P. Gutman and M. Velger, "Tracking targets using adaptive kalman filtering," EEE Transactions on Aerospace and Electronic Systems, vol. 26, pp. 691-699, 1990.
D. Comaniciu, V. Ramesh, and P. Meer, "Real-time tracking of non-rigid objects usingmean shift," presented at the Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR Hilton Head Island, 2000.
D. Comaniciu and P. Meer, "Mean shift: A robust approach toward feature space analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, pp. 603 – 619, 2002.
D. Comaniciu, V. Ramesh, and P. Meer, "Kernel-based object tracking," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, pp. 564-577, 2003.
K. Nummiaro, K.-M. Esther, and V. G. Luc, "An adaptive color-based particle filter," Image and Vision Computing, vol. 21, pp. 99-110, 2003.
A. Dawoud, M. Alam, A. Bal, and C. Loo, "Target tracking in infrared imagery using weighted composite reference function-based decision fusion," IEEE Transactions on Image Processing, vol. 15, pp. 404-410, 2006.
O. Tuzel, F. Porikli, and P. Meer, "Region covariance: A fast descriptor for detection and classification," in Proc. ECCV Conf., 2006, pp. 589–600.
O. Arif and P. A. Vela, "Kernel covariance image region description for object tracking," in 16th IEEE International Conference on Image Processing (ICIP), 2009.
Y. Wu, B. Ma, and P. A. Y. Jia, "Differential tracking with a kernel-based region covariance descriptor," Pattern Analysis and Applications, vol. 18, pp. 45–59, 2015.
W. Forstner and B. Moonen, "A metric for covariance matrices," Technical Report, Dept. of Geodesy and Geoinformatics, Stuttgart University, 1999.
A. Karteek, "The thermal infrared visual object tracking VOT-TIR2015 challenge results," presented at the IEEE International Conference on Computer Vision Workshop (ICCVW), Santiago, 2015.
A. Berg, J. Ahlberg, and M. Felsberg, "A thermal object tracking benchmark," presented at the 12th IEEE International Conference on Advanced Video- and Signal-Based Surveillance, Karlsruhe, Germany, 2015.
Burak ALPTEKIN , Hamidullah BINOL , Huseyin CUKUR (2016). Moving Target Tracking in Infrared Image Sequences Based on Differential Kernel Covariance Descriptor. International Journal of Natural Sciences Research, 4(5): 92-99. DOI: 10.18488/journal.63/2016.4.5/220.127.116.11
Forward looking infrared (FLIR) imaging has been used in many areas of
research and everyday life, but it has been mostly employed in military
and security domains. In these fields, remote infrared target tracking
is a crucial element for surveillance. However, long-range captured IR
image sequences generally have poor contrast, variable illumination, and
high background clutter. These challenges make target tracking
difficult. This paper suggests a technique for target tracking in
different ranges in challenging FLIR image sequences, based on
Differential Kernel Covariance Descriptor (DKCD). This new method
diminishes rotation and illumination variation effects. The proposed
technique calculates the differential kernel matrix of reference target
by using various statistical and spatial features such as first and
second derivatives, location information, and the intensity value of
pixels. Later, the differential covariance matrix is constructed by
using different pixel features and applying the appropriate kernel
function to the matrix. Thanks to the kernel functions, the algorithm
redefines the target's differential spatial features in Hilbert space.
This process makes the descriptor non-linear. The predicted position of
the target is calculated with the nearest neighbor algorithm in the
candidate regions in the sub-frame. The performance of the suggested
single target tracking system is then tested on challenging real-life
This paper proposes a new nonlinear descriptor which mainly uses kernel
covariance matrix based on difference of features. The technique
minimizes the effect of pose variation, illumination, size, and
The Effect of Impulse Denoising on Geometric Based Hyperspectral Unmixing
J. W. Boardman, "Automating spectral unmixing of aviris data using convex geometry concepts," in Proc. Summ. 4th Annu. JPL Airborne Geosci. Workshop, R. O. Green, Ed, 1994, pp. 11-14.
