Tomographic Imaging of Combustion Zones: Impact of Measurement Noise and Reconstruction Error

2012 ◽  
Author(s):  
Avishek Guha ◽  
Ingmar Schoegl
Keyword(s):  
Reconstruction Error ◽  
Tomographic Imaging ◽  
Measurement Noise

scholarly journals ARTI (Adaptive Radio Tomographic Imaging): One New Adaptive Elliptical Weighting Model Combining with Tracking Estimates

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1034 ◽  
Author(s):  
Chunhua Zhu ◽  
Jiaojiao Wang ◽  
Yue Chen
Keyword(s):  
Imaging System ◽  
Tomographic Imaging ◽  
Measurement Noise ◽  
Moving Target ◽  
Indoor Environments ◽  
Smoothing Filter ◽  
Model Combining ◽  
Proposed Model ◽  
Radio Tomographic Imaging ◽  
Weight Model

Imaging and tracking performance suffers from the mismatch between the model and the measurements in an adaptive radio tomographic imaging system. In this paper, a model-based approach is reviewed and a new adaptive elliptical weighting model is proposed, in which the coverage of ellipse and the voxels weightings can adaptively match the actual environments, and the Savitzky–Golay smoothing filter is presented to eliminate the influence of measurement noise and multipath interference. In our proposed model, the optimal coverage of ellipse and weightings can be obtained from voxel weightings distribution inside the ellipse and pseudo-position area and trailing phenomenon. Finally, the development efforts are evaluated and validated with real experiments conducted in indoor environments for a moving target. The results have shown that the proposed algorithm can improve the accuracy of image and location estimates compared with the normalized weight model and the const-eccentricity weight model.

2.5D Algorithm for Tomographic Imaging of the Deep Electromagnetic Geophysical Measurement

PIERS Online ◽  
2006 ◽  
Vol 2 (2) ◽  
pp. 214-218
Author(s):  
Aria Abubakar ◽  
Tarek M Habashy ◽  
V. Druskin ◽  
D. Alumbaugh ◽  
P. Zhang ◽  
...  
Keyword(s):  
Tomographic Imaging ◽  
Geophysical Measurement

Concurrent EEG And NIRS Tomographic Imaging Based on Wearable Electro-Optodes

2014 ◽  
Author(s):  
Jeng-Ren Duann ◽  
Tzyy-Ping Jung ◽  
Jin-Chern Chiou
Keyword(s):  
Tomographic Imaging

Kinematic Analysis of the Reconstruction Error of A Calibration Volume for 3d Analysis in Swimming~!2009-07-05~!2009-11-09~!2010-04-20~!

2010 ◽  
Vol 3 (1) ◽  
pp. 28-30 ◽  
Author(s):  
S. Brandao ◽  
P. Figueiredo ◽  
P. Goncalves ◽  
J. P. Vilas-Boas ◽  
R. J. Fernandes
Keyword(s):  
Kinematic Analysis ◽  
Reconstruction Error ◽  
3D Analysis

Tomographic imaging of ionisation in gases

1996 ◽  
Vol 32 (12) ◽  
pp. 1085 ◽  
Author(s):  
C.J. Kotre
Keyword(s):  
Tomographic Imaging

scholarly journals Cluster-Wise Weighted NMF for Hyperspectral Images Unmixing with Imbalanced Data

2021 ◽  
Vol 13 (2) ◽  
pp. 268
Author(s):  
Xiaochen Lv ◽  
Wenhong Wang ◽  
Hongfu Liu
Keyword(s):  
Spatial Information ◽  
Hyperspectral Image ◽  
Imbalanced Data ◽  
Reconstruction Error ◽  
Hyperspectral Data ◽  
Weight Matrix ◽  
Hyperspectral Images ◽  
Mixed Data ◽  
Sparsity Constraints ◽  
Additional Constraints

Hyperspectral unmixing is an important technique for analyzing remote sensing images which aims to obtain a collection of endmembers and their corresponding abundances. In recent years, non-negative matrix factorization (NMF) has received extensive attention due to its good adaptability for mixed data with different degrees. The majority of existing NMF-based unmixing methods are developed by incorporating additional constraints into the standard NMF based on the spectral and spatial information of hyperspectral images. However, they neglect to exploit the nature of imbalanced pixels included in the data, which may cause the pixels mixed with imbalanced endmembers to be ignored, and thus the imbalanced endmembers generally cannot be accurately estimated due to the statistical property of NMF. To exploit the information of imbalanced samples in hyperspectral data during the unmixing procedure, in this paper, a cluster-wise weighted NMF (CW-NMF) method for the unmixing of hyperspectral images with imbalanced data is proposed. Specifically, based on the result of clustering conducted on the hyperspectral image, we construct a weight matrix and introduce it into the model of standard NMF. The proposed weight matrix can provide an appropriate weight value to the reconstruction error between each original pixel and the reconstructed pixel in the unmixing procedure. In this way, the adverse effect of imbalanced samples on the statistical accuracy of NMF is expected to be reduced by assigning larger weight values to the pixels concerning imbalanced endmembers and giving smaller weight values to the pixels mixed by majority endmembers. Besides, we extend the proposed CW-NMF by introducing the sparsity constraints of abundance and graph-based regularization, respectively. The experimental results on both synthetic and real hyperspectral data have been reported, and the effectiveness of our proposed methods has been demonstrated by comparing them with several state-of-the-art methods.

Sign in / Sign up

Export Citation Format

Share Document