The objective of this paper is to develop a novel approach for encryption and compression of biometric information
utilizing orthogonal coding and steganography techniques. Multiple biometric signatures are encrypted individually
using orthogonal codes and then multiplexed together to form a single image, which is then embedded in a cover image
using the proposed steganography technique. The proposed technique employs three least significant bits for this purpose
and a secret key is developed to choose one from among these bits to be replaced by the corresponding bit of the
biometric image. The proposed technique offers secure transmission of multiple biometric signatures in an identification
document which will be protected from unauthorized steganalysis attempt.
Target detection in hyperspectral imagery is a challenging task as the targets occupy only a few pixels
or less. The presence of noise can make detection more complicated as spectral signature of pixels can
change due to noise. In this paper, a novel technique for detection is proposed using one dimensional
maximum average correlation height (MACH) filter. The MACH filter is trained using likely variations of
target spectral signatures. The variations can be taken from data or can be generated by applying Gaussian
noise. Each pixels of the input scene is then correlated with the detection filter. The MACH filter
maximizes the relative height of correlation peak for target in comparison with background and noise.
Thus, a target can be detected by analyzing the correlation peak values. Single or Multiple targets in a
hyperspectral sequence can be detected simultaneously this approach. Test results using real life
hyperspectral data are presented to verify the accomplishments of one dimensional MACH filter.
Pattern recognition in hyperspectral imagery is a challenging task as the objects occupy only a few pixels or
less. The presence of noise can make detection more complicated as spectral signature of pixels can change
due to noise. In this paper a technique is proposed for detection in hyperspectral imagery using one
dimensional maximum average correlation height (MACH) filter. MACH filter is a type of matched spatial
training filter which is widely used for spatial aperture radar (SAR), laser radar (LADAR), forward looking
infrared (FLIR) and other class of two-dimensional imageries to train and detect objects. For hyperspectral
case a modified one-dimensional MACH filter is proposed which uses likely variations of a given ideal
spectral signature for training. Each pixel vector of the data cube is then compared with the detection filter
using Mahalanobis distance. Based on Mahalanobis distance between the trained filter and the pixels of the
imagery, two classes are formed called the background class which does not contain a desired object and the
object class which does contain the desired object. By applying threshold boundary, a decision is then made
whether a given pixel belongs to the background class or object class. The simulation results using real life
hyperspectral imagery show that the proposed technique can detect and classify the desired objects with a
higher rate of efficiency even for very small and scattered objects.
Various correlation based techniques for detection and classification of targets in forward looking infrared (FLIR) imagery have been developed in last two decades. Correlation filters are attractive for automatic target recognition (ATR) because of their distortion tolerance and shift invariance capabilities. The extended maximum average correlation height (EMACH) filter was developed to detect a target with low false alarm rate while providing good distortion tolerance using a trade off parameter (beta). By decomposing the EMACH filter using the eigen-analysis, another generalized filter, called the eigen-EMACH (EEMACH) filter was developed. The EEMACH filter exhibits consistent performance over a wide range which controls the trade-off between distortion tolerance and clutter
rejection. In this paper, a new technique is proposed to combine the EEMACH and polynomial distance classifier correlation filter (PDCCF) for detecting and tracking both single and multiple targets in real life FLIR sequences. At first, EEMACH filter was used to select regions of interest (ROI) from input images and then PDCCF is applied to identify targets using thresholds and distance measures. Both the EEMACH and PDCCF filters are trained with different sizes and orientations corresponding to the target to be detected. This method provides improved clutter rejection capability by exploiting the eigen vectors of the desired class. Both single and multiple targets were identified in each frame by independently using EEMACH-PDCCF algorithm to avoid target disappearance problems under complicated scenarios.
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