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Valvekaameratel põhineva inimseire täiustamine pildi resolutsiooni parandamise ning näotuvastuse abil
Due to importance of security in the society, monitoring activities and recognizing specific
people through surveillance video camera is playing an important role. One of
the main issues in such activity rises from the fact that cameras do not meet the resolution
requirement for many face recognition algorithms. In order to solve this issue,
in this work we are proposing a new system which super resolve the image. First,
we are using sparse representation with the specific dictionary involving many natural
and facial images to super resolve images. As a second method, we are using deep
learning convulutional network. Image super resolution is followed by Hidden Markov
Model and Singular Value Decomposition based face recognition. The proposed system
has been tested on many well-known face databases such as FERET, HeadPose, and
Essex University databases as well as our recently introduced iCV Face Recognition
database (iCV-F). The experimental results shows that the recognition rate is increasing
considerably after applying the super resolution by using facial and natural image
dictionary. In addition, we are also proposing a system for analysing people movement
on surveillance video. People including faces are detected by using Histogram of Oriented
Gradient features and Viola-jones algorithm. Multi-target tracking system with
discrete-continuouos energy minimization tracking system is then used to track people.
The tracking data is then in turn used to get information about visited and passed
locations and face recognition results for tracked people
Sparse and Redundant Representations for Inverse Problems and Recognition
Sparse and redundant representation of data enables the
description of signals as linear combinations of a few atoms from
a dictionary. In this dissertation, we study applications of
sparse and redundant representations in inverse problems and
object recognition. Furthermore, we propose two novel imaging
modalities based on the recently introduced theory of Compressed
Sensing (CS).
This dissertation consists of four major parts. In the first part
of the dissertation, we study a new type of deconvolution
algorithm that is based on estimating the image from a shearlet
decomposition. Shearlets provide a multi-directional and
multi-scale decomposition that has been mathematically shown to
represent distributed discontinuities such as edges better than
traditional wavelets. We develop a deconvolution algorithm that
allows for the approximation inversion operator to be controlled
on a multi-scale and multi-directional basis. Furthermore, we
develop a method for the automatic determination of the threshold
values for the noise shrinkage for each scale and direction
without explicit knowledge of the noise variance using a
generalized cross validation method.
In the second part of the dissertation, we study a reconstruction
method that recovers highly undersampled images assumed to have a
sparse representation in a gradient domain by using partial
measurement samples that are collected in the Fourier domain. Our
method makes use of a robust generalized Poisson solver that
greatly aids in achieving a significantly improved performance
over similar proposed methods. We will demonstrate by experiments
that this new technique is more flexible to work with either
random or restricted sampling scenarios better than its
competitors.
In the third part of the dissertation, we introduce a novel
Synthetic Aperture Radar (SAR) imaging modality which can provide
a high resolution map of the spatial distribution of targets and
terrain using a significantly reduced number of needed transmitted
and/or received electromagnetic waveforms. We demonstrate that
this new imaging scheme, requires no new hardware components and
allows the aperture to be compressed. Also, it
presents many new applications and advantages which include strong
resistance to countermesasures and interception, imaging much
wider swaths and reduced on-board storage requirements.
The last part of the dissertation deals with object recognition
based on learning dictionaries for simultaneous sparse signal
approximations and feature extraction. A dictionary is learned
for each object class based on given training examples which
minimize the representation error with a sparseness constraint. A
novel test image is then projected onto the span of the atoms in
each learned dictionary. The residual vectors along with the
coefficients are then used for recognition. Applications to
illumination robust face recognition and automatic target
recognition are presented
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