A Method on Monocular Vision of Camera for Spatial Position

2013 ◽  
Vol 281 ◽  
pp. 14-18 ◽  
Author(s):  
Zhen Yang ◽  
Ming Jun Wu ◽  
Fang Wang ◽  
Li Zhang ◽  
Li Na Gong
Keyword(s):  
Edge Detection ◽  
Geometric Model ◽  
Digital Camera ◽  
Calibration Method ◽  
Spatial Position ◽  
Monocular Vision ◽  
Feature Points ◽  
Intrinsic Parameter ◽  
Extrinsic Parameter ◽  
Parameter Matrix

In this paper, a method on monocular vision for spatial position is presented. The geometric model of digital camera is built and decomposed to intrinsic and extrinsic parameter matrixes under a certain assumption. Firstly, the intrinsic parameter matrix of camera is determined. Then, the extrinsic parameter matrix is solved according to the information of image. The edge detection operators are worked and in order to detect the accuracy of this method, the point of some feature points are obtained by using the principle of least squares. Compared with the conventional calibration method, this method is simple, fast and robust.

Multi-View 3D Object Reconstruction Using Coordinate Transformation

2013 ◽  
Vol 284-287 ◽  
pp. 2167-2170 ◽  
Author(s):  
Ji Hun Park
Keyword(s):  
Coordinate Transformation ◽  
Digital Camera ◽  
Optimal Solution ◽  
Calibration Method ◽  
Target Object ◽  
Feature Points ◽  
Geometric Information ◽  
Local Coordinates ◽  
Camera Parameters ◽  
Parameter Values

The algorithm presented in this paper takes at least two images taken by an ordinary digital camera, computes camera parameters and outputs scene geometric information. The method takes at least one right-angle triangle in the scene, install a local coordinate at each triangle, and compute camera parameters by using the local coordinate transformation. The algorithm computes orientations and displacement relationships among the triangles in the scene while correcting the previously computed camera parameter values. In order to find optimal solution, we set optimization variables as camera calibration parameters and coordinate transformation values between local coordinates. The background eliminated images and calculated camera parameters allow us to reconstruct a 3D target object in VRML. The merit of this algorithm is in handling varying focal camera, in easy initial guessing value computation and in using fewer feature points compared to Zhang’s calibration method.

Monocular Camera Calibration Method for Robot System

2015 ◽  
Vol 741 ◽  
pp. 697-700 ◽  
Author(s):  
Li Lun Huang ◽  
Wen Guo Li ◽  
Qi Le Yang ◽  
Ying Chun Chen
Keyword(s):  
Edge Detection ◽  
Camera Calibration ◽  
Calibration Method ◽  
Feature Points ◽  
Circular Array ◽  
Robot System ◽  
Canny Edge Detection ◽  
Basic Principles ◽  
Center Point ◽  
Canny Edge

The basic principles of camera calibration are first analyzed, and the method of camera calibrate based on 2D plane circular array is presented. The first process is the use of the canny edge detection operator, and get the edge coordinates of ellipse. Then the ellipse is fitted to obtain the center point of the ellipse, and the centre point coordinates of ellipse is used to regard the feature points to implement camera caliblation. Finally, Zhang Zhengyou's method is used to obtain internal and external parameters of camera. This calibration method can be used to calbration of robot system.

scholarly journals Symmetry Analysis of Oriental Polygonal Pagodas Using 3D Point Clouds for Cultural Heritage

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1228
Author(s):  
Ting On Chan ◽  
Linyuan Xia ◽  
Yimin Chen ◽  
Wei Lang ◽  
Tingting Chen ◽  
...  
Keyword(s):  
Cultural Heritage ◽  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Geometric Model ◽  
Digital Camera ◽  
Point Clouds ◽  
Symmetry Analysis ◽  
3D Point Clouds ◽  
Transmission Towers ◽  
Aerial Vehicle

