2,710 research outputs found
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
Imaging spectrometers measure electromagnetic energy scattered in their
instantaneous field view in hundreds or thousands of spectral channels with
higher spectral resolution than multispectral cameras. Imaging spectrometers
are therefore often referred to as hyperspectral cameras (HSCs). Higher
spectral resolution enables material identification via spectroscopic analysis,
which facilitates countless applications that require identifying materials in
scenarios unsuitable for classical spectroscopic analysis. Due to low spatial
resolution of HSCs, microscopic material mixing, and multiple scattering,
spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus,
accurate estimation requires unmixing. Pixels are assumed to be mixtures of a
few materials, called endmembers. Unmixing involves estimating all or some of:
the number of endmembers, their spectral signatures, and their abundances at
each pixel. Unmixing is a challenging, ill-posed inverse problem because of
model inaccuracies, observation noise, environmental conditions, endmember
variability, and data set size. Researchers have devised and investigated many
models searching for robust, stable, tractable, and accurate unmixing
algorithms. This paper presents an overview of unmixing methods from the time
of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models
are first discussed. Signal-subspace, geometrical, statistical, sparsity-based,
and spatial-contextual unmixing algorithms are described. Mathematical problems
and potential solutions are described. Algorithm characteristics are
illustrated experimentally.Comment: This work has been accepted for publication in IEEE Journal of
Selected Topics in Applied Earth Observations and Remote Sensin
Fuzzy Logic and Singular Value Decomposition based Through Wall Image Enhancement
Singular value decomposition based through wall image enhancement is proposed which is capable of discriminating target, noise and clutter signals. The overlapping boundaries of clutter, noise and target signals are separated using fuzzy logic. Fuzzy inference engine is used to assign weights to different spectral components. K-means clustering is used for suitable selection of fuzzy parameters. Proposed scheme significantly works well for extracting multiple targets in heavy cluttered through wall images. Simulation results are compared on the basis of mean square error, peak signal to noise ratio and visual inspection
A Novel Image Similarity Measure Based on Greatest and Smallest Eigen Fuzzy Sets
A novel image similarity index based on the greatest and smallest fuzzy set solutions of the maxâmin and minâmax compositions of fuzzy relations, respectively, is proposed. The greatest and smallest fuzzy sets are found symmetrically as the minâmax and maxâmin solutions, respectively, to a fuzzy relation equation. The original image is partitioned into squared blocks and the pixels in each block are normalized to [0, 1] in order to have a fuzzy relation. The greatest and smallest fuzzy sets, found for each block, are used to measure the similarity between the original image and the image reconstructed by joining the squared blocks. Comparison tests with other well-known image metrics are then carried out where source images are noised by applying Gaussian filters. The results show that the proposed image similarity measure is more effective and robust to noise than the PSNR and SSIM-based measures
THRIVE: Threshold Homomorphic encryption based secure and privacy preserving bIometric VErification system
In this paper, we propose a new biometric verification and template
protection system which we call the THRIVE system. The system includes novel
enrollment and authentication protocols based on threshold homomorphic
cryptosystem where the private key is shared between a user and the verifier.
In the THRIVE system, only encrypted binary biometric templates are stored in
the database and verification is performed via homomorphically randomized
templates, thus, original templates are never revealed during the
authentication stage. The THRIVE system is designed for the malicious model
where the cheating party may arbitrarily deviate from the protocol
specification. Since threshold homomorphic encryption scheme is used, a
malicious database owner cannot perform decryption on encrypted templates of
the users in the database. Therefore, security of the THRIVE system is enhanced
using a two-factor authentication scheme involving the user's private key and
the biometric data. We prove security and privacy preservation capability of
the proposed system in the simulation-based model with no assumption. The
proposed system is suitable for applications where the user does not want to
reveal her biometrics to the verifier in plain form but she needs to proof her
physical presence by using biometrics. The system can be used with any
biometric modality and biometric feature extraction scheme whose output
templates can be binarized. The overall connection time for the proposed THRIVE
system is estimated to be 336 ms on average for 256-bit biohash vectors on a
desktop PC running with quad-core 3.2 GHz CPUs at 10 Mbit/s up/down link
connection speed. Consequently, the proposed system can be efficiently used in
real life applications
Face recognition on bag locking mechanism
With the emergent of biometric technology, people are no longer afraid to keep their important things in the safe box or room or even facility. This is because; human beings have unique features that distinguish them with other people. The scheme is based on an information theory approach that decomposes face images into a small set of characteristic feature images called âEigenfacesâ, which are actually the principal components of the initial training set of face images. In this report, thorough explanation on design process of face recognition on bags locking mechanism will be elucidated. The results and analysis of the proposed design prototype also presented and explained. The platform for executing the algorithm is on the Raspberry Pi. There are two artificial intelligent techniques applied to manipulate and processing data which is fuzzy logic and neural networks. Both systems are interdependent with each other, so that it can calculate and analyse data precisely. The receive image from the camera is analysed through the Eigenfaces algorithm. The algorithm is using Principal Component Analysis (PCA) method which comprise of artificial neural network paradigm and also statistical paradigm
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