A. Plaza and C. I. Chang, "An improved n-findr algoritm in implementation," in Algorithm and Technology for Multispectral, Hyperspectral and Ultraspectral Imagery XI, n.d.
J. M. P. Nascimento and J. M. B. Dias, "Vertex component analysis: A fast algorithm to unmix hyperspectral data," IEEE Transactions on Geoscience and Remote Sensing, vol. 43, pp. 898-910, 2005.
J. Li and J. Bioucas-Dias, "Minimum volume simplex analysis: A fast algorithm to unmix hyperspectral data," presented at the IEEE International Geoscience and Remote Sensing Symposium, 2008.
J. Bioucas-Dias, "A variable splitting augmented lagrangian approach to linear spectral unmixing," presented at the IEEE GRSS Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Grenoble, France, 2009.
Q. Yuan, L. Zhang, and H. Shen, "Hyperspectral image denoising employing a spectral-spatial adaptive total variation model," IEEE Transactions on Geoscience and Remote Sensing, vol. 50, pp. 3660-3677, 2012.
Q. Yuan, L. Zhang, and H. Shen, "Hyperspectral image denoising with a spatial-spectral view fusion strategy," IEEE Transactions on Geoscience and Remote Sensing, vol. 52, pp. 2314-2325, 2014.
H. K. Aggarwal and A. Majumdar, "Exploiting spatio-spectral correlation for impulse denoising in hyperspectral images," SPIE Journal of Electronic Imaging, vol. 24, pp. 013027-013027, 2015.
D. Heinz and C. Chang, "Fully constrained least squares linear mixture analysis for material quantification in hyperspectral imagery," IEEE Transactions on Geoscience and Remote Sensing, vol. 39, pp. 529-545, 2001a.
L. I. Rudin, S. Osher, and E. Fatemi, "Nonlinear total variation based noise removal algorithms," Physica D: Nonlinear Phenomena, vol. 60, pp. 259-268, 1992.
M. A. Saunders, "Solution of sparse rectangular systems using LSQR and CRAIG," BIT Numerical Mathematics, vol. 35, pp. 588-604, 1995.
S. Jia and Y. Qian, "Spectral and spatial complexity-based hyperspectral unmixing," IEEE Transactions on Geoscience and Remote Sensing, vol. 45, pp. 3867-3879, 2007.
A. Plaza, P. Martinez, R. Perez, and J. Plaza, "A quantitive and comparative analysis of endmember extraction algorithms from hyperspectral data," IEEE Transactions on Geoscience and Remote Sensing, vol. 42, pp. 650-663, 2004.
D. Heinz and C. I. Chang, "Fully constrained least squares linear mixture analysis for material quantification in hyperspectral imagery," IEEE Transactions on Geoscience and Remote Sensing, vol. 39, pp. 529-545, 2001b.
Bilal KOCAKUSAKLAR , Nihan KAHRAMAN (2016). The Effect of Impulse Denoising on Geometric Based Hyperspectral Unmixing. International Journal of Natural Sciences Research, 4(5): 83-91. DOI: 10.18488/journal.63/2016.4.5/18.104.22.168
Hyperspectral unmixing is a process to find number of spectral component
(called endmember), estimation of endmember signatures and their
abundance fractions in each pixel on the scene. Geometric based
algorithms are developed for hyperspectral unmixing problem in the
literature. The distribution of spectra (points in n-dimensional
scatterplot) can be used to estimate endmember signatures geometrically.
Impulse denoising before unmixing process can help getting better
results for endmember extraction. For this reason, General Prior
Algorithm (GAP) is used before unmixing process. Experiments using real
data demonstrate that this preprocessing step provided better results
for endmember estimation.
This study contributes better estimation of endmember signatures on
geometric based unmixing algorithms by applying spatio-spectral
correlation for impulse denoising.