Ancient pagodas are usually parts of hot tourist spots in many oriental countries due to their unique historical backgrounds. They are usually polygonal structures comprised by multiple floors, which are separated by eaves. In this paper, we propose a new method to investigate both the rotational and reflectional symmetry of such polygonal pagodas through developing novel geometric models to fit to the 3D point clouds obtained from photogrammetric reconstruction. The geometric model consists of multiple polygonal pyramid/prism models but has a common central axis. The method was verified by four datasets collected by an unmanned aerial vehicle (UAV) and a hand-held digital camera. The results indicate that the models fit accurately to the pagodas’ point clouds. The symmetry was realized by rotating and reflecting the pagodas’ point clouds after a complete leveling of the point cloud was achieved using the estimated central axes. The results show that there are RMSEs of 5.04 cm and 5.20 cm deviated from the perfect (theoretical) rotational and reflectional symmetries, respectively. This concludes that the examined pagodas are highly symmetric, both rotationally and reflectionally. The concept presented in the paper not only work for polygonal pagodas, but it can also be readily transformed and implemented for other applications for other pagoda-like objects such as transmission towers.

scholarly journals Pose Measurement for Unmanned Aerial Vehicle Based on Rigid Skeleton

2021 ◽  
Vol 11 (4) ◽  
pp. 1373
Author(s):  
Jingyu Zhang ◽  
Zhen Liu ◽  
Guangjun Zhang
Keyword(s):  
Measurement Errors ◽  
Structural Characteristics ◽  
Geometric Model ◽  
Formation Flight ◽  
Depth Information ◽  
Feature Points ◽  
Feature Point Extraction ◽  
Aerial Vehicle ◽  
Uav Navigation ◽  
Rigid Skeleton

Pose measurement is a necessary technology for UAV navigation. Accurate pose measurement is the most important guarantee for a UAV stable flight. UAV pose measurement methods mostly use image matching with aircraft models or 2D points corresponding with 3D points. These methods will lead to pose measurement errors due to inaccurate contour and key feature point extraction. In order to solve these problems, a pose measurement method based on the structural characteristics of aircraft rigid skeleton is proposed in this paper. The depth information is introduced to guide and label the 2D feature points to eliminate the feature mismatch and segment the region. The space points obtained from the marked feature points fit the space linear equation of the rigid skeleton, and the UAV attitude is calculated by combining with the geometric model. This method does not need cooperative identification of the aircraft model, and can stably measure the position and attitude of short-range UAV in various environments. The effectiveness and reliability of the proposed method are verified by experiments on a visual simulation platform. The method proposed can prevent aircraft collision and ensure the safety of UAV navigation in autonomous refueling or formation flight.

Industrial part localization and grasping using a robotic arm guided by 2D monocular vision

2018 ◽  
Vol 45 (6) ◽  
pp. 794-804 ◽  
Author(s):  
Zhaohui Zheng ◽  
Yong Ma ◽  
Hong Zheng ◽  
Yu Gu ◽  
Mingyu Lin
Keyword(s):  
Camera Calibration ◽  
High Precision ◽  
Calibration Method ◽  
Monocular Vision ◽  
Robotic Arm ◽  
Robot Arm ◽  
Content Type ◽  
Calibration Algorithm ◽  
Target Locating ◽  
Practical Implications

Purpose The welding areas of the workpiece must be consistent with high precision to ensure the welding success during the welding of automobile parts. The purpose of this paper is to design an automatic high-precision locating and grasping system for robotic arm guided by 2D monocular vision to meet the requirements of automatic operation and high-precision welding. Design/methodology/approach A nonlinear multi-parallel surface calibration method based on adaptive k-segment master curve algorithm is proposed, which improves the efficiency of the traditional single camera calibration algorithm and accuracy of calibration. At the same time, the multi-dimension feature of target based on k-mean clustering constraint is proposed to improve the robustness and precision of registration. Findings A method of automatic locating and grasping based on 2D monocular vision is provided for robot arm, which includes camera calibration method and target locating method. Practical implications The system has been integrated into the welding robot of an automobile company in China. Originality/value A method of automatic locating and grasping based on 2D monocular vision is proposed, which makes the robot arm have automatic grasping function, and improves the efficiency and precision of automatic grasp of robot arm.

