International Journal of Natural Sciences Research

Published by: Conscientia Beam
Online ISSN: 2311-4746
Print ISSN: 2311-7435
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No. 5

Moving Target Tracking in Infrared Image Sequences Based on Differential Kernel Covariance Descriptor

Pages: 92-99
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Moving Target Tracking in Infrared Image Sequences Based on Differential Kernel Covariance Descriptor

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DOI: 10.18488/journal.63/2016.4.5/63.5.92.99

Burak ALPTEKIN , Hamidullah BINOL , Huseyin CUKUR

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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.63/2016.4.5/63.5.92.99
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 video sequences.
Contribution/ Originality
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 background changes.

The Effect of Impulse Denoising on Geometric Based Hyperspectral Unmixing

Pages: 83-91
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The Effect of Impulse Denoising on Geometric Based Hyperspectral Unmixing

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DOI: 10.18488/journal.63/2016.4.5/63.5.83.91

Bilal KOCAKUSAKLAR , Nihan KAHRAMAN

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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.63/2016.4.5/63.5.83.91
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.
Contribution/ Originality
This study contributes better estimation of endmember signatures on geometric based unmixing algorithms by applying spatio-spectral correlation for impulse denoising.