Translational Motion Estimation of Moving Object Based on Windowed Phase Correlation Algorithm with Kernel Regression

2010 ◽  
Author(s):  
Yinghuai Yu ◽  
Hongda Zhao ◽  
Hong Liu ◽  
Benyong Liu
Keyword(s):  
Motion Estimation ◽  
Translational Motion ◽  
Kernel Regression ◽  
Moving Object ◽  
Phase Correlation ◽  
Correlation Algorithm ◽  
Object Based

A windowed phase correlation algorithm for subpixel motion estimation

2010 ◽  
Author(s):  
Yueting Chen ◽  
Jiagu Wu ◽  
Qi Li ◽  
Zhihai Xu ◽  
Huajun Feng
Keyword(s):  
Motion Estimation ◽  
Phase Correlation ◽  
Correlation Algorithm

scholarly journals Collision Avoidance with a Moving Object Based on Q-Learning.

2003 ◽  
Vol 69 (680) ◽  
pp. 1051-1057 ◽  
Author(s):  
Masashi FURUKAWA ◽  
Michiko WATANABE ◽  
Masaharu IKEDA ◽  
Masahiro KINOSHITA ◽  
Yukinori KAKAZU
Keyword(s):  
Collision Avoidance ◽  
Moving Object ◽  
Q Learning ◽  
Object Based

scholarly journals Compressive Imaging of Moving Object Based on Linear Array Sensor

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Changjun Zha ◽  
Yao Li ◽  
Jinyao Gui ◽  
Huimin Duan ◽  
Tailong Xu
Keyword(s):  
Moving Objects ◽  
Linear Array ◽  
Matching Pursuit ◽  
Moving Object ◽  
Imaging Method ◽  
Compressive Imaging ◽  
Array Sensor ◽  
Object Based ◽  
Direct Separation ◽  
Matching Pursuit Algorithm

Using the characteristics of a moving object, this paper presents a compressive imaging method for moving objects based on a linear array sensor. The method uses a higher sampling frequency and a traditional algorithm to recover the image through a column-by-column process. During the compressive sampling stage, the output values of the linear array sensor are multiplied by a coefficient that is a measurement matrix element, and then the measurement value can be acquired by adding all the multiplication values together. During the reconstruction stage, the orthogonal matching pursuit algorithm is used to recover the original image when all the measurement values are obtained. Numerical simulations and experimental results show that the proposed compressive imaging method not only effectively captures the information required from the moving object for image reconstruction but also achieves direct separation of the moving object from a static scene.

scholarly journals Compressive Ghost Imaging of the Moving Object Using the Low-Order Moments

2020 ◽  
Vol 10 (21) ◽  
pp. 7941
Author(s):  
Dongyue Yang ◽  
Chen Chang ◽  
Guohua Wu ◽  
Bin Luo ◽  
Longfei Yin
Keyword(s):  
Image Quality ◽  
Motion Estimation ◽  
Sampling Procedure ◽  
Motion Blur ◽  
Moving Object ◽  
Motion Information ◽  
Ghost Imaging ◽  
Consecutive Sampling ◽  
Novel Method ◽  
Low Order

Ghost imaging reconstructs the image based on the second-order correlation of the repeatedly measured light fields. When the observed object is moving, the consecutive sampling procedure leads to a motion blur in the reconstructed images. To overcome this defect, we propose a novel method of ghost imaging to obtain the motion information of moving object with a small number of measurements, in which the object could be regarded as relatively static. Our method exploits the idea of compressive sensing for a superior image reconstruction, combining with the low-order moments of the images to directly extract the motion information, which has the advantage of saving time and computation. With the gradual motion estimation and compensation during the imaging process, the experimental results show the proposed method could effectively overcome the motion blur, also possessing the advantage of reducing the necessary measurement number for each motion estimation and improving the reconstructed image quality.

Sign in / Sign up

Export Citation Format

Share Document