A Robust Feature Points Matching Algorithm in 3D Optical Measuring System

2011 ◽  
Vol 383-390 ◽  
pp. 5193-5199 ◽  
Author(s):  
Jian Ying Yuan ◽  
Xian Yong Liu ◽  
Zhi Qiang Qiu
Keyword(s):  
3D Reconstruction ◽  
Digital Camera ◽  
Image Data ◽  
Epipolar Geometry ◽  
Bundle Adjustment ◽  
Measuring System ◽  
Feature Points ◽  
3 Dimensional ◽  
Feature Points Matching ◽  
Optical Measuring

In optical measuring system with a handheld digital camera, image points matching is very important for 3-dimensional(3D) reconstruction. The traditional matching algorithms are usually based on epipolar geometry or multi-base lines. Mistaken matching points can not be eliminated by epipolar geometry and many matching points will be lost by multi-base lines. In this paper, a robust algorithm is presented to eliminate mistaken matching feature points in the process of 3D reconstruction from multiple images. The algorithm include three steps: (1) pre-matching the feature points using constraints of epipolar geometry and image topological structure firstly; (2) eliminating the mistaken matching points by the principle of triangulation in multi-images; (3) refining camera external parameters by bundle adjustment. After the external parameters of every image refined, repeat step (1) to step (3) until all the feature points been matched. Comparative experiments with real image data have shown that mistaken matching feature points can be effectively eliminated, and nearly no matching points have been lost, which have a better performance than traditonal matching algorithms do.

scholarly journals Semi-Automatic Calibration Method for a Bed-Monitoring System Using Infrared Image Depth Sensors

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4581 ◽  
Author(s):  
Komagata ◽  
Kakinuma ◽  
Ishikawa ◽  
Shinoda ◽  
Kobayashi
Keyword(s):  
Monitoring System ◽  
Infrared Image ◽  
Calibration Method ◽  
Spatial Position ◽  
Depth Sensor ◽  
Depth Sensors ◽  
Care Facilities ◽  
Set Up ◽  
Automated Methods ◽  
Image Depth

With the aging of society, the number of fall accidents has increased in hospitals and care facilities, and some accidents have happened around beds. To help prevent accidents, mats and clip sensors have been used in these facilities but they can be invasive, and their purpose may be misinterpreted. In recent years, research has been conducted using an infrared-image depth sensor as a bed-monitoring system for detecting a patient getting up, exiting the bed, and/or falling; however, some manual calibration was required initially to set up the sensor in each instance. We propose a bed-monitoring system that retains the infrared-image depth sensors but uses semi-automatic rather than manual calibration in each situation where it is applied. Our automated methods robustly calculate the bed region, surrounding floor, sensor location, and attitude, and can recognize the spatial position of the patient even when the sensor is attached but unconstrained. Also, we propose a means to reconfigure the spatial position considering occlusion by parts of the bed and also accounting for the gravity center of the patient’s body. Experimental results of multi-view calibration and motion simulation showed that our methods were effective for recognition of the spatial position of the patient.

scholarly journals Charuco Board-Based Omnidirectional Camera Calibration Method

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 421 ◽  
Author(s):  
Gwon An ◽  
Siyeong Lee ◽  
Min-Woo Seo ◽  
Kugjin Yun ◽  
Won-Sik Cheong ◽  
...  
Keyword(s):  
Camera Calibration ◽  
Mean Absolute Error ◽  
Three Dimensional ◽  
Absolute Error ◽  
Calibration Method ◽  
Feature Points ◽  
Omnidirectional Camera ◽  
Reprojection Error ◽  
Conventional Methods ◽  
Better Than

In this paper, we propose a Charuco board-based omnidirectional camera calibration method to solve the problem of conventional methods requiring overly complicated calibration procedures. Specifically, the proposed method can easily and precisely provide two-dimensional and three-dimensional coordinates of patterned feature points by arranging the omnidirectional camera in the Charuco board-based cube structure. Then, using the coordinate information of the feature points, an intrinsic calibration of each camera constituting the omnidirectional camera can be performed by estimating the perspective projection matrix. Furthermore, without an additional calibration structure, an extrinsic calibration of each camera can be performed, even though only part of the calibration structure is included in the captured image. Compared to conventional methods, the proposed method exhibits increased reliability, because it does not require additional adjustments to the mirror angle or the positions of several pattern boards. Moreover, the proposed method calibrates independently, regardless of the number of cameras comprising the omnidirectional camera or the camera rig structure. In the experimental results, for the intrinsic parameters, the proposed method yielded an average reprojection error of 0.37 pixels, which was better than that of conventional methods. For the extrinsic parameters, the proposed method had a mean absolute error of 0.90° for rotation displacement and a mean absolute error of 1.32 mm for translation displacement.

