scholarly journals Non-Target Structural Displacement Measurement Using Reference Frame-Based Deepflow

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2992 ◽  
Author(s):  
Jongbin Won ◽  
Jong-Woong Park ◽  
Kyoohong Park ◽  
Hyungchul Yoon ◽  
Do-Soo Moon
Keyword(s):  
Reference Frame ◽  
Tracking Error ◽  
Displacement Measurement ◽  
Displacement Sensor ◽  
Feature Points ◽  
Points Of Interest ◽  
Cantilevered Beam ◽  
Error Accumulation ◽  
Reference Laser ◽  
Point Detection

Displacement is crucial for structural health monitoring, although it is very challenging to measure under field conditions. Most existing displacement measurement methods are costly, labor-intensive, and insufficiently accurate for measuring small dynamic displacements. Computer vision (CV)-based methods incorporate optical devices with advanced image processing algorithms to accurately, cost-effectively, and remotely measure structural displacement with easy installation. However, non-target-based CV methods are still limited by insufficient feature points, incorrect feature point detection, occlusion, and drift induced by tracking error accumulation. This paper presents a reference frame-based Deepflow algorithm integrated with masking and signal filtering for non-target-based displacement measurements. The proposed method allows the user to select points of interest for images with a low gradient for displacement tracking and directly calculate displacement without drift accumulated by measurement error. The proposed method is experimentally validated on a cantilevered beam under ambient and occluded test conditions. The accuracy of the proposed method is compared with that of a reference laser displacement sensor for validation. The significant advantage of the proposed method is its flexibility in extracting structural displacement in any region on structures that do not have distinct natural features.

Robust Automatic Feature Tracking on Beating Human Hearts for Minimally Invasive CABG Surgery

2016 ◽  
Vol 10 (4) ◽  
Author(s):  
H. Mohamadipanah ◽  
M. Andalibi ◽  
L. Hoberock
Keyword(s):  
Feature Tracking ◽  
Human Heart ◽  
Tracking Error ◽  
State Space Model ◽  
Arbitrary Point ◽  
Feature Points ◽  
Short Term ◽  
Tracking Errors ◽  
Motion Reconstruction ◽  
Detection Stage

This paper presents a robust algorithm for automatic tracking of feature points on the human heart. The emphases and key contributions of the proposed algorithm are uniform distribution of the feature points and sustained tolerable tracking error. While in many methods in the literature, detection takes place independently from the tracking procedure, adopting a different approach, we selected a data-driven detection stage, which works based on the feedback from tracking results from the Lucas–Kanade (LK) tracking algorithm to avoid unacceptable tracking errors. To ensure a uniform spatial distribution of the total detected feature points for tracking, a cost function is employed using the simulated annealing optimizer, which prevents the newly detected points from accumulating near the previously located points or stagnant regions. Implementing the proposed algorithm on a real human heart dataset showed that the presented algorithm yields more robust tracking and improved motion reconstruction, compared with the other available methods. Furthermore, to predict the motion of feature points for handling short-term occlusions, a state space model is utilized, and thin-plate spline (TPS) interpolation was also employed to estimate motion of any arbitrary point on the heart surface.

scholarly journals Estimation of surgical tool-tip tracking error distribution in coordinate reference frame involving pivot calibration uncertainty

2017 ◽  
Vol 4 (5) ◽  
pp. 193-198 ◽  
Author(s):  
Zhe Min ◽  
Hongliang Ren ◽  
Max Q.-H. Meng
Keyword(s):  
Reference Frame ◽  
Tracking Error ◽  
Error Distribution ◽  
Surgical Tool ◽  
Calibration Uncertainty

Automatic Tracking Tunnel Cables Using Egomotion Estimation

2013 ◽  
Vol 710 ◽  
pp. 546-549
Author(s):  
Chang An Liu ◽  
Zhe Sun ◽  
Hua Wu ◽  
Guo Tian Yang
Keyword(s):  
Detection Algorithm ◽  
Power Line ◽  
Experimental Results ◽  
Feature Points ◽  
Automatic Tracking ◽  
Egomotion Estimation ◽  
The Matrix ◽  
Point Detection

We proposed an online method of tracking the tunnel cable based on egomotion estimation. The method is firstly applied key point detection algorithm to extract feature points, and then the points are matched to estimate the matrix of egomotion representing the camera movement. Finally, we use the matrix to locate a mask around the cable in each frames captured inside the power line tunnel. The experimental results show robustness and efficiency of our method.

