scholarly journals Registration Error Estimation with Corresponding Point Search and Kalman Filter

2017 ◽  
Vol 53 (3) ◽  
pp. 207-216
Author(s):  
Takashi MATSUZAKI ◽  
Yasushi OBATA ◽  
Yoshie OGURA
Keyword(s):  
Kalman Filter ◽  
Error Estimation ◽  
Registration Error ◽  
Point Search

scholarly journals A Registration Error Estimation Framework for Correlative Imaging

2021 ◽  
Author(s):  
Guillaume Potier ◽  
Frederic Lavancier ◽  
Stephan Kunne ◽  
Perrine Paul-Gilloteaux
Keyword(s):  
Error Estimation ◽  
Registration Error ◽  
Correlative Imaging

An Adaptive Phase Tracking Scheme of OFDM Wireless Communication System

2013 ◽  
Vol 336-338 ◽  
pp. 1798-1803
Author(s):  
Qian Du ◽  
Wen Wu Xie
Keyword(s):  
Kalman Filter ◽  
Wireless Communication ◽  
Channel Estimation ◽  
Error Estimation ◽  
Communication System ◽  
Phase Error ◽  
Basic Structure ◽  
Adaptive Kalman Filter ◽  
Phase Tracking ◽  
New Phase

This paper proposes a new phase tracking algorithm for the 802.11a system. Since this system illuminates the basic structure of 802.11a system, and introduces the OFDM frame generation principle based the transmitter, phase error estimation and channel estimation. On the basis of this, this paper presents a phase tracking scheme based on adaptive Kalman filter, and then simulates the process based on 802.11a system. The result indicates that the BER has been improved because of this adaptive phase tracking scheme.

Model error estimation by ideal state processing in linear Kalman filter

1992 ◽  
Vol 15 (3) ◽  
pp. 775-777 ◽  
Author(s):  
S. K. Pillai ◽  
S. S. Balakrishnan ◽  
V. Seshagiri Rao ◽  
N. Narasaiah
Keyword(s):  
Kalman Filter ◽  
Error Estimation ◽  
Model Error ◽  
Ideal State ◽  
State Processing

scholarly journals The Use of Robust Local Hausdorff Distances in Accuracy Assessment for Image Alignment of Brain MRI

2008 ◽  
Author(s):  
Eric Billet ◽  
Andriy Fedorov ◽  
Nikos Chrisochoides
Keyword(s):  
Error Estimation ◽  
Accuracy Assessment ◽  
Brain Mri ◽  
Source Code ◽  
Visual Assessment ◽  
Image Alignment ◽  
Error Assessment ◽  
Registration Error ◽  
Robust Version ◽  
Local Distance

We present and implement an error estimation protocol in the Insight Toolkit (ITK) for assessing the accuracy of image alignment. We base this error estimation on a robust version of the HausdorffDistance (HD) metric applied to the recovered edges of the images. The robust modifications we introduce to the HD metric significantly reduce the amount of outliers in the local distance error estimation. We evaluate the accuracy of our protocol on synthetically deformed images. We provide the source code and datasets to reproduce this evaluation. The proposed method is shown to improve error assessment when it is compared with conventional HD methods. This approach has many applications including local estimation and visual assessment of registration error and registration parameter selection.

Sign in / Sign up

Export Citation Format

Share Document