Target Identification and Location Algorithm Based on SURF-BRISK Operator

2016 ◽  
Vol 30 (06) ◽  
pp. 1655016 ◽  
Author(s):  
Qiong Liu ◽  
JiZhuang Hui ◽  
Li Luo ◽  
YanPu Yang
Keyword(s):  
Stereo Vision ◽  
Feature Matching ◽  
Hamming Distance ◽  
Target Identification ◽  
Vision System ◽  
Three Dimensional ◽  
Image Feature ◽  
Geometric Distortion ◽  
Work Piece ◽  
Target Image

Accurate and fast target image recognition is an important function of applications such as remote sensing imaging and medical imaging. However, an operator such as speeded up robust feature (SURF) cannot be accurately matched in the recognition process of a target image. This led us to propose the use of a method capable of matching identification, i.e. binary robust invariant scalable keypoints (BRISK) operators, in combination with SURF operators. The proposed algorithm combines the accuracy of SURF operators and the rapidity of BRISK operators to obtain a quick and accurate way of matching. The initial matching of image feature extraction for targets is performed using the SURF-BRISK algorithm, and similarity measurements of feature matching are performed for the feature points of initial matching using the Hamming distance. Then, secondary fine matching is performed using the M-estimator Sample and Consensus (MSAC) algorithm to eliminate mismatched point pairs in order to achieve recognition of target images. Then, the three-dimensional coordinates of the work piece are obtained by using a binocular stereo vision system to provide location coordinates for the robots to grasp the work pieces accurately. In the experiment, stereo vision matching is conducted for targets obtained using the SURF-BRISK algorithm, and the location coordinates of targets are passed to the robot controller. The experimental results show that if the special geometric distortion is neglected, this method can be adapted for accurate positioning of the target; hence, it can identify the target in complex environments, access the location coordinates of the target, and achieve accurate robotic grasping of the work piece in real time.

scholarly journals Stereo Vision System for Vision-Based Control of Inspection-Class ROVs

2021 ◽  
Vol 13 (24) ◽  
pp. 5075
Author(s):  
Stanisław Hożyń ◽  
Bogdan Żak
Keyword(s):  
Stereo Vision ◽  
Feature Matching ◽  
Control Method ◽  
Vision System ◽  
Image Features ◽  
Image Feature ◽  
Stereo Pair ◽  
Navigation Systems ◽  
Stereo Vision System ◽  
Vision Based Control

The inspection-class Remotely Operated Vehicles (ROVs) are crucial in underwater inspections. Their prime function is to allow the replacing of humans during risky subaquatic operations. These vehicles gather videos from underwater scenes that are sent online to a human operator who provides control. Furthermore, these videos are used for analysis. This demands an RGB camera operating at a close distance to the observed objects. Thus, to obtain a detailed depiction, the vehicle should move with a constant speed and a measured distance from the bottom. As very few inspection-class ROVs possess navigation systems that facilitate these requirements, this study had the objective of designing a vision-based control method to compensate for this limitation. To this end, a stereo vision system and image-feature matching and tracking techniques were employed. As these tasks are challenging in the underwater environment, we carried out analyses aimed at finding fast and reliable image-processing techniques. The analyses, through a sequence of experiments designed to test effectiveness, were carried out in a swimming pool using a VideoRay Pro 4 vehicle. The results indicate that the method under consideration enables automatic control of the vehicle, given that the image features are present in stereo-pair images as well as in consecutive frames captured by the left camera.

Research on People Counting Based on Stereo Vision

2013 ◽  
Vol 373-375 ◽  
pp. 619-623
Author(s):  
Yun Zhou Zhang ◽  
Shou Shuai Xu ◽  
Liang Gao ◽  
Shan Bao Yang ◽  
Xiao Lin Su
Keyword(s):  
Stereo Vision ◽  
Moving Objects ◽  
Feature Matching ◽  
Object Segmentation ◽  
Vision System ◽  
Office Building ◽  
Disparity Map ◽  
Passenger Flow ◽  
The People ◽  
Binocular Vision System

In both industrial field and office building, the accurate statistic of people who enter or leave the elevator has important practical meaning in security and analysis of passenger flow. We present a binocular vision system to count the people pass by. The camera is set in the height of 2.45 meters to monitor the people overhead in order to reduce the overlap of pedestrians. The object segment and tracking method proposed in this paper show good result with the disparity map gained by the dual-camera. Dynamic promotion of threshold is used in the object segmentation. Feature matching is used to track the moving objects. The system can get the number of people accurate and timely. Experiment results show that our system has good performance under relatively complex circumstance.

scholarly journals Three-dimensional mechanical parts reconstruction technology based on two-dimensional image

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142091000
Author(s):  
Jiaofei Huo ◽  
Xiaomo Yu
Keyword(s):  
Image Processing ◽  
Stereo Vision ◽  
Stereo Matching ◽  
Vision System ◽  
Three Dimensional ◽  
Three Dimensional Reconstruction ◽  
Stereo Vision System ◽  
Binocular Stereo Vision ◽  
Extraction Algorithm ◽  
Dimensional Reconstruction