Monocular Vision System that Learns with Approximation Spaces

2011 ◽  
pp. 186-203 ◽  
Author(s):  
James F Peters
Keyword(s):  
Reinforcement Learning ◽  
Control Strategy ◽  
Power Transmission ◽  
Behavior Pattern ◽  
Vision System ◽  
Digital Camera ◽  
Movement Control ◽  
Monocular Vision ◽  
Approximation Spaces ◽  
Q Learning

This paper introduces a monocular vision system that learns with approximation spaces to control the pan and tilt operations of a digital camera that is tracking a moving target. This monocular vision system has been designed to facilitate inspection by a line-crawling robot that moves along an electric power transmission line. The principal problem considered in this chapter is how to use various forms of reinforcement learning to control movements of a digital camera. Prior work on the solution to this problem was done by Chris Gaskett using neural Q-learning starting in 1998 and more recently by Gaskett in 2002. However, recent experiments have revealed that both classical targets tracking as well as other forms of reinforcement learning control outperform Q-learning. This chapter considers various forms of the Actor Critic (AC) method to solve the camera movement control problem. Both the conventional AC method as well as a modified AC method that has a built-in run-and-twiddle (RT) control strategy mechanism is considered in this article. The RT mechanism introduced by Oliver Selfridge in 1981 is an action control strategy, where an organism continues what it has been doing while things are improving (increasing action reward) and twiddles (changes its action strategy) when past actions yield diminishing rewards. In this work, RT is governed by measurements (by a critic) of the degree of overlap between past behaviour patterns and a behavior pattern template representing a standard are carried out within the framework provided by approximation spaces introduced by Zdzislaw Pawlak during the early 1980s. This paper considers how to guide reinforcement learning based on knowledge of acceptable behavior patterns. The contribution of this article is an introduction to actor critic learning methods that benefit from approximation spaces in controlling camera movements during target tracking.

scholarly journals Calibration Method for Monitoring Hygro-Mechanical Reactions of Pine and Oak Wood by Acoustic Emission Nondestructive Testing

Materials ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 3775
Author(s):  
Chiara Bertolin ◽  
Lavinia de Ferri ◽  
Filippo Berto
Keyword(s):  
Acoustic Emission ◽  
Brittle Fracture ◽  
Digital Camera ◽  
Calibration Method ◽  
Tensile Tests ◽  
Damage Effect ◽  
Fracture Mechanisms ◽  
Dynamic Surface ◽  
Hardening Mechanism

The main issue of wood is its sensitivity to Relative Humidity (RH) variations, affecting its dimensional stability, and thus leading to crack formations and propagations. In situ structural health monitoring campaigns imply the use of portable noninvasive techniques such as acoustic emission, used for real-time detection of energy released when cracks form and grow. This paper proposes a calibration method, i.e., acoustic emission, as an early warning tool for estimating the length of new formed cracks. The predictability of ductile and brittle fracture mechanisms based on acoustic emission features was investigated, as well as climate-induced damage effect, leading to a strain-hardening mechanism. Tensile tests were performed on specimens submitted to a 50% RH variation and coated with chemicals to limit moisture penetration through the radial surfaces. Samples were monitored for acoustic emission using a digital camera to individuate calibration curves that correlated the total emitted energy with the crack propagation, specifically during brittle fracture mechanism, since equations provide the energy to create a new surface as the crack propagates. The dynamic surface energy value was also evaluated and used to define a Locus of Equilibrium of the energy surface rate for crack initiation and arrest, as well as to experimentally demonstrate the proven fluctuation concept.

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