scholarly journals Feature Point Extraction and Motion Tracking of Cardiac Color Ultrasound under Improved Lucas–Kanade Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiaoli Zhang ◽  
Punan Li ◽  
Yibing Li
Keyword(s):  
Optical Flow ◽  
Speckle Tracking ◽  
Motion Tracking ◽  
Color Doppler ◽  
Color Doppler Ultrasound ◽  
Feature Points ◽  
Sift Algorithm ◽  
Calculation Time ◽  
Tracking Method ◽  
Optical Flow Method

The purpose of this research is to study the application effect of Lucas–Kanade algorithm in right ventricular color Doppler ultrasound feature point extraction and motion tracking under the condition of scale invariant feature transform (SIFT). This study took the right ventricle as an example to analyze the extraction effect and calculation rate of SIFT algorithm and improved Lucas–Kanade algorithm. It was found that the calculation time before and after noise removal by the SIFT algorithm was 0.49 s and 0.46 s, respectively, and the number of extracted feature points was 703 and 698, respectively. The number of feature points extracted by the SIFT algorithm and the calculation time were significantly better than those of other algorithms ( P < 0.01 ). The mean logarithm of the matching points of the SIFT algorithm for order matching and reverse order matching was 20.54 and 20.46, respectively. The calculation time and the number of feature points for the SIFT speckle tracking method were 1198.85 s and 81, respectively, and those of the optical flow method were 3274.19 s and 80, respectively. The calculation time of the SIFT speckle tracking method was significantly lower than that of the optical flow method ( P < 0.05 ), and there was no statistical difference in the number of feature points between the SIFT speckle tracking method and the optical flow method ( P > 0.05 ). In conclusion, the improved Lucas–Kanade algorithm based on SIFT significantly improves the accuracy of feature extraction and motion tracking of color Doppler ultrasound, which shows the value of the algorithm in the clinical application of color Doppler ultrasound.

A Dual Tissue-Doppler Optical-Flow Method for Speckle Tracking Echocardiography at High Frame Rate

2018 ◽  
Vol 37 (9) ◽  
pp. 2022-2032 ◽  
Author(s):  
Jonathan Poree ◽  
Mathilde Baudet ◽  
Francois Tournoux ◽  
Guy Cloutier ◽  
Damien Garcia
Keyword(s):  
Optical Flow ◽  
Speckle Tracking ◽  
Speckle Tracking Echocardiography ◽  
Tissue Doppler ◽  
Frame Rate ◽  
High Frame Rate ◽  
Flow Method ◽  
Optical Flow Method

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.

SU-FF-J-24: An Optical Flow Based Motion Tracking Method Using Fluoroscopic Video

2006 ◽  
Vol 33 (6Part5) ◽  
pp. 2025-2025 ◽  
Author(s):  
Q Xu ◽  
R Hamilton ◽  
S Jiang
Keyword(s):  
Optical Flow ◽  
Motion Tracking ◽  
Tracking Method ◽  
J 24

scholarly journals THE APPLICATION OF PYRAMID LUCAS-KANADE OPTICAL FLOW METHOD FOR TRACKING RAIN MOTION USING HIGH-RESOLUTION RADAR IMAGES

2020 ◽  
Vol 83 (1) ◽  
pp. 105-115
Author(s):  
Roby Hambali ◽  
Djoko Legono ◽  
Rachmad Jayadi
Keyword(s):  
Optical Flow ◽  
Short Duration ◽  
Motion Tracking ◽  
Displacement Vector ◽  
Disaster Mitigation ◽  
Time Interval ◽  
Radar Images ◽  
Optical Flow Method ◽  
X Band ◽  
High Resolution Radar

Short-duration rainfall characteristics in the form of certain intensity, time, and spatial distribution become valuable contribution for lahar flow disaster mitigation in a mountainous region. Due to mitigation purpose, such information can be provided through the rainfall nowcasting process. One of the promising rainfall nowcasting applications is the extrapolation-based method. Rain motion tracking is a crucial part of the rainfall nowcasting based on this method. This paper discusses the application of Pyramid Lucas-Kanade Optical Flow (PLKOF) method on the rain motion tracking analysis using 150x150m resolution radar image. The study of rain motion tracking is carried out using 112 successive rainfall images with 10-minutes time interval originating from Mt. Merapi X-band multiparameter radar. The rainfall movement patterns in short duration are presented in the displacement vector (u,v) images and scatter diagrams of rain motions at x- and y-directions. From the simulations, it was found that the average displacement of rain motions in the Mt. Merapi region is 9 pixels (8.3 km/h) with the dominant direction is northeast. The results show that PLKOF is relatively good at detecting small displacements, yet unable to identify the occurrence of rain growth and decay properly. The ability of PLKOF method in predicting the position of rain cell displacement is satisfied as indicated by the POD, CSI, and FAR indexes.

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