With the development of computer technology and three-dimensional reconstruction technology, three-dimensional reconstruction based on visual images has become one of the research hotspots in computer graphics. Three-dimensional reconstruction based on visual image can be divided into three-dimensional reconstruction based on single photo and video. As an indirect three-dimensional modeling technology, this method is widely used in the fields of film and television production, cultural relics restoration, mechanical manufacturing, and medical health. This article studies and designs a stereo vision system based on two-dimensional image modeling technology. The system can be divided into image processing, camera calibration, stereo matching, three-dimensional point reconstruction, and model reconstruction. In the part of image processing, common image processing methods, feature point extraction algorithm, and edge extraction algorithm are studied. On this basis, interactive local corner extraction algorithm and interactive local edge detection algorithm are proposed. It is found that the Harris algorithm can effectively remove the features of less information and easy to generate clustering phenomenon. At the same time, the method of limit constraints is used to match the feature points extracted from the image. This method has high matching accuracy and short time. The experimental research has achieved good matching results. Using the platform of binocular stereo vision system, each step in the process of three-dimensional reconstruction has achieved high accuracy, thus achieving the three-dimensional reconstruction of the target object. Finally, based on the research of three-dimensional reconstruction of mechanical parts and the designed binocular stereo vision system platform, the experimental results of edge detection, camera calibration, stereo matching, and three-dimensional model reconstruction in the process of three-dimensional reconstruction are obtained, and the full text is summarized, analyzed, and prospected.

The Calibration Method of a stereo-Vision-Based Coordinates Measurement System via a Two-Axis Turntable--The Combination of Stereo Vision and Turntable

2021 ◽  
Vol 14 ◽  
Author(s):  
Zimiao Zhang ◽  
Zhiwu Wang ◽  
Shihai Zhang ◽  
Anqi Fu
Keyword(s):  
Stereo Vision ◽  
Measurement System ◽  
Vision System ◽  
Three Dimensional ◽  
Calibration Method ◽  
Measurement Range ◽  
Measurement Technology ◽  
Rotation Axes ◽  
Internal Parameters ◽  
Calibration Methods

Background: Stereo-vision-based three-dimensional coordinates measurement technology has been widely applied in the military or civil fields. There are two problems that need to be solved. The first problem is that each camera internal parameters and the two cameras external parameters need to be calibrated. To increase the measurement range, usually the turntable is used with the stereo vision system together. The second problem is the calibration of the turntable. Objective: The aim of the study is to construct and calibrate a stereo-vision-based coordinates measurement system via a two-axis turntable. Methods: Considering that the internal parameters of each camera do not change during the measurement process and the complicated optimization process of one-step self-calibration, a two-step stereo vision calibration method is proposed. In the first step, we calibrate the internal parameters of each camera through a specially designed planar target with circular points. In the second step, on the basis of the calibrated results of the internal parameters, the two cameras external parameters are calibrated through a simple target which could be distributed in the measurement volume. For the calibration of the two-axis turntable, we calibrated the rotation axes of the turntable and the coordinates of points in the 3D space could be measured considering the non-orthogonality of the axes. Results: Some experiments are provided to examine the calibration methods we proposed. They are the plane target measurement experiments, the standard ball center coordinates measurement experiments and target pose measurement experiments. Experiment results demonstrate the superiority of the calibration method we proposed. Conclusion: We studied the calibration methods of the stereo-vision-based coordinates measurement system via a two-axis turntable. The experimental results show the measurement accuracy of our system is less than 0.1mm.

FPGA-Based Stereo Vision System Using Gradient Feature Correspondence

2015 ◽  
Vol 27 (6) ◽  
pp. 681-690 ◽  
Author(s):  
Hayato Hagiwara ◽  
◽  
Yasufumi Touma ◽  
Kenichi Asami ◽  
Mochimitsu Komori
Keyword(s):  
Mobile Robot ◽  
Stereo Vision ◽  
Vision System ◽  
Image Feature ◽  
Interest Point ◽  
Feature Correspondence ◽  
Speeded Up Robust Features ◽  
Stereo Vision System ◽  
Simpler Algorithm ◽  
Gradient Feature

<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00270006/10.jpg"" width=""300"" /> Mobile robot with a stereo vision</div>This paper describes an autonomous mobile robot stereo vision system that uses gradient feature correspondence and local image feature computation on a field programmable gate array (FPGA). Among several studies on interest point detectors and descriptors for having a mobile robot navigate are the Harris operator and scale-invariant feature transform (SIFT). Most of these require heavy computation, however, and using them may burden some computers. Our purpose here is to present an interest point detector and a descriptor suitable for FPGA implementation. Results show that a detector using gradient variance inspection performs faster than SIFT or speeded-up robust features (SURF), and is more robust against illumination changes than any other method compared in this study. A descriptor with a hierarchical gradient structure has a simpler algorithm than SIFT and SURF descriptors, and the result of stereo matching achieves better performance than SIFT or SURF.

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