potted seedling was measured by machine vision technology to detect the growing status and position information of the potted seedling. Therefore, a smart identification framework of healthy vegetable seedlings (SIHVS) was constructed to identify healthy potted seedlings. By comparing the identification accuracy of 273 potted seedlings images, the identification accuracy of the proposed method is 94.33%, which is higher than 89.37% obtained by the comparison method.

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.

scholarly journals Using nonlocal filtering and feature extraction approaches in three-dimensional face recognition by Kinect

2018 ◽  
Vol 15 (4) ◽  
pp. 172988141878774 ◽  
Author(s):  
Shahram Mohammadi ◽  
Omid Gervei
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Recognition Rate ◽  
Three Dimensional ◽  
Component Analysis ◽  
Block Matching ◽  
Depth Image ◽  
Support Vector ◽  
Depth Images ◽  
Dimensional Face

To use low-cost depth sensors such as Kinect for three-dimensional face recognition with an acceptable rate of recognition, the challenges of filling up nonmeasured pixels and smoothing of noisy data need to be addressed. The main goal of this article is presenting solutions for aforementioned challenges as well as offering feature extraction methods to reach the highest level of accuracy in the presence of different facial expressions and occlusions. To use this method, a domestic database was created. First, the noisy pixels-called holes-of depth image is removed by solving multiple linear equations resulted from the values of the surrounding pixels of the holes. Then, bilateral and block matching 3-D filtering approaches, as representatives of local and nonlocal filtering approaches, are used for depth image smoothing. Curvelet transform as a well-known nonlocal feature extraction technique applied on both RGB and depth images. Two unsupervised dimension reduction techniques, namely, principal component analysis and independent component analysis, are used to reduce the dimension of extracted features. Finally, support vector machine is used for classification. Experimental results show a recognition rate of 90% for just depth images and 100% when combining RGB and depth data of a Kinect sensor which is much higher than other recently proposed algorithms.

A High-Precision Binocular Measurement System

2013 ◽  
Vol 823 ◽  
pp. 402-405
Author(s):  
Yue Gang Fu ◽  
Fan Hao Jin
Keyword(s):  
Robot Navigation ◽  
Three Dimensional ◽  
Distant Object ◽  
Dimensional Measurement ◽  
Binocular Stereo Vision ◽  
Unique Measure ◽  
Three Dimensional Measurement ◽  
Binocular Stereo ◽  
Dimensional Measurements ◽  
Binocular Measurement

The system uses three-dimensional measurement of binocular vision theory, through optical, mechanical, computer and other aspects of the technology to measure the target without contact, and up to a certain precision. In the high-voltage area, high-altitude target,and the target which is not easy to touch, there is a unique measure advantage. Binocular stereo vision is based on the principle of parallax and using imaging devices from different locations to obtain two images of the measured object and obtain three dimensional geometric of target by positional deviation between corresponding points in computer image. Binocular stereo currently used in four areas: robot navigation, micro operating system parameter detection, three-dimensional measurements and virtual reality. In addition, the system not only to measure the length of the distant object, the system can also be used to measure the width, surface area, height and tilt angle. As the tip of an optical imaging technology this system has a broad application prospects in the future.

Sign in / Sign up

Export Citation Format

Share Document