Visual Tracking Using Combining Motion Constraint Model and Online Multiple Instance Boost Random Ferns

2012 ◽  
Vol 263-266 ◽  
pp. 2385-2392
Author(s):  
He Rong Zheng ◽  
Ye Jue Huang
Keyword(s):  
Object Tracking ◽  
Tracking Error ◽  
Object Motion ◽  
Motion Constraints ◽  
Video Object Tracking ◽  
Iir Filter ◽  
Error Accumulation ◽  
Constraint Model ◽  
Constraints Model ◽  
Motion Constraint

Video object tracking is essential algorithm for computer vision applications. An object tracking algorithm using combining motion constraints model and online multiple instance boost random ferns is proposed, which use IIR filter to obtain online learning for random ferns, and the random ferns are selected by online multiple instance boosting to construct classifier of online multiple instance boost random ferns. To reduce effects of tracking error accumulation, object motion constraint model is constructed to constrain the results classified by online multiple instance boost random ferns to locate object correctly, and construct positive and negative set to online update the classifier. The experiment shows that the proposed method achieves competitive detection results, which are comparable with state-of-the-art methods.

scholarly journals A Poisson Equation-Based Method for 3D Reconstruction of Animated Images

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ziang Lei
Keyword(s):  
3D Reconstruction ◽  
Real Time ◽  
Large Scale ◽  
Least Square ◽  
Reconstruction Method ◽  
Feature Points ◽  
Computing Power ◽  
To Come ◽  
Point Detection ◽  
Considerable Period

3D reconstruction techniques for animated images and animation techniques for faces are important research in computer graphics-related fields. Traditional 3D reconstruction techniques for animated images mainly rely on expensive 3D scanning equipment and a lot of time-consuming postprocessing manually and require the scanned animated subject to remain in a fixed pose for a considerable period. In recent years, the development of large-scale computing power of computer-related hardware, especially distributed computing, has made it possible to come up with a real-time and efficient solution. In this paper, we propose a 3D reconstruction method for multivisual animated images based on Poisson’s equation theory. The calibration theory is used to calibrate the multivisual animated images, obtain the internal and external parameters of the camera calibration module, extract the feature points from the animated images of each viewpoint by using the corner point detection operator, then match and correct the extracted feature points by using the least square median method, and complete the 3D reconstruction of the multivisual animated images. The experimental results show that the proposed method can obtain the 3D reconstruction results of multivisual animation images quickly and accurately and has certain real-time and reliability.

Equi-affine differential invariants for invariant feature point detection

2019 ◽  
Vol 31 (2) ◽  
pp. 277-296
Author(s):  
STANLEY L. TUZNIK ◽  
PETER J. OLVER ◽  
ALLEN TANNENBAUM
Keyword(s):  
Feature Point ◽  
Image Feature ◽  
Differential Invariants ◽  
Feature Points ◽  
Affine Invariant ◽  
Moving Frames ◽  
Feature Detectors ◽  
Point Detection ◽  
Point Detector ◽  
Method Of Moving Frames

Image feature points are detected as pixels which locally maximise a detector function, two commonly used examples of which are the (Euclidean) image gradient and the Harris–Stephens corner detector. A major limitation of these feature detectors is that they are only Euclidean-invariant. In this work, we demonstrate the application of a 2D equi-affine-invariant image feature point detector based on differential invariants as derived through the equivariant method of moving frames. The fundamental equi-affine differential invariants for 3D image volumes are also computed.

Scale-invariant optical flow in tracking using a pan-tilt-zoom camera

Robotica ◽  
2014 ◽  
Vol 34 (9) ◽  
pp. 1923-1947 ◽  
Author(s):  
Salam Dhou ◽  
Yuichi Motai
Keyword(s):  
Optical Flow ◽  
Focal Length ◽  
Tracking Error ◽  
Feature Points ◽  
Scale Invariant ◽  
Tracking Method ◽  
Camera Control ◽  
Speed Up ◽  
Feature Based ◽  
Scale Invariant Feature

SUMMARYAn efficient method for tracking a target using a single Pan-Tilt-Zoom (PTZ) camera is proposed. The proposed Scale-Invariant Optical Flow (SIOF) method estimates the motion of the target and rotates the camera accordingly to keep the target at the center of the image. Also, SIOF estimates the scale of the target and changes the focal length relatively to adjust the Field of View (FoV) and keep the target appear in the same size in all captured frames. SIOF is a feature-based tracking method. Feature points used are extracted and tracked using Optical Flow (OF) and Scale-Invariant Feature Transform (SIFT). They are combined in groups and used to achieve robust tracking. The feature points in these groups are used within a twist model to recover the 3D free motion of the target. The merits of this proposed method are (i) building an efficient scale-invariant tracking method that tracks the target and keep it in the FoV of the camera with the same size, and (ii) using tracking with prediction and correction to speed up the PTZ control and achieve smooth camera control. Experimental results were performed on online video streams and validated the efficiency of the proposed method SIOF, comparing with OF, SIFT, and other tracking methods. The proposed SIOF has around 36% less average tracking error and around 70% less tracking overshoot than OF.

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